Search for an Excess of Electron Neutrino Interactions in MicroBooNE Using Multiple Final-State Topologies

Search for an Excess of Electron Neutrino Interactions in MicroBooNE Using Multiple Final-State Topologies P. Abratenko,33 R. An,14 J. Anthony,4L. Arellano,18 J. Asaadi,32 A. Ashkenazi,30 S. Balasubramanian,11 B. Baller,11 C. Barnes,20 G. Barr,23 V. Basque,18 L. Bathe-Peters,13 O. Benevides Rodrigues,29 S. Berkman,11 A. Bhanderi,18 A. Bhat,29 M. Bishai,2A. Blake,16 T. Bolton,15 J. Y. Book,13 L. Camilleri,9D. Caratelli,11 I. Caro Terrazas,8F. Cavanna,11 G. Cerati,11 Y. Chen,1D. Cianci,9G. H. Collin,19 J. M. Conrad,19 M. Convery,26 L. Cooper-Troendle,36 J. I. Crespo-Anadón,5 M. Del Tutto,11 S. R. Dennis,4P. Detje,4A. Devitt,16 R. Diurba,21 R. Dorrill,14 K. Duffy,11 S. Dytman,24 B. Eberly,28 A. Ereditato,1L. Escudero Sanchez,4J. J. Evans,18 R. Fine,17 G. A. Fiorentini Aguirre,27 R. S. Fitzpatrick,20 B. T. Fleming,36 N. Foppiani,13 D. Franco,36 A. P. Furmanski,21 D. Garcia-Gamez,12 S. Gardiner,11 G. Ge,9V. Genty,9 S. Gollapinni,31,17 O. Goodwin,18 E. Gramellini,11 P. Green,18 H. Greenlee,11 W. Gu,2R. Guenette,13 P. Guzowski,18 L. Hagaman,36 O. Hen,19 C. Hilgenberg,21 G. A. Horton-Smith,15 A. Hourlier,19 R. Itay,26 C. James,11 X. Ji,2L. Jiang,34 J. H. Jo,36 R. A. Johnson,7Y.-J. Jwa,9D. Kaleko,9D. Kalra,9N. Kamp,19 N. Kaneshige,3G. Karagiorgi,9W. Ketchum,11 M. Kirby,11 T. Kobilarcik,11 I. Kreslo,1R. LaZur,8I. Lepetic,25 K. Li,36 Y. Li,2K. Lin,17 A. Lister,16 B. R. Littlejohn,14 W. C. Louis,17 X. Luo,3K. Manivannan,29 C. Mariani,34 D. Marsden,18 J. Marshall,35 D. A. Martinez Caicedo,27 K. Mason,33 A. Mastbaum,25 N. McConkey,18 V. Meddage,15 T. Mettler,1K. Miller,6J. Mills,33 K. Mistry,18 A. Mogan,31 T. Mohayai,11 J. Moon,19 M. Mooney,8A. F. Moor,4C. D. Moore,11 L. Mora Lepin,18 J. Mousseau,20 M. Murphy,34 D. Naples,24 A. Navrer-Agasson,18 M. Nebot-Guinot,10 R. K. Neely,15 D. A. Newmark,17 J. Nowak,16 M. Nunes,29 O. Palamara,11 V. Paolone,24 A. Papadopoulou,19 V. Papavassiliou,22 S. F. Pate,22 N. Patel,16 A. Paudel,15 Z. Pavlovic,11 E. Piasetzky,30 I. D. Ponce-Pinto,36 S. Prince,13 X. Qian,2J. L. Raaf,11 V. Radeka,2A. Rafique,15 M. Reggiani-Guzzo,18 L. Ren,22 L. C. J. Rice,24 L. Rochester,26 J. Rodriguez Rondon,27 M. Rosenberg,24 M. Ross-Lonergan,9B. Russell,36 G. Scanavini,36 D. W. Schmitz,6A. Schukraft,11 W. Seligman,9M. H. Shaevitz,9R. Sharankova,33 J. Shi,4J. Sinclair,1 A. Smith,4E. L. Snider,11 M. Soderberg,29 S. Söldner-Rembold,18 S. R. Soleti,23,13 P. Spentzouris,11 J. Spitz,20 M. Stancari,11 J. St. John,11 T. Strauss,11 K. Sutton,9S. Sword-Fehlberg,22 A. M. Szelc,10 W. Tang,31 K. Terao,26 M. Thomson,4C. Thorpe,16 D. Totani,3M. Toups,11 Y.-T. Tsai,26 M. A. Uchida,4T. Usher,26 W. Van De Pontseele,23,13 B. Viren,2M. Weber,1H. Wei,2Z. Williams,32 S. Wolbers,11 T. Wongjirad,33 M. Wospakrik,11 K. Wresilo,4N. Wright,19 W. Wu,11 E. Yandel,3T. Yang,11 G. Yarbrough,31 L. E. Yates,19 H. W. Yu,2G. P. Zeller ,11 J. Zennamo,11 and C. Zhang2 (MicroBooNE Collaboration)* 1Universität Bern, Bern CH-3012, Switzerland 2Brookhaven National Laboratory (BNL), Upton, New York 11973, USA 3University of California, Santa Barbara, California 93106, USA 4University of Cambridge, Cambridge CB3 0HE, United Kingdom 5Centro de Investigaciones Energ´eticas, Medioambientales y Tecnológicas (CIEMAT), Madrid E-28040, Spain 6University of Chicago, Chicago, Illinois 60637, USA 7University of Cincinnati, Cincinnati, Ohio 45221, USA 8Colorado State University, Fort Collins, Colorado 80523, USA 9Columbia University, New York, New York 10027, USA 10University of Edinburgh, Edinburgh EH9 3FD, United Kingdom 11Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA 12Universidad de Granada, Granada E-18071, Spain 13Harvard University, Cambridge, Massachusetts 02138, USA 14Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA 15Kansas State University (KSU), Manhattan, Kansas 66506, USA 16Lancaster University, Lancaster LA1 4YW, United Kingdom 17Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA 18The University of Manchester, Manchester M13 9PL, United Kingdom 19Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA 20University of Michigan, Ann Arbor, Michigan 48109, USA 21University of Minnesota, Minneapolis, Minnesota 55455, USA 22New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA 23University of Oxford, Oxford OX1 3RH, United Kingdom PHYSICAL REVIEW LETTERS 128, 241801 (2022) Editors' Suggestion Featured in Physics 0031-9007=22=128(24)=241801(9) 241801-1 Published by the American Physical Society 24University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA 25Rutgers University, Piscataway, New Jersey 08854, USA 26SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA 27South Dakota School of Mines and Technology (SDSMT), Rapid City, South Dakota 57701, USA 28University of Southern Maine, Portland, Maine 04104, USA 29Syracuse University, Syracuse, New York 13244, USA 30Tel Aviv University, Tel Aviv, Israel, 69978 31University of Tennessee, Knoxville, Tennessee 37996, USA 32University of Texas, Arlington, Texas 76019, USA 33Tufts University, Medford, Massachusetts 02155, USA 34Center for Neutrino Physics, Virginia Tech, Blacksburg, Virginia 24061, USA 35University of Warwick, Coventry CV4 7AL, United Kingdom 36Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA (Received 29 October 2021; accepted 13 April 2022; published 13 June 2022) We present a measurement of νeinteractions from the Fermilab Booster Neutrino Beam using the MicroBooNE liquid argon time projection chamber to address the nature of the excess of low energy interactions observed by the MiniBooNE Collaboration. Three independent νesearches are performed across multiple single electron final states, including an exclusive search for two-body scattering events with a single proton, a semi-inclusive search for pionless events, and a fully inclusive search for events containing all hadronic final states. With differing signal topologies, statistics, backgrounds, reconstruction algorithms, and analysis approaches, the results are found to be either consistent with or modestly lower than the nominal νerate expectations from the Booster Neutrino Beam and no excess of νeevents is observed. DOI: 10.1103/PhysRevLett.128.241801 MicroBooNE is the first liquid argon time projection chamber (LArTPC) to acquire high statistics samples of neutrino interactions on argon. Using this unique dataset, MicroBooNE has pioneered a large body of results on neutrino interactions [1–7], astrophysical [8,9], and beyond the standard model physics [10,11], neutrino event reconstruction [12–23], and detector properties [24–33]. Here, we report the first measurement of electron neutrinos produced in the Fermilab Booster Neutrino Beamline (BNB) using the MicroBooNE detector. This multipronged search is aimed at investigating the as-yet unexplained low energy excess of electromagnetic activity observed by the MiniBooNE Collaboration [34]. Over the past decade, there has been a rich and evolving landscape of theoretical interpretations to explain the origin of the observed MiniBooNE excess, including standard processes [35] as well as new physics involving sterile neutrinos [36,37], dark sector portals [38–40], heavy neutral leptons [41,42], nonstandard Higgs physics [43–46], new particles produced in the beam [47,48], and mixed models of sterile neutrino oscillations and decay [49,50]. A number of scenarios have also been ruled out [51]. Because of the variety of theoretical explanations and their possible signatures, MicroBooNE has developed three distinct νesearches targeting the MiniBooNE signal: an exclusive search for two-body νecharged current quasielastic (CCQE) scattering, a semi-inclusive search for pionless νeevents, and an inclusive νesearch containing any hadronic final state. Additionally, a companion singlephoton-based search focused on radiative decays of the Δ resonance is reported elsewhere [52]. This work capitalizes on the broad capabilities of a LArTPC to perform high purity measurements of electron neutrinos across multiple signal topologies and with significantly improved ability to distinguish whether an electromagnetic shower is electron or photon-induced compared to Cherenkov-based detectors such as MiniBooNE. The advantage of this particular probe of the MiniBooNE signal is that MicroBooNE is located in the same neutrino beamline and at roughly the same location as MiniBooNE, but uses an imaging detector capable of mm-scale spatial resolution and substantially lower energy detection thresholds for many particle types. The MicroBooNE LArTPC [53,54] itself contains 85 tons of liquid argon and is sited 72.5 m upstream of the MiniBooNE detector hall at a distance of 468.5 m from the BNB proton target. The data used in this work are taken from an exposure of 7×1020 protons on target (POT) collected in neutrino mode, a 93.7% νμ(5.8% ¯ νμ) pure beam, from February 2016 to July 2018. These results represent an initial probe 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 LETTERS 128, 241801 (2022) 241801-2 into electron neutrino production in the BNB using roughly half of the total data collected by MicroBooNE. Two different data streams are used in this analysis: an on-beam data sample triggered by BNB neutrino spills and an offbeam data sample taken during periods when no beam was received. The off-beam data sample is used for a direct data-based measurement of cosmic-induced backgrounds which are of importance given MicroBooNE’s location near the surface. While probing different event topologies with distinct event reconstruction methods, the three independent electron neutrino searches in MicroBooNE share several aspects in common. To simulate neutrino interactions in argon, the analyses rely on a GEANT 4-based [55] simulation of the neutrino beam [56], a variation of the GENIE V 3event generator [57] specifically tuned to data that reflects our best knowledge of neutrino scattering in the BNB energy range [58], and a GEANT 4-based [55] detector simulation for particle propagation, with the processing of the charge response of the TPC and modeling of scintillation light implemented in the LA r S oft framework [59]. The data-driven detector simulation represents a significant upgrade from what has been historically available to model LArTPCs and incorporates pioneering work performed by MicroBooNE on wire signal processing [28,29], noise removal [31], electric field mapping [25,26], and detector calibrations [27]. A common framework is additionally used to evaluate neutrino flux, neutrino cross section, and detector systematics. The evaluation of neutrino flux uncertainties is built on techniques developed by MiniBooNE [56]. A total of more than 50 model parameters are varied within GENIE to assess uncertainties related to simulating neutrino interactions and final state effects in argon [58]. A full complement of LArTPC detector systematics are modeled, many with a novel data-driven technique built on comparisons of wire response in data and simulation [60]. In addition, beam-related νeevent predictions and uncertainties are further constrained through calculation of conditional means and variances using data-driven measurements of neutrino charged current (CC) and neutral current (NC) interactions in MicroBooNE, with constraint samples tailored to each of the νeanalysis approaches. Table I summarizes the signal definitions, constraint samples, and reconstruction approaches for the different analyses. Common approaches are also used for assessing agreement between observed and predicted νesamples and for hypothesis testing related to the presence or absence of an anomalous νerate. In each search, the neutrino energy (Eν) is based on reconstructed final state particle energies [61–63]. Binned reconstructed Eνdistributions for data and simulation are then compared using a combined Neyman-Pearson test statistic, χ2 CNP [64], which approximates well to the Poisson-likelihood test statistic for low event numbers. Similar statistical studies are performed to test a model of the νeevent excess with an Eνspectrum and normalization representative of that observed in the MiniBooNE experiment. A blind analysis scheme was adopted in which the signal region BNB νedata were only accessed after each analysis was completed. Details on each νeanalysis, including the optimization of purity and efficiency in each approach, databased sidebands and simulated datasets used to validate the analyses, as well as data and Monte Carlo comparisons in a variety of kinematics, can be found in Refs. [61–63].The next three sections describe the strategy behind each of the νe searches and a summary of their results. Two-body νeCCQE scattering (1e1p).—An exclusive selection of νecandidates satisfying two-body CCQE kinematic constraints is performed in MicroBooNE with an analysis strategy focused around the use of deeplearning techniques [61]. The CCQE process is predicted to dominate at low energies, with 77% of νeevents below 500 MeV (in true Eν) expected to interact via this channel in MicroBooNE. A strength of this selection is its clean final state topology, which simplifies event reconstruction and selection. The pure CCQE requirement also allows the use of kinematic variables for signal selection, such as proton momentum, transverse momentum, neutrino direction, momentum transfer, and Bjorken x. For this analysis, only fully contained candidate νeand νμinteractions with one reconstructed final state lepton and proton are selected. Final-state particle content and kinematics are reconstructed by applying a combination of conventional and deep-learning tools to prepared event images. Conventional tools are used to remove cosmic backgrounds [15], identify candidate neutrino interaction vertices [65], and reconstruct final-state particle candidates and their kinematics [65,66], while convolutional neural networks (CNNs) are used to differentiate tracklike from showerlike image pixels [67] and determine particle species contained in event images [68]. After basic data quality selection criteria, boosted decision tree (BDT) ensembles exploit 23 (16) kinematic and topological variables, including those described above, to collect a purified CCQE νe(νμ) event set. The CNN TABLE I. Summary of signal definitions, signal-constraining datasets, and reconstruction approaches used for each of the three MicroBooNE νesearches. All samples require fully contained events with the exception of the 1eX analysis which additionally uses both partially and fully contained νμCC samples as constraints. νeFinal state Signal constraints Reconstruction approach 1e1pð0πÞCCQE νμCCQE Deep learning [61] 1eNð≥1Þp0π, 1e0p0π νμCC P ANDORA [62] 1eX νμCC, νμCC π0, νμNC π0 Wire-Cell [63] PHYSICAL REVIEW LETTERS 128, 241801 (2022) 241801-3 designed for particle identification is then used to select final candidates from this set. The results of the νeCCQE analysis are shown in Fig. 1. This selection is predicted to produce a 75% (75%) pure sample of νe(νμ) CCQE events in the reconstructed Eν range between 200 and 1200 MeV, with an efficiency of 6.6% for all true νeCCQE interactions in the LArTPC active volume. The average Eνenergy resolution for selected νeevents is estimated to be 16.5%, with negligible bias predicted between true and reconstructed Eν. The νe CCQE prediction is constrained using a high-statistics νμCCQE candidate dataset, which increases predicted νe counts by 6% and reduces systematic uncertainties on the prediction by a factor of 2.0. Statistical uncertainties dominate the final measurement. The postconstraint prediction yields 29.05.2ðstatÞ1.9ðsystÞevents in the full Eνrange given above (statistical errors follow the CNP formalism [64]), with most events (89%) predicted to arise from CCQE and non-CCQE νeinteractions intrinsic to the beam. In the final selection, a total of 25 data candidates are observed in this range. The χ2 CNP test statistic calculated between predicted and observed distributions is found to be 25.3 for the analysis in ten Eνbins (where 6.9 units of χ2 CNP result from a single bin at 850 MeV), leading to a pvalue of 0.014. Below 500 MeV, the χ2 CNP contribution is 7.9 for three Eνbins, with data in two of the three bins falling slightly below predicted values. Pionless νescattering (1eNp0π,1e0p0π).—A higher statistics search for pionless νeinteractions that includes any number of protons in the final state uses the PANDORA event reconstruction package [22], which has been exercised over the years to produce a wide variety of MicroBooNE physics measurements [2–7,10,11]. The P ANDORA pattern recognition software, which reconstructs and classifies LArTPC events, is combined with specialized tools that further remove cosmic-ray background events as well as identify the different particles produced in a neutrino interaction [69] and reconstruct their energies [6]. This search focuses on two exclusive channels with one electron and no pions in the final state: one with at least one visible proton (1eNp0π,N≥1) and one with no visible protons (1e0p0π). A strength of this selection is that the two topologies combined exactly replicate the electronlike signal event signature in MiniBooNE. This selection on fully contained events spanning neutrino energies from 10 to 2390 MeV provides an efficiency of 15% (9%) with a purity of 80% (43%) for 1eNp0π(1e0p0π) events. The typical energy resolution is 2% for protons, 3% for muons, and approximately 12% for electrons, resulting in a predicted Eνresolution of 15% with ∼5% bias. To constrain neutrino flux and cross section uncertainties on the predicted intrinsic νeevent rate, this analysis uses a highstatistics, 77% pure νμCC inclusive event sample [62] and makes use of the cosmic-ray tagger detector system in MicroBooNE [54] to further reduce cosmic backgrounds. This constraint reduces the systematic uncertainties in the νeselections by a factor of 1.7 and the result remains dominated by statistical uncertainties. This analysis is also validated using MicroBooNE data from the NuMI beam [70] that provides a large number of νe-argon interactions at a similar energy range as the BNB. The results of the PANDORA -based pionless νeanalysis are shown in Fig. 2. For the 1eNp0πchannel, 64 νedata events are observed compared to 86.88.8ðstatÞ 11.5ðsystÞevents expected (statistical errors follow the CNP formalism), in a reconstructed Eνrange between 10 and 2390 MeV. For the 1e0p0πchannel, 34 νedata events are observed compared to 30.25.6ðstatÞ4.3ðsystÞ events expected over that same energy range. The data are consistent with the prediction: in the region 150 MeV ≤Eν≤1550 MeV where the final statistical tests are performed, the χ2 CNP=ndf (and associated p values) relative to the nominal prediction are 14.9=10ð0.194Þ,16.7.9=10ð0.116Þ, and 31.56=20ð0.097Þ for the 1eNp0πchannel, 1e0p0πchannel, and both combined, respectively. As with the 1e1pCCQE search results, the data for the 1eNp0πchannel fall slightly below prediction. For the 1e0p0πchannel, the observed event count below 500 MeV is above prediction, albeit in a region with lower predicted νepurity. Inclusive νescattering (1eX).—The highest statistics νe analysis in MicroBooNE searches inclusively for all possible hadronic final states such as the type of analyses that will be performed in the future wide-band Deep Underground Neutrino Experiment (DUNE) which will have larger contributions from additional inelastic FIG. 1. Reconstructed neutrino energy for 1e1pCCQE candidate events in the deep-learning-based analysis. Backgrounds include contributions from cosmics and νμinteractions. The νe prediction constrained using νμdata is shown without (solid histogram) and with (red dotted) a model of the MiniBooNE low energy excess included (further detail in text). Systematic uncertainties on the constrained prediction are shown as a hatched band. PHYSICAL REVIEW LETTERS 128, 241801 (2022) 241801-4 scattering processes at higher energies. This analysis uses the Wire-Cell reconstruction paradigm [71] which forms three-dimensional images of particle-induced electron ionization tracks and showers via 1D wire position tomography. The 3D images are then processed by clustering algorithms and matched to light signals for cosmic rejection [13,14,72], before a deep neural network [73] is used to determine the neutrino candidate vertex. Finally, the events are characterized in terms of energy deposit, topology, and kinematics, for eventual event building, classification (e.g., νeCC, νμCC, π0, cosmic), and neutrino energy reconstruction. The strengths of this approach are its high efficiency and high purity. After all selections, the predicted efficiency for selecting inclusive νeCC (νμCC) events is 46% (68%) with a purity of 82% (92%) for 0<E ν< 2500 MeV. For fully contained events, the predicted calorimetric-based Eνresolution is 10%–15% (15%–20%) for νeCC (νμCC) events with ∼7% (10%) bias. In addition to the νμCC data samples, which include both fully and partially contained events in the detector, CC and NC interactions with a reconstructed π0serve as additional constraints for reducing systematic uncertainties and therefore maximizing sensitivity. A high statistics sample of νeevents from the NuMI beam also serves to validate the analysis. The constraints reduce the fractional uncertainty on the predicted number of fully contained νeCC events with reconstructed Eν<600 MeV by a factor of 3.5 relative to the unconstrained prediction. After constraints, the largest systematic uncertainties for fully contained νeCC events are associated with limited Monte Carlo statistics associated with this rare event search, detector effects (mainly recombination and wire response), and neutrino cross section modeling [58].Comparedtoall systematic uncertainties, however, the statistical uncertainty on the data remains dominant. Figure 3shows the results of this inclusive νesearch. The postconstraint νeCC inclusive analysis finds a modest deficit compared to the prediction: 56 (338) data events in Eν<600 MeV (0<E ν<2500 MeV) with 69.68.0ðstatÞ5.0ðsystÞ[384.919.2ðstatÞ 15.9ðsystÞ] events expected. Good agreement is found between the data and the expectation from the BNB, with χ2 Pearson=ndf ¼17.9=25 and a corresponding pvalue of 0.848, across all energies. Notably, agreement between the data and expectation is also apparent when the Wire-Cell inclusive event sample is studied in terms of its exclusive components, 1e0pXπand 1eNpXπ(X≥0), where these subsamples are further described in Ref. [63]. Fit results.—The three aforementioned analysis selections are not designed to be disjoint to each other, and there is an overlap in the selected events. Of the 25 events selected in the 1e1pCCQE analysis, 16 are selected in either the pionless or inclusive analysis. Of the 98 events selected across both pionless analysis selections, 46 are selected in the inclusive analysis. All three analyses observe νecandidate event rates in general agreement with or below the predicted rates. Given the similar baseline and neutrino energies sampled by MicroBooNE and MiniBooNE, this picture appears to disfavor an interpretation of MiniBooNE’s observed electronlike excess signature as arising purely from an anomalously high rate of charged current νeinteractions. To more quantitatively address the comparison with the observed MiniBooNE data excess, all three analyses have performed statistical tests comparing datasets to a simple model of a MiniBooNE-like excess of νeinteractions [74]. Using MiniBooNE simulation, a response matrix is constructed translating the true incident Eνto reconstructed Eνunder a quasielastic assumption to the true incident Eν, accounting for detector response, acceptance, resolutions, FIG. 2. Reconstructed neutrino energy for pionless νecandidate events in the P ANDORA -based analysis: 1eNp0π(left) and 1e0p0π (right). Each of the plots extend from 10 to 2390 MeV with a 140 MeV bin width. The unconstrained number of predicted events is shown broken down by true interaction topology. The constrained predictions using νμdata are shown both with (red) and without (black) a model of the MiniBooNE low energy excess included (further detail in text). Systematic uncertainties on the constrained prediction are shown as a shaded band. PHYSICAL REVIEW LETTERS 128, 241801 (2022) 241801-5 event reconstruction, and selection efficiencies. Following a multidimensional unfolding procedure [75] on the MiniBooNE observation [76] and using only the statistical uncertainties on the MiniBooNE data and simulated events (to avoid any correlated uncertainties in flux and interaction models with MicroBooNE), MicroBooNE extracts an energy-dependent event rate of νeinteractions. The resulting scaling template, found to be robust against the number of unfolding iterations after an initial starting point corresponding to the MiniBooNE prediction, is derived from the increase in event rate relative to the MiniBooNE prediction and then applied to simulated intrinsic νeevents in MicroBooNE to form an “eLEE”signalmodel,shownbythe dashed lines in Figs. 1–3. This scaling template varies only in true neutrino energy, thus the electron Low Energy Excess (eLEE) model otherwise assumes the same kinematics and final-state topologies as MicroBooNE’sνesimulation— additional kinematic information from the MiniBooNE excess result is not considered. The range of the eLEE signal model is 200 <true Eν<800 MeV, as the unfolding procedure does not consider data below 200 MeV in neutrino energy and finds no significant excess at higher energies. This simple model reproduces a median MiniBooNE electronlike excess to which MicroBooNE’s results are compared, either by choosing a fixed excess normalization, x, matching MiniBooNE (x¼1), or by treating xas a free parameter to be extracted. While the MiniBooNE uncertainties are not directly included in the eLEE model nor the statistical tests presented in this Letter, the reported significance of the excess from the MiniBooNE neutrinomode data [34],4.69σ, translates to a 1σconfidence interval on the eLEE signal strength parameter of 10.21, illustrating how the MiniBooNE excess would appear. More rigorous comparisons of consistency with MiniBooNE in (MeV) ν Reconstructed E 0 500 1000 1500 2000 2500 Events/100 MeV 0 5 10 15 20 25 30 35 40 45 POT 20 10×MicroBooNE 6.369 BNB data, 338 Pred. uncertainty Others, 10.0 NC, 22.5 CC, 19.3 μ ν CC, 333.1 e ν eLEE Model (x=1), 37.0 FIG. 3. Reconstructed neutrino energy for inclusive νecandidate events in the Wire-Cell based analysis. The predicted event sample is dominated by νeevents intrinsic to the beam (green) while the other background contributions are described in the legend. The constrained predictions are shown both with (red) and without (black) a model of the MiniBooNE low energy excess included (further detail in text). Systematic uncertainties on the constrained prediction are shown as a shaded band. TABLE II. Top: Observed and predicted νecandidates in the signal-enhanced neutrino energy range predefined by each analysis prior to unblinding, in the absence (x¼0) or presence (x¼1) of a MiniBooNE-like νeevent excess. This energy range is a subset of the full fit range, also chosen prior to unblinding. Predicted events include the alternate-channel constraints of each analysis, and include statistical (following the CNP formalism) and constrained systematic uncertainties. Bottom: Frequentist-derived pvalues of the data observations compared to the prediction assuming no excess, pðχ2 x¼0Þ, and under a simple hypothesis test comparing an excess to no excess, pðΔχ2¼χ2 x¼0−χ2 x¼1<obsÞ, assuming the eLEE model (x¼1Þ. Also quoted are the 1σand 2σconfidence intervals for extracted signal strength xover the full fit range and the expected 2σupper endpoint of the interval on x assuming no excess. Signal-enhanced region comparison 1e1pCCQE 1eNp0π1e0p0π1eX Eν(MeV) 200–500 150–650 150–650 0–600 Predicted, no eLEE 8.83.030.46.119.05.369.69.4 Predicted, w/eLEE 18.54.439.06.822.35.7 104 12 Observed 6 21 27 56 Final fit results 1e1pCCQE 1eNp0π1e0p0π1eX Eν(MeV) 200–1200 150–1550 150–1550 0–2500 pðχ2 x¼0Þ1.4×10−20.18 0.13 0.85 pðΔχ2<obsÞ, w/eLEE 1.6×10−42.1×10−20.93 9.0×10−5 xobserved, 1σ[0.00,0.08] [0.00,0.41] [1.91,8.10] [0.00,0.22] xobserved, 2σ[0.00,0.38] [0.00,1.06] [0.77,24.3] [0.00,0.51] xexpected upper limit, 2σ0.98 1.44 4.64 0.56 PHYSICAL REVIEW LETTERS 128, 241801 (2022) 241801-6 the future will need to consider correlated uncertainties in the neutrino flux and cross section models, as well as additional kinematic measurements. Prior to unblinding of the data, each analysis defined a signal-enhanced low-energy region and determined the predicted number of events with and without the excess model in that region. Those predictions and the observed number of events are shown in Table II (top) and as ratios relative to the x¼0prediction in Fig. 4. Each analysis performs two statistical analyses to test the signal hypothesis. First, a simple hypothesis test uses Δχ2 CNP ¼χ2 x¼0−χ2 x¼1as a test statistic, comparing the observations to a frequentist Δχ2 CNP distribution derived from model simulations assuming x¼0and x¼1. The p values corresponding to Δχ2 CNP being less than the observed value assuming an eLEE (no eLEE) signal is 1.6×10−4 (0.02), 0.021 (0.29), 0.93 (0.98), and 9.0×10−5ð0.33Þin the 1e1pCCQE, 1eNp0π,1e0p0π, and 1eX selections, respectively. Each selection shows a strong preference for the absence of an electronlike MiniBooNE signal, with the exception of the 1e0p0πselection, driven by a data excess in the lowest energy bins, which also contain the highest contributions from non-νebackgrounds. Second, each analysis performs a nested hypothesis test where the eLEE signal strength xis varied, with a lower bound constraint at x¼0. Each analysis independently finds a best-fit signal strength, xmin, by minimizing χ2 CNP. Following this, a test statistic defined as Δχ2ðxÞ¼ χ2 CNPðxÞ−χ2 CNPðxminÞcan be constructed for varying hypothetical signal strengths. A Feldman-Cousins method [77] is used to construct confidence intervals around the best-fit signal strength, which are shown in Table II (bottom) and Fig. 5. Consistent with the observed deficit of events at low reconstructed energies, the 1e1pCCQE, 1eNp0π, and 1eX selections each find a best fit signal strength of x¼0, corresponding to the absence of an observed event excess, with 2σupper bounds at x<0.38,<1.06, and <0.51, respectively. The expected 2σupper bounds for these selections, assuming no signal, are shown in Table II. Consistent with the fact that in most analyses the observed number of events is less than the predicted number in the low energy regions, the measured upper endpoints of the 2σinterval are lower than expected. The best-fit signal strength for the 1e0p0πselection is x¼4.0, but with a wide confidence interval due to the low sensitivity of this channel. The best-fit signal strength for the 1eNp0πand 1e0p0πchannels combined is x¼0.36, with x<1.86 at the 2σconfidence level (and an expected upper bound where there is no signal at x<1.37), with more details in [62]. Conclusions.—The MicroBooNE experiment has performed a set of inclusive and exclusive searches for νeCC events using 7×1020 POT of Fermilab BNB neutrinomode data, about half of the collected dataset, with each analysis considering a hypothesis for the nature of the MiniBooNE low-energy excess. This work and Ref. [52] represent the first detailed study of this excess, noting that future MicroBooNE and short-baseline neutrino [78] measurements will continue to scrutinize the MiniBooNE results. The independent MicroBooNE search approaches have been led by distinct groups with each using a different fully automated event reconstruction software and common FIG. 5. Result of best-fit eLEE signal strength (x) in each analysis (black), along with the 1 and 2σconfidence intervals (solid and dashed lines, respectively). The expected 2σupper bound for each analysis, assuming no eLEE signal, is also shown (red). Signal strength values approximated from the MiniBooNE statistical and systematic errors (at 1σ) are shown for comparison (blue). Note that the vertical scale is presented as linear from x¼0to x¼2, while in logarithmic scale beyond that. FIG. 4. Ratio of observed to predicted νecandidate events— assuming no eLEE—in each analysis’s signal-enhanced neutrino energy range (see Table II, left) with the relative contributions shown from non-νebackgrounds (light blue) and intrinsic νe’s (green). Statistical errors are shown on the observations (black), while systematic errors are shown around the prediction (gray). The expected ratio assuming the MiniBooNE-like eLEE signal model with its median signal strength is also shown (red). PHYSICAL REVIEW LETTERS 128, 241801 (2022) 241801-7 data-blindness scheme. All results reported here are unchanged since data unblinding. Afforded by the capabilities of the LArTPC technology to image various leptonic and hadronic final states, the searches all feature excellent signal identification and background rejection. In addition, the analyses use datadriven νeestimates constrained by high-statistics samples of π0and νμCC events. The expected event rate is dominated by intrinsic νeCC events originating from the beamline, rather than background events involving photons. Despite the near-surface location, cosmic rays represent a subdominant and usually negligible contribution to the backgrounds. No excess of low-energy νecandidates is observed, and the mutually compatible, statistics-limited measurements are either consistent with or modestly lower than the predictions for all νeevent classes, including inclusive and exclusive hadronic final states, and across all energies. With the exception of the 1e0p0πselection which is the least sensitive to a simple model of the MiniBooNE lowenergy excess, MicroBooNE rejects the hypothesis that νe CC interactions are fully responsible for that excess (x¼1) at >97% CL for both exclusive (1e1pCCQE, 1eNp0π) and inclusive (1eX) event classes. Additionally, MicroBooNE disfavors generic νeinteractions as the primary contributor to the excess, with a 1σ(2σ) upper limit on the inclusive νeCC contribution to the excess of 22% (51%). While the MiniBooNE excess remains unexplained, our sensitive measurements are so far inconsistent with a νeinterpretation of the excess. This document was prepared by the MicroBooNE Collaboration using the resources of the Fermi National Accelerator Laboratory (Fermilab), a U.S. Department of Energy, Office of Science, HEP User Facility. Fermilab is managed by Fermi Research Alliance, LLC (FRA), acting under Contract No. DEAC02-07CH11359. MicroBooNE is supported by the following: the U.S. Department of Energy, Office of Science, Offices of High Energy Physics and Nuclear Physics; the U.S. National Science Foundation; the Swiss National Science Foundation; the Science and Technology Facilities Council (STFC), part of the United Kingdom Research and Innovation; the Royal Society (United Kingdom); and The European Unions Horizon 2020 Marie Sklodowska-Curie Actions. Additional support for the laser calibration system and cosmic-ray tagger was provided by the Albert Einstein Center for Fundamental Physics, Bern, Switzerland. We also acknowledge the contributions of technical and scientific staff to the design, construction, and operation of the MicroBooNE detector as well as the contributions of past collaborators to the development of MicroBooNE analyses, without whom this work would not have been possible. *[email protected] [1] P. Abratenko et al. (MicroBooNE Collaboration), Phys. Rev. Lett. 128, 151801 (2022). [2] P. 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