A measurement of soft-drop jet observables in pp collisions with the ATLAS detector at s√=13 TeV

Measurement of soft-drop jet observables in pp collisions with the ATLAS detector at ffiffis p=13 TeV G. Aad et al.* (ATLAS Collaboration) (Received 20 December 2019; accepted 10 February 2020; published 17 March 2020) Jet substructure quantities are measured using jets groomed with the soft-drop grooming procedure in dijet events from 32.9fb−1of pp collisions collected with the ATLAS detector at ffiffiffi s p¼13 TeV. These observables are sensitive to a wide range of QCD phenomena. Some observables, such as the jet mass and opening angle between the two subjets which pass the soft-drop condition, can be described by a high-order (resummed) series in the strong coupling constant αS. Other observables, such as the momentum sharing between the two subjets, are nearly independent of αS. These observables can be constructed using all interacting particles or using only charged particles reconstructed in the inner tracking detectors. Trackbased versions of these observables are not collinear safe, but are measured more precisely, and universal nonperturbative functions can absorb the collinear singularities. The unfolded data are directly compared with QCD calculations and hadron-level Monte Carlo simulations. The measurements are performed in different pseudorapidity regions, which are then used to extract quark and gluon jet shapes using the predicted quark and gluon fractions in each region. All of the parton shower and analytical calculations provide an excellent description of the data in most regions of phase space. DOI: 10.1103/PhysRevD.101.052007 I. INTRODUCTION Jets are collimated sprays of particles that are initiated by high-energy quarks and gluons. Grooming techniques systematically remove soft and wide-angle radiation, making the structure of the jet robust against contamination from multiple simultaneous proton-proton interactions (pileup) as well as against final-state radiation and the underlying event. This internal structure of a jet has been successfully used to tag the origin of jets in precision measurements and searches at the Large Hadron Collider (LHC) [1,2]. While grooming has been a powerful tool for applications of jet substructure techniques, it also provides a unique opportunity for the study of the strong force itself. If groomed in a suitable way, the radiation pattern inside the resulting jet can be predicted from first principles in QCD. The differential cross sections as a function of key observables such as the groomed jet mass have been computed beyond leading-logarithmic accuracy [3–8] as an expansion in the strong coupling constant αSalong with logarithms of ratios of physical scales. New “Sudakov safe” observables [9] that are the ratio of attributes that are both infrared-safe and collinear-safe cannot be expressed as an expansion in αS, but can be described with a series in fractional powers of αS. For particular grooming configurations, observables such as the ratio of subjet energies can be independent of αS[9]. These nonstandard and universal behaviors are now being tested with precision at the LHC and the Relativistic Heavy Ion Collider (RHIC). While many grooming procedures suppress difficult-to- model soft and wide-angle radiation, only one grooming algorithm has been successfully used for calculations beyond the formal precision of the parton shower (leading logarithm). This soft-drop grooming procedure [10] is a generalization of the modified mass drop procedure [11] and is formally insensitive to nonglobal logarithmic corrections [12]: resummation terms resulting from radiation which leaves the jet cone and then produces radiation that reenters the jet. Soft-drop jet observables have been calculated to next-to-leading-logarithm (NLL) and next- to-next-to-leading-logarithm (NNLL) accuracy. The softdrop jet mass has recently been measured in dijet events [13,14]. In the region where the calculations are expected to be accurate, the agreement with the data is excellent, and nonperturbative effects [15] have become the most important theoretical source of uncertainty instead of higherorder effects. This analysis goes beyond the jet mass by adding other soft-drop jet observables that are connected with the grooming procedure. Furthermore, in addition to measuring observables reconstructed using all interacting particles, *Full author list given at the end of the article. Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3. PHYSICAL REVIEW D 101, 052007 (2020) 2470-0010=2020=101(5)=052007(37) 052007-1 © 2020 CERN, for the ATLAS Collaboration charged-particle observables are measured using tracks. These track-based observables can be probed with better experimental precision compared to the calorimeter-based observables. Charged-particle observables are not formally collinear-safe, but universal nonperturbative functions, like parton distribution functions, can absorb the relevant singularities and allow for precise predictions [16–19]. Finally, the differences between these distributions in regions with different quark/gluon composition is used to understand how the behavior and sensitivity of the different observables depends on the origin of the jet. Previous measurements of groomed jet observables have been conducted at the LHC by CMS [14,20], ATLAS [13,21], and ALICE [22], and at RHIC by the STAR Collaboration [23] and additional studies at the detector level have been performed using CMS data [24–27]. II. SOFT-DROP PROCEDURE The soft-drop grooming algorithm proceeds as follows. After a jet is clustered using any algorithm, its constituents are then reclustered using the Cambridge/Aachen (C/A) algorithm [28,29], which iteratively clusters the closest constituents in rapidity and azimuth. This typically produces a jet with the same constituents as the original jet, but with a modified jet clustering history, which is sensitive to the angle-ordered nature of parton shower evolution. Then, the last step of the C/A clustering algorithm is undone, breaking the jet jinto the last two subjets, j1and j2, which were clustered together. These two subjets are then used to evaluate the soft-drop condition: minðpT;j1;p T;j2Þ pT;j1þpT;j2 >z cutΔR12 Rβ ;ð1Þ where pT;jiis the transverse momentum of subjet ji, and ΔR12 is the distance between the two subjets in y-ϕspace.1 The parameters zcut and βare algorithm parameters explained in greater detail below, and Ris the jet radius parameter. If j1and j2fail the soft-drop condition, then the subjet with the lower pTis removed, and the one with the higher pTis relabeled as jand the procedure is iterated. If the soft-drop condition is satisfied, then the algorithm is stopped, and the resulting jet jis the soft-dropped jet. If no pairs of subjets in the declustering satisfy the soft-drop condition, then the resulting jet is the zero vector. The parameters zcut and βdetermine the sensitivity of the algorithm to soft and wide-angle radiation. As β→∞(and zcut <1), the soft-drop condition is always satisfied, and no grooming is applied. Decreasing βpreferentially removes wide-angle radiation and increasing zcut preferentially removes soft radiation. The theoretical calculations are performed for a range in βand assume zcut is small enough so that it does not introduce large logarithms (which was explicitly checked in Refs. [5,6]). This measurement adopts the same choice as the available theoretical calculations: zcut ¼0.1and β≥0. Several βvalues are tested to probe different scales of angular structure inside the jets. This paper measures three closely related substructure observables, which are calculated from jets after they have been groomed with the soft-drop algorithm. These are the jet mass, the pTbalance zg[which is the left-hand side of Eq.(1)]of the splittingwhich passesthe soft-drop condition, and rg, which is the opening angle R12 of this splitting in Eq. (1). These three observables—the jet mass, zgand rg— are described in greater detail in Sec. V. B. These observables are approximately related by m2=p2 T∼zgr2 g, and each probes different aspects of the structure of the jet. III. ATLAS DETECTOR The ATLAS detector [30] at the LHC covers nearly the entire solid angle around the collision point. It consists of an inner tracking detector surrounded by a thin superconducting solenoid, electromagnetic and hadronic calorimeters, and a muon spectrometer incorporating three large superconducting toroidal magnets. The inner-detector system (ID) is immersed in a 2 Taxial magnetic field and provides charged-particle tracking in the range jηj<2.5. The high-granularity silicon pixel detector, theinnermostlayerofthetrackingdetector,coversthevertex region and typically provides four measurements per track, the first hit being typically recorded in the insertable B-layer that was installed before Run 2 [31,32]. It is followed by the silicon microstrip tracker, which usually provides eight measurements per track. These silicon detectors are complemented by the transition radiation tracker, which enables radially extended track reconstruction up to jηj¼2.0. The calorimeter system covers the pseudorapidity range jηj<4.9. Within the region jηj<3.2, electromagnetic calorimetry is provided by barrel and end cap highgranularity lead/liquid-argon (LAr) detectors, with an additional thin LAr presampler covering jηj<1.8, to correct for energy loss in material upstream of the detectors. Hadronic calorimetry is provided by the steel/scintillator-tile detector, segmented into three barrel structures within jηj<1.7, and two copper/LAr hadronic end cap calorimeters which cover 1.5<jηj<3.2. The solid angle coverage is completed with forward copper/LAr and tungsten/LAr calorimeter modules covering 3.1<jηj<4.9, which are optimized for electromagnetic and hadronic measurements respectively. 1ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the center of the detector and the zaxis along the beam pipe. The xaxis points from the IP to the center of the LHC ring, and the yaxis points upwards. Cylindrical coordinates ðr; ϕÞare used in the transverse plane, ϕ being the azimuthal angle around the zaxis. Rapidity is defined as y¼1 2ln½ðEþpzÞ=ðE−pzÞ. The pseudorapidity is defined in terms of the polar angle θas η¼−ln tanðθ=2Þ. Angular distance is measured in units of ΔR≡ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðΔηÞ2þðΔϕÞ2 p. G. AAD et al. PHYS. REV. D 101, 052007 (2020) 052007-2 Interesting events are selected for recording by the firstlevel trigger system implemented in custom hardware, followed by selections made by algorithms implemented in software in the high-level trigger [33]. The first-level trigger makes decisions at the 40 MHz bunch crossing rate to keep the accepted-event rate below 100 kHz, which the high-level trigger further reduces in order to record events to disk at about 1 kHz. IV. DATA SETS These measurements use the data set of pp collisions recorded by the ATLAS detector in 2016, corresponding to an integrated luminosity of 32.9fb−1[34,35] at a center-of- mass energy of ffiffiffi s p¼13 TeV. Events are only considered if they were collected during stable beam conditions and satisfy all data quality requirements [36]. Due to the high instantaneous luminosity and the large total inelastic proton-proton (pp) cross section, on average there are about 25 simultaneous (pileup) collisions in each bunch crossing. The measurements presented in this paper use a variety of Monte Carlo (MC) event generator samples to estimate the impact of detector efficiency and resolution as well as for comparison with the unfolded data. Dijet events were generated at leading order (LO) with PYTHIA 8.186 [37,38], with the 2→2matrix element convolved with the NNPDF2.3LO parton distribution function (PDF) set [39] and using the A14 set of multiple-parton-interaction and parton-shower parameters [40]. PYTHIA 8uses a pT- ordered parton shower model. Additional dijet events were generated using different generators, in order to study the impact of modeling uncertainties. SHERPA 2.1 [41,42] was used to generate events using multileg 2→2 and 2→3matrix elements, which were matched to parton showers following the CKKW prescription [43]. These SHERPA events were generated using the CT10nlo PDF set [44] and the default SHERPA set of tuned parameters. H ERWIG ++ 2.7 [45,46] was used to provide a sample of events with an angle-ordered parton shower model. These events were generated with the 2→2 matrix element, convolved with the CTEQ6L1 PDF set [47] and configured with the UE-EE-5 set of tuned parameters [48]. All generator events were passed through a full simulation of the ATLAS detector [49] implemented in GEANT 4 [50], which describes the interactions of particles with the detector and the subsequent digitization of analog signals. The effects of pileup were simulated with unbiased pp collisions using the PYTHIA 8.186 generator with the A2 [51] set of tuned parameters and the MSTW2008LO [52] PDF set; these events were overlaid on the nominal dijet events. These events are then reweighted such that the distribution of the average number of interactions per bunch crossing matches that seen in data. V. EVENT SELECTION AND OBJECT RECONSTRUCTION Since the data are unfolded to particle level, it is necessary to define both the particle-level and detectorlevel objects used in the measurement. The former are chosen to be as close as possible to the latter in order to minimize the model dependence caused by an extrapolation from the phase space measured at detector level to the phase space measured at particle level. Section V. A describes the particle-level and detector-level event selection criteria. Following this, Sec. V. B describes the particle-level and detector-level jet reconstruction procedure for both the calorimeter-based (all-particle) observables and the track-based (charged-particle) observables. A. Jet and event selection Detector-level events are required to have at least one primary vertex reconstructed from at least two tracks with pTgreater than 400 MeV. The primary hard-scattering vertex of the event is chosen to be the one with the highest Ptracks p2 T. The inputs to the jet clustering algorithm are locally calibrated topological calorimetercell clusters [53]. Jets are clustered with FASTJET [54] using the anti-kt[55] algorithm with radius parameter R¼0.8. A series of simulation-based calibration factors are applied to ensure that the detector-level jet pTis the same as the particle-level value on average [56]. Each event is required to have at least two reconstructed jets, where the transverse momentum of the leading jet, plead T, is greater than 300 GeV. The jet selection is applied to ungroomed jets, which ensures that the same jets are studied for all grooming configurations. In order to enhance the dijet topology and allow an interpretation of quark or gluon origin of the jets in the event, the leading two jets are required to be well balanced: pTlead=pTsublead <1.5. Both jets are required to have jηj<1.5, and only jets with a nonzero mass are retained. Events are selected using single-jet triggers. Due to the large cross section for jet production, most of the jet triggers are prescaled. Therefore events which pass these triggers are randomly discarded with some fixed probability. The lowest-pT-threshold unprescaled R¼0.4single-jet trigger in 2016 is fully efficient for R¼0.8dijet events where the leading-jet pTis greater than 600 GeV. In events where the leading jet has 300 GeV <p T<600 GeV, a prescaled trigger is used with an average prescale value of 1000 (the inverse of the probability to be recorded). While this results in a lower effective luminosity, it provides access to the lower pTregion. The inputs to particle-level jets are stable particles (cτ>10 mm) excluding muons and neutrinos. These jets are clustered using the same radius parameter as the detector-level jets and have the same ηand pTcuts as for the detector-level selection. MEASUREMENT OF SOFT-DROP JET OBSERVABLES IN PP …PHYS. REV. D 101, 052007 (2020) 052007-3 B. Inputs for jet substructure Two types of jet substructure observables are measured: calorimeter-based observables, which correspond to observables reconstructed from all particles inside the jet at particle level, and track-based observables, which correspond to observables reconstructed from charged particles. Track-based observables are theoretically more complicated to describe, but are experimentally cleaner to measure due to the precise angular measurement from the ID. For both the calorimeter-based and track-based measurements, the jet selection is performed on the calorimeter-based jets, while the soft-drop grooming is applied to the cluster inputs and the track inputs respectively (Sec. II). The jets after the application of this algorithm are often referred to as groomed, and the constituents of these jets are used to compute the jet substructure observables. It is noted that since the event selection is applied to ungroomed jets, some selected jets are left with one constituent after grooming, resulting in jets with a mass of zero. For the calorimeter-based observables, the same constituents are used to calculate the observables as are used to create the jets described in Sec. V. A for both the detector level and particle level. For detector-level track-based observables, the soft-drop procedure is applied to tracks matched to the ungroomed jet via ghost association [57], and jet substructure observables are calculated using the groomed tracks. These tracks are selected with a pT> 500 MeV requirement and assigned to the primary vertex in accord with the track-to-vertex matching. Tracks not included in vertex reconstruction are assigned to the primary vertex if it has the smallest jΔz0sinθjcompared to any other reconstructed vertex, up to a maximum distance of 3.0 mm. Tracks not matched to the primary vertex are not considered. At particle level, these trackbased observables are built using the charged-particle constituents of the particle-level jets, excluding muons. Both the leading and subleading jet are used in this measurement. In order to expose differences between quark and gluon jets, the more forward and more central of the two jets are distinguished and measured separately. Between the leading and subleading jets, the one with the smaller jηjwill be referred to as the “central”jet, and the other one as the “forward”jet. For a fixed jet pTat high rapidity where the high-xcontribution is more important, jets are more often quark-initiated due to the large contribution of valence quarks. VI. OBSERVABLES Three substructure observables are calculated from the two jets groomed with the soft-drop algorithm (using the C/ A algorithm with R¼0.8to recluster the jets), including the jet mass, zg, and rg. These three observables completely characterize the splitting from the soft-drop condition, and they are all measured using both the calorimeter and tracker inputs. Jet mass: One of the most basic and important jet substructure observables is the jet mass: m2¼X i∈jet Ei2 −X i∈jet  pi2 ;ð2Þ whereireferstotheconstituentsofthejet.Themeasurement is performed for a dimensionless version of the jet mass: the relativemassρ≡logðm2=pT2Þ,wheremis groomed and pT is ungroomed (groomed jet pTis not infrared- and collinearsafe [5]). The calorimeter-cluster inputs are treated as massless and tracks are assigned the pion mass. Since the probability distribution of ρis approximately linear in the resummation regime (ΛQCD=pT≲m=pT≲zcut, where ΛQCD is the energy scale of hadronization) [3–8], the binning for ρ is evenly spaced. For ρ, the distributions are normalized to the integrated cross section, σresum, measured in the resummation region, −3.7<ρ<−1.7. By changing β, the distribution shifts to higher values as fewer constituents are removed from the jets during grooming. An example of the distribution of ρin simulation at the detector level (particle level) for the calorimeter-based (all particles) definition is shown in Fig. 1(a) for the more central of the two jets and for β¼0. For this observable, particularly in the lower-relative-mass region, there are nontrivial detector effects which occur due to the calorimeter granularity, resulting in a distribution with different shapes at the particle and detector levels. As expected, the distribution of logðm2=pT2Þis approximately linear for β¼0in the resummation regime. One way to reduce the impact of these detector corrections is to consider track-based (charged-particle) observables. An example of the track-based (charged-particle- based) ρis shown in Fig. 1(b), where tracks (charged particles) are used for both the mass and the pT. As in the calorimeter case, the mass is calculated using the groomed jet, while the pTis calculated using the ungroomed constituents, but no calibration is applied to the ungroomed jet since no such calibration exists for track-based inputs. Although the particle-level distributions only include charged particles, the distributions are similar to those shown in Fig. 1(a), but in this case the impact of the detector corrections is significantly smaller. zg:Animportant quantitywhendescribingthehardsplitting scale that defines the mass is zg, which is minðpT;j1;p T;j2Þ= ðpT;j1þpT;j2Þfor the splitting that satisfies the soft-drop condition. If no such splitting occurs, then the jet is not included in the measurement. Symmetric splittings are characterized by zg∼0.5.Figure2shows an example of the normalized distribution in simulation of zgat the detector level (particle level) with β¼0for both the calorimeter-based (all particles) and track-based (charged particles) definitions. For β¼0and zcut ¼0.1,zgmust be greater than 0.1 in order to G. AAD et al. PHYS. REV. D 101, 052007 (2020) 052007-4 4.5−4−3.5−3−2.5 (a) (b) −2−1.5−1−0.5− ρ 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 ρ / d σ) d resum σ(1 / ATLAS Simulation -1 = 13 TeV, 32.9 fbs R = 0.8 t Calorimeter-based, anti-k = 0β = 0.1, cut Soft Drop, z Pythia8 > 300 GeV lead T p All particles Calorimeter-based 4.5−4−3.5−3−2.5−2−1.5−1−0.5− ρ 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 ρ / d σ) d resum σ(1 / ATLAS Simulation -1 = 13 TeV, 32.9 fbs R = 0.8 t Track-based, anti-k = 0β = 0.1, cut Soft Drop, z Pythia8 > 300 GeV lead T p Charged particles Track-based FIG. 1. The distribution in simulation of ρat the detector level and particle level for the more central of the two jets for β¼0for (a) calorimeter-based (all particles), and (b) track-based (charged particles). The statistical uncertainties are drawn, but are too small to be visible. 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 g z 2 4 6 8 10 12 14 g / d zσ) d σ(1 / ATLAS Simulation -1 = 13 TeV, 32.9 fbs R = 0.8 t Calorimeter-based, anti-k = 0β = 0.1, cut Soft Drop, z Pythia8 > 300 GeV lead T p All particles Calorimeter-based 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 g z 2 4 6 8 10 12 g / d zσ) d σ(1 / ATLAS Simulation -1 = 13 TeV, 32.9 fbs R = 0.8 t Track-based, anti-k = 0β = 0.1, cut Soft Drop, z Pythia8 > 300 GeV lead T p Charged particles Track-based (a) (b) FIG. 2. The distribution in simulation of zgat the detector level and particle level for β¼0for (a) calorimeter-based (all particles), and (b) track-based (charged particles). The statistical uncertainties are drawn, but are too small to be visible. 1.2−1−0.8−0.6 (a) (b) −0.4−0.2− ) g (r 10 log 0.5 1 1.5 2 2.5 3ATLAS Simulation -1 = 13 TeV, 32.9 fbs R = 0.8 t Calorimeter-based, anti-k = 0β = 0.1, cut Soft Drop, z Pythia8 > 300 GeV lead T p All particles Calorimeter-based 1.2−1−0.8−0.6−0.4−0.2− ) g (r 10 log 0.5 1 1.5 2 2.5 ) g (r 10 /d logσ) d σ(1/ ) g (r 10 /d logσ) d σ(1/ ATLAS Simulation -1 = 13 TeV, 32.9 fbs R = 0.8 t Track-based, anti-k = 0β = 0.1, cut Soft Drop, z Pythia8 > 300 GeV lead T p Charged particles Track-based FIG. 3. The distribution in simulation of rgat the detector level and particle level for the more central of the dijet system for β¼0for (a) calorimeter-based (all particles), and (b) track-based (charged particles). The statistical uncertainties are drawn, but are too small to be visible. MEASUREMENT OF SOFT-DROP JET OBSERVABLES IN PP …PHYS. REV. D 101, 052007 (2020) 052007-5 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Pr(particle-level | detector-level) 4.5−4−3.5−3−2.5−2−1.5−1−0.5− ρDetector-level (a) (b) (c) (d) (e) (f) 4.5− 4− 3.5− 3− 2.5− 2− 1.5− 1− 0.5− ρParticle-level ATLAS Simulation -1 = 13 TeV, 32.9 fbs R = 0.8 t Calorimeter-based, anti-k = 0β = 0.1, cut Soft Drop, z Pythia 8.186 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Pr(particle-level | detector-level) 4.5−4−3.5−3−2.5−2−1.5−1−0.5− ρDetector-level 4.5− 4− 3.5− 3− 2.5− 2− 1.5− 1− 0.5− ρParticle-level ATLAS Simulation -1 = 13 TeV, 32.9 fbs R = 0.8 t Track-based, anti-k = 0β = 0.1, cut Soft Drop, z Pythia 8.186 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Pr(particle-level | detector-level) 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 g Detector-level z 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 g Particle-level z ATLAS Simulation -1 = 13 TeV, 32.9 fbs R = 0.8 t Calorimeter-based, anti-k = 0β = 0.1, cut Soft Drop, z Pythia 8.186 0.1 0.2 0.3 0.4 0.5 0.6 Pr(particle-level | detector-level) 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 g Detector-level z 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 g Particle-level z ATLAS Simulation -1 = 13 TeV, 32.9 fbs R = 0.8 t Track-based, anti-k = 0β = 0.1, cut Soft Drop, z Pythia 8.186 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Pr(particle-level | detector-level) 1.2−1−0.8−0.6−0.4−0.2− ) g (r 10 Detector-level log 1.2− 1− 0.8− 0.6− 0.4− 0.2− ) g (r 10 Particle-level log ATLAS Simulation -1 = 13 TeV, 32.9 fbs R = 0.8 t Calorimeter-based, anti-k = 0β = 0.1, cut Soft Drop, z 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Pr(particle-level | detector-level) 1.2−1−0.8−0.6−0.4−0.2− ) g (r 10 Detector-level log 1.2− 1− 0.8− 0.6− 0.4− 0.2− ) g (r 10 Particle-level log ATLAS Simulation -1 = 13 TeV, 32.9 fbs R = 0.8 t Track-based, anti-k = 0β = 0.1, cut Soft Drop, z FIG. 4. The distribution of Prðparticle-leveljdetector-levelÞfor the more central jet for (top) ρ, (middle) zg, and (bottom) rgwith β¼0 for PYTHIA 8for the (left) calorimeter-based definition, and (right) track-based definition. G. AAD et al. PHYS. REV. D 101, 052007 (2020) 052007-6 pass the soft-drop condition, and therefore bins with zgvalues less than 0.1 are not shown (this is not the case for β>0). As in the case with the mass, the distributions of the chargedparticles and all-particles versions of zgare similar. Detector effects for the calorimeter-based zgare smaller than for the relative mass, because zgis less sensitive to the angular distribution of energy within the jet. The binning is evenly spaced in zgand the distributions are normalized to the integrated cross section σ. rg: The opening angle ΔR12 between the two subjets that pass the soft-drop condition is rg. This angle is smaller than the jet radius by definition. Although rgis highly correlated with the relative mass and zg, it is useful for explicitly exposing the angular distribution. Figure 3shows an example of the normalized calorimeter-based (all particles) and track-based (charged particles) rgdistributions. As expected, there are large detector effects for the calorimeterbased case, especially at low angles. Due to the correlation 4.5−4−3.5−3−2.5−2−1.5−1−0.5− ρ 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Relative Uncertainty ATLAS -1 = 13 TeV, 32.9 fbs Calorimeter-based R = 0.8 t anti-k = 0β = 0.1, cut Soft Drop, z > 300 GeV lead T p Total Uncertainty Data statistical error Unfolding Nonclosure Fragmentation Modeling Cluster energy scale Cluster energy resolution Pileup modeling Other 4.5−4−3.5−3−2.5−2−1.5−1−0.5− ρ 0.05 0.1 0.15 0.2 0.25 Relative Uncertainty ATLAS -1 = 13 TeV, 32.9 fbs Track-based R = 0.8 t anti-k = 0β = 0.1, cut Soft Drop, z > 300 GeV lead T p Total Uncertainty Data statistical error Unfolding Nonclosure Fragmentation Modeling Efficiency within jets Fake rate Cluster energy scale Other 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 g z 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 Relative Uncertainty ATLAS -1 = 13 TeV, 32.9 fbs Calorimeter-based R = 0.8 t anti-k = 0β = 0.1, cut Soft Drop, z > 300 GeV lead T p Total Uncertainty Data statistical error Unfolding Nonclosure Fragmentation Modeling Cluster energy scale Cluster energy resolution Pileup modeling Other 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 g z 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018 0.02 0.022 0.024 Relative Uncertainty ATLAS -1 = 13 TeV, 32.9 fbs Track-based R = 0.8 t anti-k = 0β = 0.1, cut Soft Drop, z > 300 GeV lead T p Total Uncertainty Data statistical error Unfolding Nonclosure Fragmentation Modeling Efficiency within jets Fake rate Cluster energy scale Other 1.2−1−0.8−0.6 −0.4 −0.2 − ) g (r 10 log 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 Relative Uncertainty ATLAS -1 = 13 TeV, 32.9 fbs Calorimeter-based R = 0.8 t anti-k = 0β = 0.1, cut Soft Drop, z > 300 GeV lead T p Total Uncertainty Data statistical error Unfolding Nonclosure Fragmentation Modeling Cluster energy scale Cluster energy resolution Pileup modeling Other 1.2 −1−0.8−0.6 −0.4−0.2 − ) g (r 10 log 0.01 0.02 0.03 0.04 0.05 Relative Uncertainty ATLAS -1 = 13 TeV, 32.9 fbs Track-based R = 0.8 t anti-k = 0β = 0.1, cut Soft Drop, z > 300 GeV lead T p Total Uncertainty Data statistical error Unfolding Nonclosure Fragmentation Modeling Efficiency within jets Fake rate Cluster energy scale Other (a) (b) (c) (d) (e) (f) FIG. 5. Total and individual uncertainties inclusive in pTfor β¼0for calorimeter-based observables (left) and track-based observables (right) for ρ(top), zg(middle), and rg(bottom). MEASUREMENT OF SOFT-DROP JET OBSERVABLES IN PP …PHYS. REV. D 101, 052007 (2020) 052007-7 between mass and rg, the distribution shapes and detector effects look similar to the ones shown in Fig. 1. The binning for rgis logarithmically spaced. The distributions are normalized to the integrated cross section σ. Similar to ρ, increasing βshifts the distribution to higher values as there is less grooming. VII. UNFOLDING The substructure observables are reconstructed in bins of the transverse momentum of the jet, and the double-differential distributions are unfolded using PYTHIA 8.186. An iterative Bayesian technique [58] is used with one (four) iterations for track-based (calorimeterbased) observables. These values were chosen to minimize the total uncertainty, and are implemented in the RooUnfold framework [59]. The probability distributions of obtaining a particle-level value given a detector-level observation, Prðparticle− leveljdetector −levelÞ,in PYTHIA 8for β¼0are presented for all three observables for the calorimeter-based and track-based definitions in Fig. 4. While the unfolding is 4−3.5−3−2.5−2−1.5−1−− ρ 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 ρ / d σ) d resum σ(1 / ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Calorimeter-based, anti-k = 0β = 0.1, cut Soft Drop, z > 300 GeV lead T p Data Pythia 8.186 Sherpa 2.1 Herwig++ 2.7 4.5−4−3.5−3−2.5 (a) (b) (c) (d) (e) (f) −2−1.5−1−0.5− ρ 0.5 1 1.5 Ratio to Data 4−3.5−3−2.5−2−1.5−1−− ρ 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 ρ / d σ) d resum σ(1 / ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Track-based, anti-k = 0β = 0.1, cut Soft Drop, z > 300 GeV lead T p Data Pythia 8.186 Sherpa 2.1 Herwig++ 2.7 4.5−4−3.5−3−2.5−2−1.5−1−0.5− ρ 0.5 1 1.5 Ratio to Data 4−3.5−3−2.5−2−1.5−1−− ρ 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 ρ / d σ) d resum σ(1 / ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Calorimeter-based, anti-k = 1β = 0.1, cut Soft Drop, z > 300 GeV lead T p Data Pythia 8.186 Sherpa 2.1 Herwig++ 2.7 4.5−4−3.5−3−2.5−2−1.5−1−0.5− ρ 0.5 1 1.5 Ratio to Data 4−3.5−3−2.5−2−1.5−1−− ρ 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 ρ / d σ) d resum σ(1 / ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Track-based, anti-k = 1β = 0.1, cut Soft Drop, z > 300 GeV lead T p Data Pythia 8.186 Sherpa 2.1 Herwig++ 2.7 4.5−4−3.5−3−2.5−2−1.5−1−0.5− ρ 0.5 1 1.5 Ratio to Data 4−3.5−3−2.5−2−1.5−1−− ρ 0.5 1 1.5 2 2.5 ρ / d σ) d resum σ(1 / ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Calorimeter-based, anti-k = 2β = 0.1, cut Soft Drop, z > 300 GeV lead T p Data Pythia 8.186 Sherpa 2.1 Herwig++ 2.7 4.5−4−3.5−3−2.5−2−1.5−1−0.5− ρ 0.5 1 1.5 Ratio to Data 4−3.5−3−2.5−2−1.5−1−− ρ 0.5 1 1.5 2 2.5 3 ρ / d σ) d resum σ(1 / ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Track-based, anti-k = 2β = 0.1, cut Soft Drop, z > 300 GeV lead T p Data Pythia 8.186 Sherpa 2.1 Herwig++ 2.7 4.5−4−3.5−3−2.5−2−1.5−1−0.5− ρ 0.5 1 1.5 Ratio to Data FIG. 6. Comparison of the unfolded ρdistribution with MC predictions. The uncertainty bands include all sources: data and MC statistical uncertainties, nonclosure, modeling, and cluster or tracking uncertainties where relevant. (a) β¼0, calorimeter-based. (b) β¼0, track-based. (c) β¼1, calorimeter-based. (d) β¼1, track-based. (e) β¼2, calorimeter-based. (f) β¼2, track-based. G. AAD et al. PHYS. REV. D 101, 052007 (2020) 052007-8 done simultaneously in pTand the jet observable, the unfolding matrices are shown inclusively in pTfor simplicity. As anticipated, the unfolding matrices for the trackbased observables have significantly smaller off-diagonal elements than their calorimeter-based analogs. VIII. UNCERTAINTIES Several sources of statistical and systematic uncertainties are considered for this analysis. The data and simulation statistical uncertainties are evaluated from pseudoexperiments using the bootstrap method [60]. The uncertainties from the calorimeter-cell reconstruction, track reconstruction, and MC modeling are determined by applying variations to the simulation, as detailed in Secs. VIII. A,VIII. B,andVIII. C, respectively. The impact of the calorimeter-cell cluster uncertainties on the jets is taken into account for both the calorimeter-based measurement as well as the trackbased measurement since it impacts the selection of jets. The varied simulation is then used to repeat 0.15 0.2 0.25 0.3 0.35 0.4 0.45 g z 2 4 6 8 10 12 g / d zσ) d σ(1 / ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Calorimeter-based, anti-k = 0β = 0.1, cut Soft Drop, z > 300 GeV lead T p Data Pythia 8.186 Sherpa 2.1 Herwig++ 2.7 0.1 0.15 0.2 0.25 0.3 (a) (b) (c) (d) (e) (f) 0.35 0.4 0.45 0.5 g z 0.8 1 1.2 Ratio to Data 0.15 0.2 0.25 0.3 0.35 0.4 0.45 g z 2 4 6 8 10 12 g / d zσ) d σ(1 / ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Track-based, anti-k = 0β = 0.1, cut Soft Drop, z > 300 GeV lead T p Data Pythia 8.186 Sherpa 2.1 Herwig++ 2.7 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 g z 0.8 1 1.2 Ratio to Data 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 g z 2 4 6 8 10 12 14 16 g / d zσ) d σ(1 / ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Calorimeter-based, anti-k = 1β = 0.1, cut Soft Drop, z > 300 GeV lead T p Data Pythia 8.186 Sherpa 2.1 Herwig++ 2.7 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 g z 0.8 1 1.2 Ratio to Data 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 g z 2 4 6 8 10 12 14 g / d zσ) d σ(1 / ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Track-based, anti-k = 1β = 0.1, cut Soft Drop, z > 300 GeV lead T p Data Pythia 8.186 Sherpa 2.1 Herwig++ 2.7 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 g z 0.8 1 1.2 Ratio to Data 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 g z 5 10 15 20 25 g / d zσ) d σ(1 / ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Calorimeter-based, anti-k = 2β = 0.1, cut Soft Drop, z > 300 GeV lead T p Data Pythia 8.186 Sherpa 2.1 Herwig++ 2.7 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 g z 0.8 1 1.2 Ratio to Data 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 g z 2 4 6 8 10 12 14 16 18 20 22 g / d zσ) d σ(1 / ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Track-based, anti-k = 2β = 0.1, cut Soft Drop, z > 300 GeV lead T p Data Pythia 8.186 Sherpa 2.1 Herwig++ 2.7 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 g z 0.8 1 1.2 Ratio to Data FIG. 7. Comparison of the unfolded zgdistribution with MC predictions. The uncertainty bands include all sources: data and MC statistical uncertainties, nonclosure, modeling, and cluster or tracking uncertainties where relevant. (a) β¼0, calorimeter-based. (b) β¼0, track-based. (c) β¼1, calorimeter-based. (d) β¼1, track-based. (e) β¼2, calorimeter-based. (f) β¼2, track-based. MEASUREMENT OF SOFT-DROP JET OBSERVABLES IN PP …PHYS. REV. D 101, 052007 (2020) 052007-9 modeled by simulation. This is followed by a comparison between the unfolded data and state-of-the-art analytical predictions in Sec. IX. B. Section IX. C directly compares the results of the measurements of the calorimeter- and track-based observables. While these observables are unfolded to different particle-level definitions, this comparison highlights the similarities between the different definitions, as well as demonstrates the improved precision in track-based measurements of observables sensitive to the angular structure of the jet. The forward and central measurements are compared in Sec. IX. D, and these measurements are used as input to the extraction of the quark- and gluon-jet distributions of these observables, which are shown in Sec. IX. E. 4−3.5−3−2.5−2−1.5−1−− ρ 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Calorimeter-based, anti-k = 0β = 0.1, cut Soft Drop, z > 300 GeV lead T p Data, Central Jet Data, Forward Jet 4.5−4−3.5−3−2.5 (a) (b) (c) (d) (e) (f) −2−1.5−1−0.5− ρ 0.6 0.8 1 1.2 1.4 Ratio to Central 4−3.5−3−2.5−2−1.5−1−− ρ 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 ρ /d σ) d resum σ(1/ ρ /d σ) d resum σ(1/ ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Track-based, anti-k = 0β = 0.1, cut Soft Drop, z > 300 GeV lead T p Data, Central Jet Data, Forward Jet 4.5−4−3.5−3−2.5−2−1.5−1−0.5− ρ 0.6 0.8 1 1.2 1.4 Ratio to Central 0.15 0.2 0.25 0.3 0.35 0.4 0.45 g z 2 4 6 8 10 12 ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Calorimeter-based, anti-k = 0β = 0.1, cut Soft Drop, z > 300 GeV lead T p Data, Central Jet Data, Forward Jet 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 g z 0.8 1 1.2 Ratio to Central 0.15 0.2 0.25 0.3 0.35 0.4 0.45 g z 2 4 6 8 10 12 g /dzσ) d σ(1/ g /dzσ) d σ(1/ ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Track-based, anti-k = 0β = 0.1, cut Soft Drop, z > 300 GeV lead T p Data, Central Jet Data, Forward Jet 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 g z 0.8 1 1.2 Ratio to Central 1−0.8−0.6−0.4−0.2−) g (r 10 log 0.5 1 1.5 2 2.5 3 ) g (r 10 /d logσ) d σ(1/ ) g (r 10 /d logσ) d σ(1/ ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Calorimeter-based, anti-k = 0β = 0.1, cut Soft Drop, z > 300 GeV lead T p Data, Central Jet Data, Forward Jet 1.2−1−0.8−0.6−0.4−0.2− ) g (r 10 log 0.8 1 1.2 Ratio to Central 1−0.8−0.6−0.4−0.2−) g (r 10 log 0.5 1 1.5 2 2.5 3ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Track-based, anti-k = 0β = 0.1, cut Soft Drop, z > 300 GeV lead T p Data, Central Jet Data, Forward Jet 1.2−1−0.8−0.6−0.4−0.2− ) g (r 10 log 0.8 1 1.2 Ratio to Central FIG. 14. Comparison of the forward and central unfolded distributions for β¼0. The uncertainty bands include all sources: data and MC statistical uncertainties, cluster uncertainties, nonclosure, and modeling. See Sec. VIII for details. (a) ρdistribution, β¼0, calorimeter-based. (b) ρdistribution, β¼0, track-based. (c) zgdistribution, β¼0, calorimeter-based. (d) zgdistribution, β¼0, trackbased. (e) rgdistribution, β¼0, calorimeter-based. (f) rgdistribution, β¼0, track-based. G. AAD et al. PHYS. REV. D 101, 052007 (2020) 052007-16 A. Comparison with MC predictions Figures 6–8compare the unfolded data from both jets with the particle-level distributions from MC generators described in Sec. IV. Several trends are visible in these results. For ρ, the MC predictions are mostly accurate within 10% except for the lowest relative masses, which are dominated by nonperturbative physical effects. This becomes more visible for larger values of β, where more soft radiation is included within the jet, increasing the size of the nonperturbative effects. In addition, in the highrelative-mass region, where the effects of the fixed-order calculation are relevant, some differences between MC generators are seen. A similar trend may be seen for rg, where the small-angle region shows more pronounced differences between MC generators, since this corresponds to the region where nonperturbative effects are largest. Overall, these effects are smaller than for the relative mass. Unlike the other two observables, zgis modeled well within about 10% across most of the spectrum. However, there is some tension between the predictions and the unfolded data, which is visible particularly for the track-based observables, which have better precision. In general, the MC predictions show similar behavior for the calorimeter-based and track-based definitions, both in their overall distributions and in their agreement with the unfolded data distribution. However, as the tracking measurement is more precise, the disagreement between data and MC simulation in the nonperturbative regions is more significant. For instance, in Figs. 7(e)–7(f), the H ERWIG ++ prediction does not agree with the unfolded distribution at high values of zgfor the track-based case, but it does agree in the calorimeter-based case. 4−3.5−3−2.5−2−1.5−1−− ρ 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 ρ / d σ) d resum σ(1 / ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Track-based, anti-k = 0β = 0.1, cut Soft Drop, z Quarks, Data Gluons, Data Quarks, Pythia 8.186 Gluons, Pythia 8.186 4.5−4−3.5−3−2.5 (a) (b) (c) −2−1.5−1−0.5− ρ 0 0.5 1 1.5 2 2.5 Quark Data Ratio to 4−3.5−3−2.5−2−1.5−1−− ρ 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 ρ / d σ) d resum σ(1 / ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Track-based, anti-k = 1β = 0.1, cut Soft Drop, z Quarks, Data Gluons, Data Quarks, Pythia 8.186 Gluons, Pythia 8.186 4.5−4−3.5−3−2.5−2−1.5−1−0.5− ρ 0 0.5 1 1.5 2 2.5 Quark Data Ratio to 4−3.5−3−2.5−2−1.5−1−− ρ 0.5 1 1.5 2 2.5 ρ / d σ) d resum σ(1 / ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Track-based, anti-k = 2β = 0.1, cut Soft Drop, z Quarks, Data Gluons, Data Quarks, Pythia 8.186 Gluons, Pythia 8.186 4.5−4−3.5−3−2.5−2−1.5−1−0.5− ρ 0 0.5 1 1.5 2 2.5 Quark Data Ratio to FIG. 15. Comparison of the quark and gluon unfolded ρdistributions for the track-based measurement. The uncertainty bands include all sources: data and MC statistical uncertainties, tracking uncertainties, nonclosure, and modeling.(a) ρdistribution, β¼0, track-based. (b) ρdistribution, β¼1, track-based. (c) ρdistribution, β¼2, track-based. TABLE I. The gluon fractions predicted by the PYTHIA 8 multijet simulation. Gluon Fraction [%] Central Region Forward Region 300 GeV <p T<400 GeV 75.1 69.5 400 GeV <p T<600 GeV 71.7 64.4 600 GeV <p T<800 GeV 66.2 56.9 800 GeV <p T<1000 GeV 61.0 50.5 1000 GeV <p T<2000 GeV 54.4 43.3 MEASUREMENT OF SOFT-DROP JET OBSERVABLES IN PP …PHYS. REV. D 101, 052007 (2020) 052007-17 B. Comparison with analytical predictions Currently, it is only possible to perform analytical predictions when including both charged and neutral particles, and therefore results in this section are only compared with the calorimeter-based results. Subleading logarithms have been computed for ρand rg, as described below. Several calculations have been performed to predict the ρdistribution, and these predictions are compared with the unfolded data. In addition, only ρand rgare studied, since no predictions exist for zgbeyond leading-logarithmic accuracy. In particular, these include the NLO þNLL prediction from Refs. [5,6], the LO þNNLL prediction from Refs. [3,4], and the NNLL prediction from Refs. [7,8]. The LO þNNLL and NNLL calculations are based on soft collinear effective theory [69,70]. The former is matched to leading order using M ad G raph5_a MC @ NLO [71] with the MSTW2008LO PDF. The latter uses the CT14nlo [72] PDF set and includes finite zcut resummation as well as nonperturbative corrections based on an analytic shape function with one free parameter that is chosen based on comparisons with PYTHIA 8. While strictly for inclusive jets, the NNLL calculation is also applicable here because at high jet pT, the difference between inclusive jets and dijets is negligible. The NLO þNLL calculation is matched to fixed order using NLOJet++ [73,74] with the CT14nlo PDF and includes finite zcut resummation as well as nonperturbative corrections from the envelope of parton shower MC predictions from HERWIG 6.521 [75] AUET2 [76], PYTHIA 6.428 [37] Perugia 2011 [77], PYTHIA 6.428 Z2 [78], PYTHIA 8.223 [37,38,79] 4C [80], and PYTHIA 8.223 Monash 13 [81]. These predictions are compared with the unfolded data in Fig. 9. Because the LO þNNLL and NLO þNLL calculations for ρare only available for pT>600 GeV, the unfolded data are shown for both a low-pTjet selection (pT>300 GeV) and a high-pTjet selection (pT> 600 GeV). The calculations are able to model the data in the resummation region (approximately −3≲ρ≲−1)at the level of a 10% difference. The NLO þNLL calculation also provides an accurate model of the data at the high values of ρ, while the LO þNNLL and NNLL calculations do not model this region as accurately. This is the region where the fixed-order effects are dominant, and so this behavior is expected. 0.15 0.2 0.25 0.3 0.35 0.4 0.45 g z 2 4 6 8 10 12 g / d zσ) d σ(1 / ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Track-based, anti-k = 0β = 0.1, cut Soft Drop, z Quarks, Data Gluons, Data Quarks, Pythia 8.186 Gluons, Pythia 8.186 0.1 0.15 0.2 0.25 0.3 (a) (b) (c) 0.35 0.4 0.45 0.5 g z 0 0.5 1 1.5 2 2.5 Quark Data Ratio to 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 g z 2 4 6 8 10 12 14 16 18 20 22 g / d zσ) d σ(1 / ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Track-based, anti-k = 1β = 0.1, cut Soft Drop, z Quarks, Data Gluons, Data Quarks, Pythia 8.186 Gluons, Pythia 8.186 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 g z 0 0.5 1 1.5 2 2.5 Quark Data Ratio to 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 g z 5 10 15 20 25 g / d zσ) d σ(1 / ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Track-based, anti-k = 2β = 0.1, cut Soft Drop, z Quarks, Data Gluons, Data Quarks, Pythia 8.186 Gluons, Pythia 8.186 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 g z 0 0.5 1 1.5 2 2.5 Quark Data Ratio to FIG. 16. Comparison of the quark and gluon unfolded zgdistribution for the track-based measurement. The uncertainty bands include all sources: data and MC statistical uncertainties, nonclosure, modeling, and tracking uncertainties where relevant. (a) zgdistribution, β¼0, track-based. (b) zgdistribution, β¼1, track-based. (c) zgdistribution, β¼2, track-based. G. AAD et al. PHYS. REV. D 101, 052007 (2020) 052007-18 At lower values of relative mass, the nonperturbative corrections are needed to describe the data. This can be seen particularly from the low-pTresults, which show the NNLL prediction with and without the inclusion of nonperturbative effects. As expected, the inclusion of these effects brings the prediction much closer to the unfolded data distribution, although the level of agreement is still not as good as in the resummation region. The region where nonperturbative corrections are relevant shifts to higher relative mass with increased values of β, since more soft radiation is included within the jet. In general, similar levels of agreement are seen in the low-pTand high-pTcases, although it is noted that the nonperturbative region shifts to slightly lower relative mass in the high-pTcase. An NLL calculation of rghas been performed recently [82], and the results of this calculation are compared with the unfolded data distribution in Fig. 10. Unlike the jet mass case, nonglobal logarithms are not absent (β¼0)or power suppressed (β>0). The calculation includes both the nonglobal and clustering logarithms to achieve full NLL accuracy. In general, in the region where nonperturbative effects are expected to be small, the prediction agrees with the data within uncertainties, while in the regions where nonperturbative effects are large, the prediction is systematically higher than the data. C. Comparison of track-based and calorimeter-based measurements On a jet-by-jet basis, the value of the all-particles and charged-particles jet substructure observables are largely uncorrelated. However, due to isospin symmetry, the probability distributions for all-particles and charged-par- ticles distributions are nearly identical. This is studied by comparing the unfolded distributions for the cluster-based and track-based measurements, which are shown in Figs. 11–13 for the region which includes both jets in the dijet system. The results generally agree in the perturbative regions at high values of ρand rg, and there is disagreement in the low-relative-mass regions. There is also some disagreement for low values of zgfor β>0. These studies also enable a comparison of the sizes of the uncertainties for calorimeter-based and track-based observables. For all of these observables, the uncertainties for the track-based observables are significantly smaller than those for the calorimeter-based observables, particularly for 1−0.8−0.6−0.4−0.2−) g (r 10 log 0.5 1 1.5 2 2.5 3ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Track-based, anti-k = 0β = 0.1, cut Soft Drop, z Quarks, Data Gluons, Data Quarks, Pythia 8.186 Gluons, Pythia 8.186 1.2−1−0.8−0.6−0.4−0.2− ) g (r 10 log 0 0.5 1 1.5 2 2.5 Quark Data Ratio to 1−0.8−0.6−0.4−0.2−) g (r 10 log 0.5 1 1.5 2 2.5 3 ) g (r 10 /d logσ) d σ(1/ ) g (r 10 /d logσ) d σ(1/ ) g (r 10 /d logσ) d σ(1/ ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Track-based, anti-k = 1β = 0.1, cut Soft Drop, z Quarks, Data Gluons, Data Quarks, Pythia 8.186 Gluons, Pythia 8.186 1.2−1−0.8−0.6−0.4−0.2− ) g (r 10 log 0 0.5 1 1.5 2 2.5 Quark Data Ratio to 1−0.8−0.6−0.4−0.2−) g (r 10 log 0.5 1 1.5 2 2.5 3 3.5 4ATLAS -1 = 13 TeV, 32.9 fbs R = 0.8 t Track-based, anti-k = 2β = 0.1, cut Soft Drop, z Quarks, Data Gluons, Data Quarks, Pythia 8.186 Gluons, Pythia 8.186 1.2−1−0.8−0.6−0.4−0.2− ) g (r 10 log 0 0.5 1 1.5 2 2.5 Quark Data Ratio to (a) (b) (c) FIG. 17. Comparison of the quark and gluon unfolded rgdistribution for the track-based measurement. The uncertainty bands include all sources: data and MC statistical uncertainties, nonclosure, modeling, and tracking uncertainties where relevant. (a) rgdistribution, β¼0, track-based. (b) rgdistribution, β¼1, track-based. (c) rgdistribution, β¼2, track-based. MEASUREMENT OF SOFT-DROP JET OBSERVABLES IN PP …PHYS. REV. D 101, 052007 (2020) 052007-19 higher values of β, where more soft radiation is included within the jet. However, since no track-based calculations exist at the present time, calorimeter-based measurements are still useful for precision QCD studies. D. Comparison of forward and central measurements The distribution of the substructure observables at a given pTis a function of the composition of the initiating parton type, and should not be affected by where the jet is produced within the detector. Therefore, any differences seen between the distribution of the observable in different regions of the detector are related to the quark-gluon composition of the events produced there. Since this measurement was done separately for the more forward and more central jet in the dijet samples, it is possible to compare these distributions to see if these effects are visible. The fraction of central and forward jets originating from gluons, fG,in PYTHIA 8multijet events is shown in Table I, where the jet flavor is determined by the highest-energy parton inside the jet cone.2This shows that the gluon fractions in the forward and central regions differ by about 5–10%. For each of the three observables, Fig. 14 compares the unfolded distributions for the jets in the forward region with those for jets in the central region. As expected, since the forward region is quark-enhanced, it has more jets at lower relative masses. These differences are numerically small because the gluon fractions are similar for the forward and central jets. E. Quark-gluon extraction of the observables Since the shape of the ρ,rg, and zgdistributions at a given jet pTonly depends on the flavor of the initiating parton and not on the rapidity, the quark and gluon distributions may be extracted from the measurements of the central and forward distributions if the quark-gluon fraction is known for each region. In particular, the central and forward distributions for these observables may be described as the sum of the quark and gluon distributions, weighted by the quark and gluon composition of the sample: hF i¼fF QhQ iþfF GhG i; hC i¼fC QhQ iþfC GhG i;ð3Þ where hiis a bin of a histogram for an observable, Fand C represent the forward and central regions, and Qand G represent quark or gluon. The quark and gluon fractions (fQand fG) for the more forward and more central jets are determined from the nominal PYTHIA 8MC event sample, where the quark fraction fQis given by 1−fG. This extraction is model dependent, but the more forward and more central distributions are made public for reinterpretation using any model. Table Ishows these values for each pTbin. Equation (2) may then be solved for hG iand hQ ito extract these distributions from the forward and central distributions. The extracted quark and gluon distributions are shown in Figs. 15–17 for track-based observables. Cluster-based observables are not shown, but exhibit similar behavior overall. For these results, the PDF uncertainties and the uncertainties in the jet inputs are taken to be fully correlated between the more forward and more central jets, while all other uncertainties are considered fully uncorrelated. In addition, to account for the uncertainty in the composition of the sample, the difference between the extracted distributions using the PYTHIA 8and SHERPA compositions is taken as an uncertainty. A few observations can be made about the differences between the quark and gluon distributions. For the jet mass, the gluon distribution tends towards higher values of the mass, which is expected due to the larger color factor associated with gluons. These differences become more apparent at larger values of β. For β¼0,zgis independent of αSto leading order, and the distributions are very similar, while for β>0, some differences begin to appear. Finally, for rg, the gluon distribution tends towards a larger splitting, which is similarly more apparent at larger values of β. X. CONCLUSION This paper presented a measurement of soft-drop jet substructure observables in dijet events in pp collisions at ffiffiffi s p¼13 TeV using a data set corresponding to an integrated luminosity of 32.9fb−1collected with the ATLAS detector at the LHC. Unfolded measurements of three substructure observables were shown for both the calorimeter-based observables unfolded to the all-particle level and track-based observables unfolded to the charged-particles level. These two types of measurements allow a direct comparison of how the different object definitions affect the measured observables. The calorimeter-based measurements for the relative jet mass and rgwere compared with analytical predictions and were shown to be in good agreement in the perturbative region. In particular, this provides the first comparison between an analytical prediction and an unfolded measurement of rg. Particularly for observables which are sensitive to the angular distribution of radiation within a jet, track-based observables were shown to be more precise than calorimeter-based observables, due to the better angular resolution of tracks. Since analytical predictions of track-based observables are not currently available, cluster-based observables are still relevant for probing the perturbative region. The forward 2Various definitions were studied in Ref. [83] and found to have a small effect on quark/gluon extractions. Furthermore, the universality of this definition was studied in Ref. [84]. G. AAD et al. PHYS. REV. D 101, 052007 (2020) 052007-20 and central jets were measured separately, which enables an extraction of the quark- and gluon-jet distributions using input from simulation. The extractions demonstrate differences between the observables in their sensitivity to the quark and gluon composition of the sample, which are most pronounced for the least amount of grooming. ACKNOWLEDGMENTS We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRT, Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russia Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada and CRC, Canada; ERC, ERDF, Horizon 2020, Marie Skłodowska-Curie Actions and COST, European Union; Investissements d’Avenir Labex, Investissements d’Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; CERCA Programme Generalitat de Catalunya and PROMETEO Programme Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CCIN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in Ref. [85]. [1] R. Kogler et al., Jet substructure at the Large Hadron Collider, Rev. Mod. Phys. 91, 045003 (2019). [2] A. J. Larkoski, I. Moult, and B. Nachman, Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning, Phys. Rep. 841, 1 (2020). [3] C. Frye, A. J. Larkoski, M. D. Schwartz, and K. Yan, Factorization for groomed jet substructure beyond the next-to-leading logarithm, J. High Energy Phys. 07 (2016) 064. [4] C. Frye, A. J. Larkoski, M. D. Schwartz, and K. Yan, Precision physics with pile-up insensitive observables, arXiv:1603.06375. [5] S. Marzani, L. Schunk, and G. Soyez, A study of jet mass distributions with grooming, J. High Energy Phys. 07 (2017) 132. [6] S. Marzani, L. Schunk, and G. Soyez, The jet mass distribution after soft drop, Eur. Phys. J. C 78, 96 (2018). [7] Z.-B. Kang, K. Lee, X. Liu, and F. Ringer, Soft drop groomed jet angularities at the LHC, Phys. Lett. B 793,41 (2019). [8] Z.-B. Kang, K. Lee, X. Liu, and F. Ringer, The groomed and ungroomed jet mass distribution for inclusive jet production at the LHC, J. High Energy Phys. 10 (2018) 137. [9] A. J. Larkoski, S. Marzani, and J. Thaler, Sudakov safety in perturbative QCD, Phys. Rev. D 91, 111501 (2015). [10] A. J. Larkoski, S. Marzani, G. Soyez, and J. Thaler, Soft drop, J. High Energy Phys. 05 (2014) 146. [11] M. Dasgupta, A. Fregoso, S. Marzani, and G. P. Salam, Towards an understanding of jet substructure, J. High Energy Phys. 09 (2013) 029. [12] M. Dasgupta and G. P. Salam, Resummation of non-global QCD observables, Phys. Lett. B 512, 323 (2001). [13] ATLAS Collaboration, Measurement of the Soft-Drop Jet Mass in pp Collisions at ffiffiffi s p¼13 TeV with the ATLAS Detector, Phys. Rev. Lett. 121, 092001 (2018). [14] CMS Collaboration, Measurements of the differential jet cross section as a function of the jet mass in dijet events from proton-proton collisions at ffiffiffi s p¼13 TeV, J. High Energy Phys. 11 (2018) 113. [15] A. H. Hoang, S. Mantry, A. Pathak, and I. W. Stewart, Nonperturbative corrections to soft drop jet mass, J. High Energy Phys. 12 (2019) 002. [16] D. Krohn, M. D. Schwartz, T. Lin, and W. J. Waalewijn, Jet charge at the LHC, Phys. Rev. Lett. 110, 212001 (2013). [17] W. J. Waalewijn, Calculating the charge of a jet, Phys. Rev. D86, 094030 (2012). [18] H.-M. Chang, M. Procura, J. Thaler, and W. J. Waalewijn, Calculating Track-Based Observables for the LHC, Phys. Rev. Lett. 111, 102002 (2013). MEASUREMENT OF SOFT-DROP JET OBSERVABLES IN PP …PHYS. REV. D 101, 052007 (2020) 052007-21 [19] B. T. Elder, M. Procura, J. Thaler, W. J. Waalewijn, and K. Zhou, Generalized fragmentation functions for fractal jet observables, J. High Energy Phys. 06 (2017) 085. [20] CMS Collaboration, Measurement of jet substructure observables in t¯ tevents from proton-proton collisions at ffiffiffi s p¼13 TeV, Phys. Rev. D 98, 092014 (2018). [21] ATLAS Collaboration, Measurement of jet-substructure observables in top quark, Wboson and light jet production in proton-proton collisions at ffiffiffi s p¼13 TeV with the ATLAS detector, J. High Energy Phys. 08 (2019) 033. [22] ALICE Collaboration, Exploration of jet substructure using iterative declustering in pp and Pb-Pb collisions at LHC energies, arXiv:1905.02512. [23] STAR Collaboration, Measurements of the jet internal substructure and its relevance to parton shower evolution in pþp and Au þAu collisions at STAR, Proc. Sci., 345 (2019) 090 [arXiv:1903.12115]. [24] CMS Collaboration, Measurement of the groomed jet mass in PbPb and pp collisions at ffiffiffiffiffiffiffiffi sNN p¼5.02 TeV, J. High Energy Phys. 10 (2018) 161. [25] CMS Collaboration, Measurement of the Splitting Function in pp and Pb-Pb Collisions at ffiffiffiffiffiffiffiffi sNN p¼5.02 TeV, Phys. Rev. Lett. 120, 142302 (2018). [26] A. Larkoski, S. Marzani, J. Thaler, A. Tripathee, and W. Xue, Exposing the QCD Splitting Function with CMS Open Data, Phys. Rev. Lett. 119, 132003 (2017). [27] A. Tripathee, W. Xue, A. Larkoski, S. Marzani, and J. Thaler, Jet substructure studies with CMS open data, Phys. Rev. D 96, 074003 (2017). [28] Y. L. Dokshitzer, G. D. Leder, S. Moretti, and B. R. Webber, Better jet clustering algorithms, J. High Energy Phys. 08 (1997) 001. [29] M. Wobisch and T. Wengler, Hadronization corrections to jet cross-sections in deep-inelastic scattering, in Proceedings of the Workshop on Monte Carlo Generators for HERA Physics (1999) p. 270, http://inspirehep.net/record/511711. [30] ATLAS Collaboration, The ATLAS experiment at the CERN Large Hadron Collider, J. Instrum. 3, S08003 (2008). [31] ATLAS Collaboration, ATLAS insertable B-layer technical design report, CERN Report No. ATLAS-TDR-19, 2010, https://cds.cern.ch/record/1291633; Addendum, CERN Report No. ATLAS-TDR-19-ADD-1, 2012, https://cds .cern.ch/record/1451888. [32] B. P. Abbott et al., Production and integration of the ATLAS insertable B-layer, J. Instrum. 13, T05008 (2018). [33] ATLAS Collaboration, Performance of the ATLAS trigger system in 2015, Eur. Phys. J. C 77, 317 (2017). [34] ATLAS Collaboration, Luminosity determination in pp collisions at ffiffiffi s p¼13 TeV using the ATLAS detector at the LHC, CERN Report No. ATLAS-CONF-2019-021, 2019, https://cds.cern.ch/record/2677054. [35] G. Avoni et al., The new LUCID-2 detector for luminosity measurement and monitoring in ATLAS, J. Instrum. 13, P07017 (2018). [36] ATLAS Collaboration, ATLAS data quality operations and performance for 2015-2018 data-taking, arXiv:1911 .04632. [37] T. Sjöstrand, S. Mrenna, and P. Z. Skands, PYTHIA 6.4 physics and manual, J. High Energy Phys. 05 (2006) 026. [38] T. Sjöstrand, S. Mrenna, and P. Z. Skands, A brief introduction to PYTHIA 8.1, Comput. Phys. Commun. 178, 852 (2008). [39] R. D. Ball et al., Parton distributions with LHC data, Nucl. Phys. B867, 244 (2013). [40] ATLAS Collaboration, ATLAS PYTHIA 8 tunes to 7 TeV data, CERN Report No. ATL-PHYS-PUB-2014-021, 2014, https://cds.cern.ch/record/1966419. [41] T. Gleisberg, S. Höche, F. Krauss, M. Schönherr, S. Schumann, F. Siegert, and J. Winter, Event generation with SHERPA 1.1, J. High Energy Phys. 02 (2009) 007. [42] E. Bothmann et al., Event generation with Sherpa 2.2, SciPost Phys. 7, 034 (2019). [43] S. Catani, F. Krauss, B. R. Webber, and R. Kuhn, QCD matrix elements+parton showers, J. High Energy Phys. 11 (2001) 063. [44] H.-L. Lai, M. Guzzi, J. Huston, Z. Li, P. M. Nadolsky, J. Pumplin, and C.-P. Yuan, New parton distributions for collider physics, Phys. Rev. D 82, 074024 (2010). [45] M. Bähr et al., Herwig++ physics and manual, Eur. Phys. J. C58, 639 (2008). [46] G. Corcella, I. G. Knowles, G. Marchesini, S. Moretti, K. Odagiri, P. Richardson, M. H. Seymour, and B. R. Webber, HERWIG 6: An event generator for hadron emission reactions with interfering gluons (including supersymmetric processes), J. High Energy Phys. 01 (2001) 010. [47] J. Pumplin, D. R. Stump, J. Huston, H.-L. Lai, P. Nadolsky, and W.-K. Tung, New generation of parton distributions with uncertainties from Global QCD analysis, J. High Energy Phys. 07 (2002) 012. [48] M. H. Seymour and A. Siodmok, Constraining MPI models using σeff and recent Tevatron and LHC underlying event data, J. High Energy Phys. 10 (2013) 113. [49] ATLAS Collaboration, The ATLAS simulation infrastructure, Eur. Phys. J. C 70, 823 (2010). [50] S. Agostinelli et al., GEANT4—A simulation toolkit, Nucl. Instrum. Methods Phys. Res., Sect. A 506, 250 (2003). [51] ATLAS Collaboration, Summary of ATLAS PYTHIA 8 tunes, CERN Report No. ATL-PHYS-PUB-2012-003, 2012, https://cds.cern.ch/record/1474107. [52] A. D. Martin, W. J. Stirling, R. S. Thorne, and G. Watt, Parton distributions for the LHC, Eur. Phys. J. C 63, 189 (2009). [53] ATLAS Collaboration, Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1, Eur. Phys. J. C 77, 490 (2017). [54] M. Cacciari, G. P. Salam, and G. Soyez, FastJet user manual, Eur. Phys. J. C 72, 1896 (2012). [55] M. Cacciari, G. P. Salam, and G. Soyez, The anti-ktjet clustering algorithm, J. High Energy Phys. 04 (2008) 063. [56] ATLAS Collaboration, Jet energy scale measurements and their systematic uncertainties in proton-proton collisions at ffiffiffi s p¼13 TeV with the ATLAS detector, Phys. Rev. D 96, 072002 (2017). [57] M. Cacciari, G. P. Salam, and G. Soyez, The catchment area of jets, J. High Energy Phys. 04 (2008) 005. [58] G. D’Agostini, A multidimensional unfolding method based on Bayes’theorem, Nucl. Instrum. Methods Phys. Res., Sect. A 362, 487 (1995). G. AAD et al. PHYS. REV. D 101, 052007 (2020) 052007-22 [59] T.Adye, Unfolding algorithms and tests using RooUnfold, arXiv:1105.1160. [60] B. Efron, Bootstrap methods: Another look at the Jackknife, Ann. Stat. 7, 1 (1979). [61] B. Malaescu, An iterative, dynamically stabilized method of data unfolding, Report No. LAL-09-107, 2009. [62] ATLAS Collaboration, A measurement of the calorimeter response to single hadrons and determination of the jet energy scale uncertainty using LHC Run-1 pp-collision data with the ATLAS detector, Eur. Phys. J. C 77, 26 (2017). [63] E. Abat et al., Study of energy response and resolution of the ATLAS barrel calorimeter to hadrons of energies from 20 to 350 GeV, Nucl. Instrum. Methods Phys. Res., Sect. A 621, 134 (2010). [64] ATLAS Collaboration, Study of the material of the ATLAS inner detector for Run 2 of the LHC, J. Instrum. 12, P12009 (2017). [65] ATLAS Collaboration, Performance of the ATLAS track reconstruction algorithms in dense environments in LHC Run 2, Eur. Phys. J. C 77, 673 (2017). [66] ATLAS Collaboration, Measurement of track reconstruction inefficiencies in the core of jets via pixel dE/dx with the ATLAS experiment using ffiffiffi s p¼13 TeV pp collision data, CERN Report No. ATL-PHYS-PUB-2016-007, 2016, https://cds.cern.ch/record/2140460. [67] ATLAS Collaboration, Modelling of track reconstruction inside jets with the 2016 ATLAS ffiffiffi s p¼13 TeV pp dataset, CERN Report No. ATL-PHYS-PUB-2017-016, 2017, https://cds.cern.ch/record/2275639. [68] ATLAS Collaboration, Early inner detector tracking performance in the 2015 data at ffiffiffi s p¼13 TeV, CERN Report No. ATL-PHYS-PUB-2015-051, 2015, https://cds.cern.ch/ record/2110140. [69] C. W. Bauer, S. Fleming, D. Pirjol, and I. W. Stewart, An effective field theory for collinear and soft gluons: Heavy to light decays, Phys. Rev. D 63, 114020 (2001). [70] C. W. Bauer, D. Pirjol, and I. W. Stewart, Soft collinear factorization in effective field theory, Phys. Rev. D 65, 054022 (2002). [71] J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O. Mattelaer, H.-S. Shao, T. Stelzer, P. Torrielli, and M. Zaro, The automated computation of tree-level and next-to-lead- ing order differential cross sections, and their matching to parton shower simulations, J. High Energy Phys. 07 (2014) 079. [72] S. Dulat, T.-J. Hou, J. Gao, M. Guzzi, J. Huston, P. Nadolsky, J. Pumplin, C. Schmidt, D. Stump, and C.-P. Yuan, New parton distribution functions from a global analysis of quantum chromodynamics, Phys. Rev. D 93, 033006 (2016). [73] S. Catani and M. H. Seymour, A general algorithm for calculating jet cross sections in NLO QCD, Nucl. Phys. B485, 291 (1997). [74] Z. Nagy, Next-to-leading order calculation of three-jet observables in hadron-hadron collision, Phys. Rev. D 68, 094002 (2003). [75] G. Corcella et al., HERWIG 6.5 Release Note, arXiv:hepph/0210213. [76] ATLAS Collaboration, New ATLAS event generator tunes to 2010 data, CERN Report No. ATL-PHYS-PUB-2011- 008, 2011, https://cds.cern.ch/record/1345343. [77] P. Z. Skands, Tuning Monte Carlo generators: The Perugia tunes, Phys. Rev. D 82, 074018 (2010). [78] R. Field, Early LHC underlying event Ddata–Findings and surprises, Hadron collider physics. Proceedings, 22nd Conference, HCP 2010, Toronto, Canada (2010), http:// inspirehep.net/record/1115933. [79] T. Sjöstrand, S. Ask, J. R. Christiansen, R. Corke, N. Desai, P. Ilten, S. Mrenna, S. Prestel, C. O. Rasmussen, and P. Z. Skands, An introduction to PYTHIA 8.2, Comput. Phys. Commun. 191, 159 (2015). [80] R. Corke and T. Sjöstrand, Interleaved parton showers and tuning prospects, J. High Energy Phys. 03 (2011) 032. [81] P. Skands, S. Carrazza, and J. Rojo, Tuning PYTHIA 8.1: The Monash 2013 Tune, Eur. Phys. J. C 74, 3024 (2014). [82] Z.-B. Kang, K. Lee, X. Liu, and D. Neill and F. Ringer, The soft drop groomed jet radius at NLL, arXiv:1908.01783. [83] ATLAS Collaboration, Measurement of the charged-particle multiplicity inside jets from ffiffiffi s p¼8TeV pp collisions with the ATLAS detector, Eur. Phys. J. C 76, 322 (2016). [84] S. Bright-Thonney and B. Nachman, Investigating the topology dependence of quark and gluon jets, J. High Energy Phys. 03 (2019) 098. [85] ATLAS Collaboration, ATLAS computing acknowledgements, CERN Report No. ATL-GEN-PUB-2016-002, https://cds.cern.ch/record/2202407. G. Aad,102 B. Abbott,129 D. C. Abbott,103 A. Abed Abud,36 K. Abeling,53 D. K. Abhayasinghe,94 S. H. Abidi,167 O. S. AbouZeid,40 N. L. Abraham,156 H. Abramowicz,161 H. Abreu,160 Y. Abulaiti,6B. S. Acharya,67a,67b,b B. Achkar,53 S. Adachi,163 L. Adam,100 C. Adam Bourdarios,5L. Adamczyk,84a L. Adamek,167 J. Adelman,121 M. Adersberger,114 A. Adiguzel,12c S. Adorni,54 T. Adye,144 A. A. Affolder,146 Y. Afik,160 C. Agapopoulou,65 M. N. Agaras,38 A. Aggarwal,119 C. Agheorghiesei,27c J. A. Aguilar-Saavedra,140f,140a,c F. Ahmadov,80 W. S. Ahmed,104 X. Ai,18 G. Aielli,74a,74b S. Akatsuka,86 T. P. A. Åkesson,97 E. Akilli,54 A. V. Akimov,111 K. Al Khoury,65 G. L. Alberghi,23b,23a J. Albert,176 M. J. Alconada Verzini,161 S. Alderweireldt,36 M. Aleksa,36 I. N. Aleksandrov,80 C. Alexa,27b T. Alexopoulos,10 A. Alfonsi,120 F. Alfonsi,23b,23a M. Alhroob,129 B. Ali,142 M. Aliev,166 G. Alimonti,69a S. P. Alkire,148 C. Allaire,65 B. M. M. Allbrooke,156 B. W. Allen,132 P. P. Allport,21 A. Aloisio,70a,70b F. Alonso,89 C. Alpigiani,148 A. A. Alshehri,57 M. Alvarez Estevez,99 M. G. Alviggi,70a,70b Y. Amaral Coutinho,81b A. Ambler,104 L. Ambroz,135 C. Amelung,26 D. Amidei,106 S. P. Amor Dos Santos,140a S. Amoroso,46 C. S. Amrouche,54 F. An,79 C. Anastopoulos,149 N. Andari,145 MEASUREMENT OF SOFT-DROP JET OBSERVABLES IN PP …PHYS. REV. D 101, 052007 (2020) 052007-23 T. Andeen,11 C. F. Anders,61b J. K. Anders,20 A. Andreazza,69a,69b V. Andrei,61a C. R. Anelli,176 S. Angelidakis,38 A. Angerami,39 A. V. Anisenkov,122b,122a A. Annovi,72a C. Antel,54 M. T. Anthony,149 E. Antipov,130 M. Antonelli,51 D. J. A. Antrim,171 F. Anulli,73a M. Aoki,82 J. A. Aparisi Pozo,174 L. Aperio Bella,15a J. P. Araque,140a V. Araujo Ferraz,81b R. Araujo Pereira,81b C. Arcangeletti,51 A. T. H. Arce,49 F. A. Arduh,89 J-F. Arguin,110 S. Argyropoulos,78 J.-H. Arling,46 A. J. Armbruster,36 A. Armstrong,171 O. Arnaez,167 H. Arnold,120 Z. P. Arrubarrena Tame,114 G. Artoni,135 S. Artz,100 S. Asai,163 T. Asawatavonvanich,165 N. Asbah,59 E. M. Asimakopoulou,172 L. Asquith,156 J. Assahsah,35d K. Assamagan,29 R. Astalos,28a R. J. Atkin,33a M. Atkinson,173 N. B. Atlay,19 H. Atmani,65 K. Augsten,142 G. Avolio,36 R. Avramidou,60a M. K. Ayoub,15a A. M. Azoulay,168b G. Azuelos,110,d H. Bachacou,145 K. Bachas,68a,68b M. Backes,135 F. Backman,45a,45b P. Bagnaia,73a,73b M. Bahmani,85 H. Bahrasemani,152 A. J. Bailey,174 V. R. Bailey,173 J. T. Baines,144 C. Bakalis,10 O. K. Baker,183 P. J. Bakker,120 D. Bakshi Gupta,8S. Balaji,157 E. M. Baldin,122b,122a P. Balek,180 F. Balli,145 W. K. Balunas,135 J. Balz,100 E. Banas,85 A. Bandyopadhyay,24 Sw. Banerjee,181,e A. A. E. Bannoura,182 L. Barak,161 W. M. Barbe,38 E. L. Barberio,105 D. Barberis,55b,55a M. Barbero,102 G. Barbour,95 T. Barillari,115 M-S. Barisits,36 J. Barkeloo,132 T. Barklow,153 R. Barnea,160 S. L. Barnes,60c B. M. Barnett,144 R. M. Barnett,18 Z. Barnovska-Blenessy,60a A. Baroncelli,60a G. Barone,29 A. J. Barr,135 L. Barranco Navarro,45a,45b F. Barreiro,99 J. Barreiro Guimarães da Costa,15a S. Barsov,138 R. Bartoldus,153 G. Bartolini,102 A. E. Barton,90 P. Bartos,28a A. Basalaev,46 A. Basan,100 A. Bassalat,65,f M. J. Basso,167 R. L. Bates,57 S. Batlamous,35e J. R. Batley,32 B. Batool,151 M. Battaglia,146 M. Bauce,73a,73b F. Bauer,145 K. T. Bauer,171 H. S. Bawa,31 J. B. Beacham,49 T. Beau,136 P. H. Beauchemin,170 F. Becherer,52 P. Bechtle,24 H. C. Beck,53 H. P. Beck,20,g K. Becker,178 C. Becot,46 A. Beddall,12d A. J. Beddall,12a V. A. Bednyakov,80 M. Bedognetti,120 C. P. Bee,155 T. A. Beermann,182 M. Begalli,81b M. Begel,29 A. Behera,155 J. K. Behr,46 F. Beisiegel,24 A. S. Bell,95 G. Bella,161 L. Bellagamba,23b A. Bellerive,34 P. Bellos,9K. Beloborodov,122b,122a K. Belotskiy,112 N. L. Belyaev,112 D. Benchekroun,35a N. Benekos,10 Y. Benhammou,161 D. P. Benjamin,6M. Benoit,54 J. R. Bensinger,26 S. Bentvelsen,120 L. Beresford,135 M. Beretta,51 D. Berge,46 E. Bergeaas Kuutmann,172 N. Berger,5B. Bergmann,142 L. J. Bergsten,26 J. Beringer,18 S. Berlendis,7G. Bernardi,136 C. Bernius,153 F. U. Bernlochner,24 T. Berry,94 P. Berta,100 C. Bertella,15a I. A. Bertram,90 O. Bessidskaia Bylund,182 N. Besson,145 A. Bethani,101 S. Bethke,115 A. Betti,42 A. J. Bevan,93 J. Beyer,115 D. S. Bhattacharya,177 P. Bhattarai,26 R. Bi,139 R. M. Bianchi,139 O. Biebel,114 D. Biedermann,19 R. Bielski,36 K. Bierwagen,100 N. V. Biesuz,72a,72b M. Biglietti,75a T. R. V. Billoud,110 M. Bindi,53 A. Bingul,12d C. Bini,73a,73b S. Biondi,23b,23a M. Birman,180 T. Bisanz,53 J. P. Biswal,161 D. Biswas,181,e A. Bitadze,101 C. Bittrich,48 K. Bjørke,134 T. Blazek,28a I. Bloch,46 C. Blocker,26 A. Blue,57 U. Blumenschein,93 G. J. Bobbink,120 V. S. Bobrovnikov,122b,122a S. S. Bocchetta,97 A. Bocci,49 D. Boerner,46 D. Bogavac,14 A. G. Bogdanchikov,122b,122a C. Bohm,45a V. Boisvert,94 P. Bokan,53,172 T. Bold,84a A. S. Boldyrev,113 A. E. Bolz,61b M. Bomben,136 M. Bona,93 J. S. Bonilla,132 M. Boonekamp,145 C. D. Booth,94 H. M. Borecka-Bielska,91 A. Borisov,123 G. Borissov,90 J. Bortfeldt,36 D. Bortoletto,135 D. Boscherini,23b M. Bosman,14 J. D. Bossio Sola,104 K. Bouaouda,35a J. Boudreau,139 E. V. Bouhova-Thacker,90 D. Boumediene,38 S. K. Boutle,57 A. Boveia,127 J. Boyd,36 D. Boye,33b,h I. R. Boyko,80 A. J. Bozson,94 J. Bracinik,21 N. Brahimi,102 G. Brandt,182 O. Brandt,32 F. Braren,46 B. Brau,103 J. E. Brau,132 W. D. Breaden Madden,57 K. Brendlinger,46 L. Brenner,46 R. Brenner,172 S. Bressler,180 B. Brickwedde,100 D. L. Briglin,21 D. Britton,57 D. Britzger,115 I. Brock,24 R. Brock,107 G. Brooijmans,39 W. K. Brooks,147c E. Brost,121 J. H Broughton,21 P. A. Bruckman de Renstrom,85 D. Bruncko,28b A. Bruni,23b G. Bruni,23b L. S. Bruni,120 S. Bruno,74a,74b M. Bruschi,23b N. Bruscino,73a,73b P. Bryant,37 L. Bryngemark,97 T. Buanes,17 Q. Buat,36 P. Buchholz,151 A. G. Buckley,57 I. A. Budagov,80 M. K. Bugge,134 F. Bührer,52 O. Bulekov,112 T. J. Burch,121 S. Burdin,91 C. D. Burgard,120 A. M. Burger,130 B. Burghgrave,8J. T. P. Burr,46 C. D. Burton,11 J. C. Burzynski,103 V. Büscher,100 E. Buschmann,53 P. J. Bussey,57 J. M. Butler,25 C. M. Buttar,57 J. M. Butterworth,95 P. Butti,36 W. Buttinger,36 C. J. Buxo Vazquez,107 A. Buzatu,158 A. R. Buzykaev,122b,122a G. Cabras,23b,23a S. Cabrera Urbán,174 D. Caforio,56 H. Cai,173 V. M. M. Cairo,153 O. Cakir,4a N. Calace,36 P. Calafiura,18 A. Calandri,102 G. Calderini,136 P. Calfayan,66 G. Callea,57 L. P. Caloba,81b A. Caltabiano,74a,74b S. Calvente Lopez,99 D. Calvet,38 S. Calvet,38 T. P. Calvet,155 M. Calvetti,72a,72b R. Camacho Toro,136 S. Camarda,36 D. Camarero Munoz,99 P. Camarri,74a,74b D. Cameron,134 R. Caminal Armadans,103 C. Camincher,36 S. Campana,36 M. Campanelli,95 A. Camplani,40 A. Campoverde,151 V. Canale,70a,70b A. Canesse,104 M. Cano Bret,60c J. Cantero,130 T. Cao,161 Y. Cao,173 M. D. M. Capeans Garrido,36 M. Capua,41b,41a R. Cardarelli,74a F. Cardillo,149 G. Carducci,41b,41a I. Carli,143 T. Carli,36 G. Carlino,70a B. T. Carlson,139 L. Carminati,69a,69b R. M. D. Carney,153 S. Caron,119 E. Carquin,147c S. Carrá,46 J. W. S. Carter,167 M. P. Casado,14,i A. F. Casha,167 D. W. Casper,171 R. Castelijn,120 F. L. Castillo,174 L. Castillo Garcia,14 G. AAD et al. PHYS. REV. D 101, 052007 (2020) 052007-24 V. Castillo Gimenez,174 N. F. Castro,140a,140e A. Catinaccio,36 J. R. Catmore,134 A. Cattai,36 V. Cavaliere,29 E. Cavallaro,14 M. Cavalli-Sforza,14 V. Cavasinni,72a,72b E. Celebi,12b L. Cerda Alberich,174 K. Cerny,131 A. S. Cerqueira,81a A. Cerri,156 L. Cerrito,74a,74b F. Cerutti,18 A. Cervelli,23b,23a S. A. Cetin,12b Z. Chadi,35a D. Chakraborty,121 J. Chan,181 W. S. Chan,120 W. Y. Chan,91 J. D. Chapman,32 B. Chargeishvili,159b D. G. Charlton,21 T. P. Charman,93 C. C. Chau,34 S. Che,127 S. Chekanov,6S. V. Chekulaev,168a G. A. Chelkov,80,j M. A. Chelstowska,36 B. Chen,79 C. Chen,60a C. H. Chen,79 H. Chen,29 J. Chen,60a J. Chen,39 J. Chen,26 S. Chen,137 S. J. Chen,15c X. Chen,15b Y-H. Chen,46 H. C. Cheng,63a H. J. Cheng,15a A. Cheplakov,80 E. Cheremushkina,123 R. Cherkaoui El Moursli,35e E. Cheu,7K. Cheung,64 T. J. A. Cheval´erias,145 L. Chevalier,145 V. Chiarella,51 G. Chiarelli,72a G. Chiodini,68a A. S. Chisholm,21 A. Chitan,27b I. Chiu,163 Y. H. Chiu,176 M. V. Chizhov,80 K. Choi,11 A. R. Chomont,73a,73b S. Chouridou,162 Y. S. Chow,120 M. C. Chu,63a X. Chu,15a,15d J. Chudoba,141 A. J. Chuinard,104 J. J. Chwastowski,85 L. Chytka,131 D. Cieri,115 K. M. Ciesla,85 D. Cinca,47 V. Cindro,92 I. A. Cioară,27b A. Ciocio,18 F. Cirotto,70a,70b Z. H. Citron,180,k M. Citterio,69a D. A. Ciubotaru,27b B. M. Ciungu,167 A. Clark,54 M. R. Clark,39 P. J. Clark,50 C. Clement,45a,45b Y. Coadou,102 M. Cobal,67a,67c A. Coccaro,55b J. Cochran,79 H. Cohen,161 A. E. C. Coimbra,36 L. Colasurdo,119 B. Cole,39 A. P. Colijn,120 J. Collot,58 P. Conde Muiño,140a,140h S. H. Connell,33b I. A. Connelly,57 S. Constantinescu,27b F. Conventi,70a,l A. M. Cooper-Sarkar,135 F. Cormier,175 K. J. R. Cormier,167 L. D. Corpe,95 M. Corradi,73a,73b E. E. Corrigan,97 F. Corriveau,104,m A. Cortes-Gonzalez,36 M. J. Costa,174 F. Costanza,5D. Costanzo,149 G. Cowan,94 J. W. Cowley,32 J. Crane,101 K. Cranmer,125 S. J. Crawley,57 R. A. Creager,137 S. Cr´ep´e-Renaudin,58 F. Crescioli,136 M. Cristinziani,24 V. Croft,170 G. Crosetti,41b,41a A. Cueto,5 T. Cuhadar Donszelmann,149 A. R. Cukierman,153 W. R. Cunningham,57 S. Czekierda,85 P. Czodrowski,36 M. J. Da Cunha Sargedas De Sousa,60b J. V. Da Fonseca Pinto,81b C. Da Via,101 W. Dabrowski,84a F. Dachs,36 T. Dado,28a S. Dahbi,33d T. Dai,106 C. Dallapiccola,103 M. Dam,40 G. D’amen,29 V. D’Amico,75a,75b J. Damp,100 J. R. Dandoy,137 M. F. Daneri,30 N. P. Dang,181,e N. S. Dann,101 M. Danninger,175 V. Dao,36 G. Darbo,55b O. Dartsi,5A. Dattagupta,132 T. Daubney,46 S. D’Auria,69a,69b C. David,168b T. Davidek,143 D. R. Davis,49 I. Dawson,149 K. De,8R. De Asmundis,70a M. De Beurs,120 S. De Castro,23b,23a S. De Cecco,73a,73b N. De Groot,119 P. de Jong,120 H. De la Torre,107 A. De Maria,15c D. De Pedis,73a A. De Salvo,73a U. De Sanctis,74a,74b M. De Santis,74a,74b A. De Santo,156 K. De Vasconcelos Corga,102 J. B. De Vivie De Regie,65 C. Debenedetti,146 D. V. Dedovich,80 A. M. Deiana,42 J. Del Peso,99 Y. Delabat Diaz,46 D. Delgove,65 F. Deliot,145,n C. M. Delitzsch,7M. Della Pietra,70a,70b D. Della Volpe,54 A. Dell’Acqua,36 L. Dell’Asta,74a,74b M. Delmastro,5C. Delporte,65 P. A. Delsart,58 D. A. DeMarco,167 S. Demers,183 M. Demichev,80 G. Demontigny,110 S. P. Denisov,123 L. D’Eramo,136 D. Derendarz,85 J. E. Derkaoui,35d F. Derue,136 P. Dervan,91 K. Desch,24 C. Deterre,46 K. Dette,167 C. Deutsch,24 M. R. Devesa,30 P. O. Deviveiros,36 A. Dewhurst,144 F. A. Di Bello,54 A. Di Ciaccio,74a,74b L. Di Ciaccio,5W. K. Di Clemente,137 C. Di Donato,70a,70b A. Di Girolamo,36 G. Di Gregorio,72a,72b B. Di Micco,75a,75b R. Di Nardo,75a,75b K. F. Di Petrillo,59 R. Di Sipio,167 D. Di Valentino,34 C. Diaconu,102 F. A. Dias,40 T. Dias Do Vale,140a M. A. Diaz,147a J. Dickinson,18 E. B. Diehl,106 J. Dietrich,19 S. Díez Cornell,46 A. Dimitrievska,18 W. Ding,15b J. Dingfelder,24 F. Dittus,36 F. Djama,102 T. Djobava,159b J. I. Djuvsland,17 M. A. B. Do Vale,81c M. Dobre,27b D. Dodsworth,26 C. Doglioni,97 J. Dolejsi,143 Z. Dolezal,143 M. Donadelli,81d B. Dong,60c J. Donini,38 A. D’onofrio,15c M. D’Onofrio,91 J. Dopke,144 A. Doria,70a M. T. Dova,89 A. T. Doyle,57 E. Drechsler,152 E. Dreyer,152 T. Dreyer,53 A. S. Drobac,170 D. Du,60b Y. Duan,60b F. Dubinin,111 M. Dubovsky,28a A. Dubreuil,54 E. Duchovni,180 G. Duckeck,114 A. Ducourthial,136 O. A. Ducu,110 D. Duda,115 A. Dudarev,36 A. C. Dudder,100 E. M. Duffield,18 L. Duflot,65 M. Dührssen,36 C. Dülsen,182 M. Dumancic,180 A. E. Dumitriu,27b A. K. Duncan,57 M. Dunford,61a A. Duperrin,102 H. Duran Yildiz,4a M. Düren,56 A. Durglishvili,159b D. Duschinger,48 B. Dutta,46 D. Duvnjak,1G. I. Dyckes,137 M. Dyndal,36 S. Dysch,101 B. S. Dziedzic,85 K. M. Ecker,115 R. C. Edgar,106 M. G. Eggleston,49 T. Eifert,8G. Eigen,17 K. Einsweiler,18 T. Ekelof,172 H. El Jarrari,35e R. El Kosseifi,102 V. Ellajosyula,172 M. Ellert,172 F. Ellinghaus,182 A. A. Elliot,93 N. Ellis,36 J. Elmsheuser,29 M. Elsing,36 D. Emeliyanov,144 A. Emerman,39 Y. Enari,163 M. B. Epland,49 J. Erdmann,47 A. Ereditato,20 P. A. Erland,85 M. Errenst,36 M. Escalier,65 C. Escobar,174 O. Estrada Pastor,174 E. Etzion,161 H. Evans,66 A. Ezhilov,138 F. Fabbri,57 L. Fabbri,23b,23a V. Fabiani,119 G. Facini,95 R. M. Faisca Rodrigues Pereira,140a R. M. Fakhrutdinov,123 S. Falciano,73a P. J. Falke,5S. Falke,5J. Faltova,143 Y. Fang,15a Y. Fang,15a G. Fanourakis,44 M. Fanti,69a,69b M. Faraj,67a,67c,o A. Farbin,8 A. Farilla,75a E. M. Farina,71a,71b T. Farooque,107 S. Farrell,18 S. M. Farrington,50 P. Farthouat,36 F. Fassi,35e P. Fassnacht,36 D. Fassouliotis,9M. Faucci Giannelli,50 W. J. Fawcett,32 L. Fayard,65 O. L. Fedin,138,p W. Fedorko,175 A. Fehr,20 M. Feickert,173 L. Feligioni,102 A. Fell,149 C. Feng,60b M. Feng,49 M. J. Fenton,57 A. B. Fenyuk,123 S. W. Ferguson,43 J. Ferrando,46 A. Ferrante,173 A. Ferrari,172 P. Ferrari,120 R. Ferrari,71a D. E. Ferreira de Lima,61b A. Ferrer,174 D. Ferrere,54 MEASUREMENT OF SOFT-DROP JET OBSERVABLES IN PP …PHYS. REV. D 101, 052007 (2020) 052007-25 2Physics Department, SUNY Albany, Albany, New York, USA 3Department of Physics, University of Alberta, Edmonton, Alberta, Canada 4aDepartment of Physics, Ankara University, Ankara, Turkey 4bIstanbul Aydin University, Istanbul, Turkey 4cDivision of Physics, TOBB University of Economics and Technology, Ankara, Turkey 5LAPP, Universit´e Grenoble Alpes, Universit´e Savoie Mont Blanc, CNRS/IN2P3, Annecy, France 6High Energy Physics Division, Argonne National Laboratory, Argonne, Illinois, USA 7Department of Physics, University of Arizona, Tucson, Arizona, USA 8Department of Physics, University of Texas at Arlington, Arlington, Texas, USA 9Physics Department, National and Kapodistrian University of Athens, Athens, Greece 10Physics Department, National Technical University of Athens, Zografou, Greece 11Department of Physics, University of Texas at Austin, Austin, Texas, USA 12aBahcesehir University, Faculty of Engineering and Natural Sciences, Istanbul, Turkey 12bIstanbul Bilgi University, Faculty of Engineering and Natural Sciences, Istanbul, Turkey 12cDepartment of Physics, Bogazici University, Istanbul, Turkey 12dDepartment of Physics Engineering, Gaziantep University, Gaziantep, Turkey 13Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan 14Institut de Física d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona, Spain 15aInstitute of High Energy Physics, Chinese Academy of Sciences, Beijing, China 15bPhysics Department, Tsinghua University, Beijing, China 15cDepartment of Physics, Nanjing University, Nanjing, China 15dUniversity of Chinese Academy of Science (UCAS), Beijing, China 16Institute of Physics, University of Belgrade, Belgrade, Serbia 17Department for Physics and Technology, University of Bergen, Bergen, Norway 18Physics Division, Lawrence Berkeley National Laboratory and University of California, Berkeley, California, USA 19Institut für Physik, Humboldt Universität zu Berlin, Berlin, Germany 20Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University of Bern, Bern, Switzerland 21School of Physics and Astronomy, University of Birmingham, Birmingham, United Kingdom 22Facultad de Ciencias y Centro de Investigaciónes, Universidad Antonio Nariño, Bogota, Colombia 23aINFN Bologna and Universita’di Bologna, Dipartimento di Fisica, Bologna, Italy 23bINFN Sezione di Bologna, Bologna, Italy 24Physikalisches Institut, Universität Bonn, Bonn, Germany 25Department of Physics, Boston University, Boston, Massachusetts, USA 26Department of Physics, Brandeis University, Waltham, Massachusetts, USA 27aTransilvania University of Brasov, Brasov, Romania 27bHoria Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania 27cDepartment of Physics, Alexandru Ioan Cuza University of Iasi, Iasi, Romania 27dNational Institute for Research and Development of Isotopic and Molecular Technologies, Physics Department, Cluj-Napoca, Romania 27eUniversity Politehnica Bucharest, Bucharest, Romania 27fWest University in Timisoara, Timisoara, Romania 28aFaculty of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovak Republic 28bDepartment of Subnuclear Physics, Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice, Slovak Republic 29Physics Department, Brookhaven National Laboratory, Upton, New York, USA 30Departamento de Física, Universidad de Buenos Aires, Buenos Aires, Argentina 31California State University, California, USA 32Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom 33aDepartment of Physics, University of Cape Town, Cape Town, South Africa 33bDepartment of Mechanical Engineering Science, University of Johannesburg, Johannesburg, South Africa 33cUniversity of South Africa, Department of Physics, Pretoria, South Africa 33dSchool of Physics, University of the Witwatersrand, Johannesburg, South Africa 34Department of Physics, Carleton University, Ottawa, Ontario, Canada 35aFacult´e des Sciences Ain Chock, R´eseau Universitaire de Physique des Hautes Energies—Universit´e Hassan II, Casablanca, Morocco 35bFacult´e des Sciences, Universit´e Ibn-Tofail, K´enitra, Morocco G. AAD et al. PHYS. REV. D 101, 052007 (2020) 052007-32 35cFacult´e des Sciences Semlalia, Universit´e Cadi Ayyad, LPHEA-Marrakech, Morocco 35dFacult´e des Sciences, Universit´e Mohamed Premier and LPTPM, Oujda, Morocco 35eFacult´e des sciences, Universit´e Mohammed V, Rabat, Morocco 36CERN, Geneva, Switzerland 37Enrico Fermi Institute, University of Chicago, Chicago, Illinois, USA 38LPC, Universit´e Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand, France 39Nevis Laboratory, Columbia University, Irvington, New York, USA 40Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark 41aDipartimento di Fisica, Universit`a della Calabria, Rende, Italy 41bINFN Gruppo Collegato di Cosenza, Laboratori Nazionali di Frascati, Frascati, Italy 42Physics Department, Southern Methodist University, Dallas, Texas, USA 43Physics Department, University of Texas at Dallas, Richardson, Texas, USA 44National Centre for Scientific Research “Demokritos”, Agia Paraskevi, Greece 45aDepartment of Physics, Stockholm University, Stockholm, Sweden 45bOskar Klein Centre, Stockholm, Sweden 46Deutsches Elektronen-Synchrotron DESY, Hamburg and Zeuthen, Germany 47Lehrstuhl für Experimentelle Physik IV, Technische Universität Dortmund, Dortmund, Germany 48Institut für Kern- und Teilchenphysik, Technische Universität Dresden, Dresden, Germany 49Department of Physics, Duke University, Durham, North Carolina, USA 50SUPA—School of Physics and Astronomy, University of Edinburgh, Edinburgh, United Kingdom 51INFN e Laboratori Nazionali di Frascati, Frascati, Italy 52Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany 53II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen, Germany 54D´epartement de Physique Nucl´eaire et Corpusculaire, Universit´e de Gen`eve, Gen`eve, Switzerland 55aDipartimento di Fisica, Universit`a di Genova, Genova, Italy 55bINFN Sezione di Genova, Genova, Italy 56II. Physikalisches Institut, Justus-Liebig-Universität Giessen, Giessen, Germany 57SUPA—School of Physics and Astronomy, University of Glasgow, Glasgow, United Kingdom 58LPSC, Universit´e Grenoble Alpes, CNRS/IN2P3, Grenoble INP, Grenoble, France 59Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge, Massachusetts, USA 60aDepartment of Modern Physics and State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China, Hefei, China 60bInstitute of Frontier and Interdisciplinary Science and Key Laboratory of Particle Physics and Particle Irradiation (MOE), Shandong University, Qingdao, China 60cSchool of Physics and Astronomy, Shanghai Jiao Tong University, KLPPAC-MoE, SKLPPC, Shanghai, China 60dTsung-Dao Lee Institute, Shanghai, China 61aKirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany 61bPhysikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany 62Faculty of Applied Information Science, Hiroshima Institute of Technology, Hiroshima, Japan 63aDepartment of Physics, Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China 63bDepartment of Physics, University of Hong Kong, Hong Kong, China 63cDepartment of Physics and Institute for Advanced Study, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China 64Department of Physics, National Tsing Hua University, Hsinchu, Taiwan 65IJCLab, Universit´e Paris-Saclay, CNRS/IN2P3, 91405, Orsay, France 66Department of Physics, Indiana University, Bloomington, Indiana, USA 67aINFN Gruppo Collegato di Udine, Sezione di Trieste, Udine, Italy 67bICTP, Trieste, Italy 67cDipartimento Politecnico di Ingegneria e Architettura, Universit`a di Udine, Udine, Italy 68aINFN Sezione di Lecce, Lecce, Italy 68bDipartimento di Matematica e Fisica, Universit`a del Salento, Lecce, Italy 69aINFN Sezione di Milano, Milano, Italy 69bDipartimento di Fisica, Universit`a di Milano, Milano, Italy 70aINFN Sezione di Napoli, Napoli, Italy 70bDipartimento di Fisica, Universit`a di Napoli, Napoli, Italy 71aINFN Sezione di Pavia, Pavia, Italy 71bDipartimento di Fisica, Universit`a di Pavia, Pavia, Italy 72aINFN Sezione di Pisa, Pisa, Italy 72bDipartimento di Fisica E. Fermi, Universit`a di Pisa, Pisa, Italy MEASUREMENT OF SOFT-DROP JET OBSERVABLES IN PP …PHYS. REV. D 101, 052007 (2020) 052007-33 73aINFN Sezione di Roma, Roma, Italy 73bDipartimento di Fisica, Sapienza Universit`a di Roma, Roma, Italy 74aINFN Sezione di Roma Tor Vergata, Roma, Italy 74bDipartimento di Fisica, Universit`a di Roma Tor Vergata, Roma, Italy 75aINFN Sezione di Roma Tre, Roma, Italy 75bDipartimento di Matematica e Fisica, Universit`a Roma Tre, Roma, Italy 76aINFN-TIFPA, Trento, Italy 76bUniversit`a degli Studi di Trento, Trento, Italy 77Institut für Astro- und Teilchenphysik, Leopold-Franzens-Universität, Innsbruck, Austria 78University of Iowa, Iowa City, Iowa, USA 79Department of Physics and Astronomy, Iowa State University, Ames, Iowa, USA 80Joint Institute for Nuclear Research, Dubna, Russia 81aDepartamento de Engenharia El´etrica, Universidade Federal de Juiz de Fora (UFJF), Juiz de Fora, Brazil 81bUniversidade Federal do Rio De Janeiro COPPE/EE/IF, Rio de Janeiro, Brazil 81cUniversidade Federal de São João del Rei (UFSJ), São João del Rei, Brazil 81dInstituto de Física, Universidade de São Paulo, São Paulo, Brazil 82KEK, High Energy Accelerator Research Organization, Tsukuba, Japan 83Graduate School of Science, Kobe University, Kobe, Japan 84aAGH University of Science and Technology, Faculty of Physics and Applied Computer Science, Krakow, Poland 84bMarian Smoluchowski Institute of Physics, Jagiellonian University, Krakow, Poland 85Institute of Nuclear Physics Polish Academy of Sciences, Krakow, Poland 86Faculty of Science, Kyoto University, Kyoto, Japan 87Kyoto University of Education, Kyoto, Japan 88Research Center for Advanced Particle Physics and Department of Physics, Kyushu University, Fukuoka, Japan 89Instituto de Física La Plata, Universidad Nacional de La Plata and CONICET, La Plata, Argentina 90Physics Department, Lancaster University, Lancaster, United Kingdom 91Oliver Lodge Laboratory, University of Liverpool, Liverpool, United Kingdom 92Department of Experimental Particle Physics, Jožef Stefan Institute and Department of Physics, University of Ljubljana, Ljubljana, Slovenia 93School of Physics and Astronomy, Queen Mary University of London, London, United Kingdom 94Department of Physics, Royal Holloway University of London, Egham, United Kingdom 95Department of Physics and Astronomy, University College London, London, United Kingdom 96Louisiana Tech University, Ruston, Louisiana, USA 97Fysiska institutionen, Lunds universitet, Lund, Sweden 98Centre de Calcul de l’Institut National de Physique Nucl´eaire et de Physique des Particules (IN2P3), Villeurbanne, France 99Departamento de Física Teorica C-15 and CIAFF, Universidad Autónoma de Madrid, Madrid, Spain 100Institut für Physik, Universität Mainz, Mainz, Germany 101School of Physics and Astronomy, University of Manchester, Manchester, United Kingdom 102CPPM, Aix-Marseille Universit´e, CNRS/IN2P3, Marseille, France 103Department of Physics, University of Massachusetts, Amherst, Massachusetts, USA 104Department of Physics, McGill University, Montreal, Quebec, Canada 105School of Physics, University of Melbourne, Victoria, Australia 106Department of Physics, University of Michigan, Ann Arbor, Michigan, USA 107Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan, USA 108B.I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk, Belarus 109Research Institute for Nuclear Problems of Byelorussian State University, Minsk, Belarus 110Group of Particle Physics, University of Montreal, Montreal, Quebec, Canada 111P.N. Lebedev Physical Institute of the Russian Academy of Sciences, Moscow, Russia 112National Research Nuclear University MEPhI, Moscow, Russia 113D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Moscow, Russia 114Fakultät für Physik, Ludwig-Maximilians-Universität München, München, Germany 115Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), München, Germany 116Nagasaki Institute of Applied Science, Nagasaki, Japan 117Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya, Japan 118Department of Physics and Astronomy, University of New Mexico, Albuquerque, New Mexico, USA G. AAD et al. PHYS. REV. D 101, 052007 (2020) 052007-34 119Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen, Netherlands 120Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam, Netherlands 121Department of Physics, Northern Illinois University, DeKalb, Illinois, USA 122aBudker Institute of Nuclear Physics and NSU, SB RAS, Novosibirsk, Russia 122bNovosibirsk State University Novosibirsk, Novosibirsk, Russia 123Institute for High Energy Physics of the National Research Centre Kurchatov Institute, Protvino, Russia 124Institute for Theoretical and Experimental Physics named by A.I. Alikhanov of National Research Centre “Kurchatov Institute”, Moscow, Russia 125Department of Physics, New York University, New York, New York, USA 126Ochanomizu University, Otsuka, Bunkyo-ku, Tokyo, Japan 127Ohio State University, Columbus, Ohio, USA 128Faculty of Science, Okayama University, Okayama, Japan 129Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman, Oklahoma, USA 130Department of Physics, Oklahoma State University, Stillwater, Oklahoma, USA 131Palacký University, RCPTM, Joint Laboratory of Optics, Olomouc, Czech Republic 132Center for High Energy Physics, University of Oregon, Eugene, Oregon, USA 133Graduate School of Science, Osaka University, Osaka, Japan 134Department of Physics, University of Oslo, Oslo, Norway 135Department of Physics, Oxford University, Oxford, United Kingdom 136LPNHE, Sorbonne Universit´e, Universit´e de Paris, CNRS/IN2P3, Paris, France 137Department of Physics, University of Pennsylvania, Philadelphia, Pennsylvania, USA 138Konstantinov Nuclear Physics Institute of National Research Centre “Kurchatov Institute”, PNPI, St. Petersburg, Russia 139Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, Pennsylvania, USA 140aLaboratório de Instrumentação e Física Experimental de Partículas—LIP, Lisboa, Portugal 140bDepartamento de Física, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal 140cDepartamento de Física, Universidade de Coimbra, Coimbra, Portugal 140dCentro de Física Nuclear da Universidade de Lisboa, Lisboa, Portugal 140eDepartamento de Física, Universidade do Minho, Braga, Portugal 140fDepartamento de Física Teórica y del Cosmos, Universidad de Granada, Granada, Spain 140gDep Física and CEFITEC of Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal 140hInstituto Superior T´ecnico, Universidade de Lisboa, Lisboa, Portugal 141Institute of Physics of the Czech Academy of Sciences, Prague, Czech Republic 142Czech Technical University in Prague, Prague, Czech Republic 143Charles University, Faculty of Mathematics and Physics, Prague, Czech Republic 144Particle Physics Department, Rutherford Appleton Laboratory, Didcot, United Kingdom 145IRFU, CEA, Universit´e Paris-Saclay, Gif-sur-Yvette, France 146Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz, California, USA 147aDepartamento de Física, Pontificia Universidad Católica de Chile, Santiago, Chile 147bUniversidad Andres Bello, Department of Physics, Santiago, Chile 147cDepartamento de Física, Universidad T´ecnica Federico Santa María, Valparaíso, Chile 148Department of Physics, University of Washington, Seattle, Washington, USA 149Department of Physics and Astronomy, University of Sheffield, Sheffield, United Kingdom 150Department of Physics, Shinshu University, Nagano, Japan 151Department Physik, Universität Siegen, Siegen, Germany 152Department of Physics, Simon Fraser University, Burnaby, British Columbia, Canada 153SLAC National Accelerator Laboratory, Stanford, California, USA 154Physics Department, Royal Institute of Technology, Stockholm, Sweden 155Departments of Physics and Astronomy, Stony Brook University, Stony Brook, New York, USA 156Department of Physics and Astronomy, University of Sussex, Brighton, United Kingdom 157School of Physics, University of Sydney, Sydney, Australia 158Institute of Physics, Academia Sinica, Taipei, Taiwan 159aE. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi, Georgia 159bHigh Energy Physics Institute, Tbilisi State University, Tbilisi, Georgia 160Department of Physics, Technion, Israel Institute of Technology, Haifa, Israel 161Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel MEASUREMENT OF SOFT-DROP JET OBSERVABLES IN PP …PHYS. REV. D 101, 052007 (2020) 052007-35 162Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece 163International Center for Elementary Particle Physics and Department of Physics, University of Tokyo, Tokyo, Japan 164Graduate School of Science and Technology, Tokyo Metropolitan University, Tokyo, Japan 165Department of Physics, Tokyo Institute of Technology, Tokyo, Japan 166Tomsk State University, Tomsk, Russia 167Department of Physics, University of Toronto, Toronto, Ontario, Canada 168aTRIUMF, Vancouver, British Columbia, Canada 168bDepartment of Physics and Astronomy, York University, Toronto, Ontario, Canada 169Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Japan 170Department of Physics and Astronomy, Tufts University, Medford, Massachusetts, USA 171Department of Physics and Astronomy, University of California Irvine, Irvine, California, USA 172Department of Physics and Astronomy, University of Uppsala, Uppsala, Sweden 173Department of Physics, University of Illinois, Urbana, Illinois, USA 174Instituto de Física Corpuscular (IFIC), Centro Mixto Universidad de Valencia—CSIC, Valencia, Spain 175Department of Physics, University of British Columbia, Vancouver, British Columbia, Canada 176Department of Physics and Astronomy, University of Victoria, Victoria, British Columbia, Canada 177Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany 178Department of Physics, University of Warwick, Coventry, United Kingdom 179Waseda University, Tokyo, Japan 180Department of Particle Physics, Weizmann Institute of Science, Rehovot, Israel 181Department of Physics, University of Wisconsin, Madison, Wisconsin, USA 182Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal, Wuppertal, Germany 183Department of Physics, Yale University, New Haven, Connecticut, USA aDeceased. bAlso at Department of Physics, King’s College London, London, United Kingdom. cAlso at Instituto de Fisica Teorica, IFT-UAM/CSIC, Madrid, Spain. dAlso at TRIUMF, Vancouver, British Columbia, Canada. eAlso at Department of Physics and Astronomy, University of Louisville, Louisville, Kentucky, USA. fAlso at Physics Department, An-Najah National University, Nablus, Palestine. gAlso at Department of Physics, University of Fribourg, Fribourg, Switzerland. hAlso at Physics Dept, University of South Africa, Pretoria, South Africa. iAlso at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona, Spain. jAlso at Tomsk State University, Tomsk, and Moscow Institute of Physics and Technology State University, Dolgoprudny, Russia. kAlso at Department of Physics, Ben Gurion University of the Negev, Beer Sheva, Israel. lAlso at Universita di Napoli Parthenope, Napoli, Italy. mAlso at Institute of Particle Physics (IPP), Vancouver, Canada. nAlso at Department of Physics, University of Adelaide, Adelaide, Australia. oAlso at Dipartimento di Matematica, Informatica e Fisica, Universit`a di Udine, Udine, Italy. pAlso at Department of Physics, St. Petersburg State Polytechnical University, St. Petersburg, Russia. qAlso at Borough of Manhattan Community College, City University of New York, New York, New York, USA. rAlso at Department of Physics, California State University, Fresno, California, USA. sAlso at Department of Financial and Management Engineering, University of the Aegean, Chios, Greece. tAlso at Department of Physics, California State University, East Bay, California, USA. uAlso at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona, Spain. vAlso at Department of Physics, University of Michigan, Ann Arbor, Michigan, USA. wAlso at IJCLab, Universit´e Paris-Saclay, CNRS/IN2P3, 91405, Orsay, France. xAlso at Graduate School of Science, Osaka University, Osaka, Japan. yAlso at Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany. zAlso at Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan. aaAlso at Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen, Netherlands. bbAlso at CERN, Geneva, Switzerland. ccAlso at Department of Physics, Stanford University, Stanford, California, USA. ddAlso at Manhattan College, New York, New York, USA. eeAlso at Joint Institute for Nuclear Research, Dubna, Russia. ffAlso at Hellenic Open University, Patras, Greece. G. AAD et al. PHYS. REV. D 101, 052007 (2020) 052007-36 ggAlso at The City College of New York, New York, New York, USA. hhAlso at Department of Physics, California State University, Sacramento, California, USA. iiAlso at Moscow Institute of Physics and Technology State University, Dolgoprudny, Russia. jjAlso at D´epartement de Physique Nucl´eaire et Corpusculaire, Universit´e de Gen`eve, Gen`eve, Switzerland. kkAlso at Louisiana Tech University, Ruston, Louisiana, USA. llAlso at Institute for Nuclear Research and Nuclear Energy (INRNE) of the Bulgarian Academy of Sciences, Sofia, Bulgaria. mmAlso at Faculty of Physics, M.V. Lomonosov Moscow State University, Moscow, Russia. nnAlso at Department of Applied Physics and Astronomy, University of Sharjah, Sharjah, United Arab Emirates. ooAlso at Institut für Experimentalphysik, Universität Hamburg, Hamburg, Germany. ppAlso at CPPM, Aix-Marseille Universit´e, CNRS/IN2P3, Marseille, France. qqAlso at National Research Nuclear University MEPhI, Moscow, Russia. rrAlso at Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Budapest, Hungary. ssAlso at Giresun University, Faculty of Engineering, Giresun, Turkey. ttAlso at Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan, USA. MEASUREMENT OF SOFT-DROP JET OBSERVABLES IN PP …PHYS. REV. D 101, 052007 (2020) 052007-37