Observation of an ultra-high-energy cosmic neutrino with KM3NeT

376 | Nature | Vol 638 | 13 February 2025 Article Observation of an ultra-high-energy cosmic neutrino with KM3NeT The KM3NeT Collaboration* ✉ The detection of cosmic neutrinos with energies above a teraelectronvolt (TeV) offers a unique exploration into astrophysical phenomena1–3. Electrically neutral and interacting only by means of the weak interaction, neutrinos are not deflected by magnetic fields and are rarely absorbed by interstellar matter: their direction indicates that their cosmic origin might be from the farthest reaches of the Universe. High-energy neutrinos can be produced when ultra-relativistic cosmic-ray protons or nuclei interact with other matter or photons, and their observation could be a signature of these processes. Here we report an exceptionally high-energy event observed by KM3NeT, the deep-sea neutrino telescope in the Mediterranean Sea4, which we associate with a cosmic neutrino detection. We detect a muon with an estimated energy of 120−60 +110 petaelectronvolts (PeV). In light of its enormous energy and near-horizontal direction, the muon most probably originated from the interaction of a neutrino of even higher energy in the vicinity of the detector. The cosmic neutrino energy spectrum measured up to now5–7 falls steeply with energy. However, the energy of this event is much larger than that of any neutrino detected so far. This suggests that the neutrino may have originated in a different cosmic accelerator than the lower-energy neutrinos, or this may be the first detection of a cosmogenic neutrino8, resultingfrom the interactions of ultra-high-energy cosmic rays with background photons in the Universe. Cosmic neutrinos may be produced either in the vicinity of the cosmicray source or along the cosmic-ray propagation path, leading to the production of secondary unstable particles, which subsequently decay into neutrinos. Cosmic rays interacting in the Earth’s atmosphere produce atmospheric neutrinos, which form an experimental background to cosmic neutrinos. To detect cosmic neutrinos, very-large-volume neutrino observatories monitor natural bodies of water or ice for the Cherenkov light induced by the passage of the charged particles that result from neutrino interactions in or near the detector. The KM3NeT research infrastructure comprises two detector arrays of optical sensors deep in the Mediterranean Sea 4 . The ARCA detector is located offshore Portopalo di Capo Passero, Sicily, Italy, at a depth of about 3,450 m and connected by means of an electro-optical cable to the shore station of the INFN, Laboratori Nazionali del Sud (LNS). The geometry of ARCA is optimized for the study of high-energy cosmic neutrinos. The ORCA detector is located at a depth of about 2,450 m, offshore Toulon, France, and is optimized for the study of neutrino oscillations. Both detectors are under construction but already operational. Once completed, they will comprise 345 (230 for ARCA and 115 for ORCA) vertical detection lines, each holding 18 optical modules. Each module hosts 31 3-inch photomultiplier tubes (PMTs) pointing in all directions and ensuring 4π coverage 9 . Both detectors can identify all flavours of neutrino interactions: those producing long-lived muons, denominated ‘tracks’, and those producing electromagnetic and hadronic cascades at the neutrino interaction vertex, denominated ‘showers’. Of interest in this article are neutrino interactions that produce high-energy muons, which can travel several kilometres in seawater before being absorbed. These muons lose energy as they propagate mainly because of stochastic radiative processes such as bremsstrahlung, pair production and photonuclear reactions. The average energy loss per unit path length is proportional to the muon energy. Electromagnetic cascades arise from these stochastic energy losses; the number of charged particles that produce Cherenkov radiation in the cascades is proportional to the amount of energy lost by the muon in the process. The recorded time of arrival and time-over-threshold of the signals on the PMTs (denoted as ‘hits’) are used to reconstruct the muon direction and energy. Although atmospheric neutrinos are more abundant at lower energies (≈TeV), cosmic neutrinos should become dominant at energies above 100 TeV. The neutrino energy is thus a crucial parameter for establishing a cosmic origin. The IceCube Collaboration announced the discovery of PeV cosmic neutrinos in 2013 (ref. 10). The most energetic neutrinos reported so far are a 6.05 ± 0.72 PeV electron antineutrino observed at the energy of the Glashow resonance11 and a muon neutrino above 10 PeV from the observation of a 4.4-PeV muon5. The neutrino event KM3-230213A An extremely high-energy muon traversing the ARCA detector was observed on 13 February 2023 at 01:16:47 UTC. This event is referred to here as KM3-230213A. At that time, 21 detection lines were in operation. https://doi.org/10.1038/s41586-024-08543-1 Received: 19 August 2024 Accepted: 18 December 2024 Published online: 12 February 2025 Open access Check for updates *A list of authors and their affiliations appears at the end of the paper. ✉e-mail: [email protected] Nature | Vol 638 | 13 February 2025 | 377 The detector was in this configuration from 23 September 2022 until 11 September 2023, when seven further lines were installed. After removing data acquired in the detector commissioning phase and during detector calibration periods, 287.4 days of data taking were selected for analysis with this configuration. During this period, about 110 million events were triggered and KM3-230213A is the highest-energy event observed. KM3-230213A is visualized in Fig.1. A total of 28,086 hits were registered by the 21 detection lines. Owing to the large amount of detected light, the PMTs closest to the muon trajectory are saturated. As expected for very-high-energy muons, at least three large showers, probably because of energy-loss processes, are observed along the track (more details are provided in theSupplementary Materials). The muon trajectory is reconstructed from the measured times and positions of the first hits recorded on the PMTs, using a maximumlikelihood algorithm, described in Methods. KM3-230213A is the event with the best track log-likelihood among all those collected in this detector configuration, indicative of a highly relativistic muon travelling several hundreds of metres through the detector. The direction of KM3230213A is reconstructed as near-horizontal, originating0.6° above the horizon at an azimuth of 259.8°(azimuth angles increase clockwise, with north at 0°). The uncertainty on the direction is estimated to be 1.5° (68% confidence level), dominated by the present systematic uncertainty on the absolute orientation of the detector. The origin of this uncertainty is described in Methods. A dedicated sea campaign N 100 m N 100 m 1,800 1,600 1,400 1,200 1,000 800 600 400 200 0 Time (ns) 23 24 25 15 10 11 16 20 26 27 22 12 5 32 28 23 24 20 30 25 19 14 15 13 9 10 11 12 5 16 21 26 22 27 28 32 21 13 19 14 9 30 a b Fig. 1 | Views of the event. a, Side and top views of the event. The reconstructed trajectory of the muon is shown as a red line, along with an artist’s representation of the Cherenkov light cone. The hits of individual PMTs are represented by spheres stacked along the direction of the PMT orientations. Only the first five hits on each PMT are shown. As indicated in the legend, the spheres are coloured according to the detection time relative to the first triggered hit. The size of the spheres is proportional to the number of photons detected by the corresponding PMT. The locations of the secondary cascades, discussed in theSupplementary Material, are indicated by the black spheres along the muon trajectory. The north direction is indicated by a red arrow. A 100-m scale and the Eiffel Tower (330 m height, 125 m base width) are shown for size comparison. b, Zoomed-in view of the optical modules that are close to the first two observed secondary showers in the event. Here light-blue spheres represent hits that arrive within −5 to 25 ns of the expected Cherenkov arrival times. 378 | Nature | Vol 638 | 13 February 2025 Article is planned in the future to improve the knowledge of the positions of the detector elements on the seafloor; a recalibration of all data will then be performed and will allow us to approach the intrinsic statistical uncertainty on the muon direction of 0.12° (median, as described in Methods). The muon energy at the detector is estimated by counting the number of PMTs that participate in the triggering of the event, trig PMT N. This quantity is robust against limitations of the detector simulations, as described in Methods, and against contamination from the optical backgrounds in seawater. The observed number of triggered PMTs for KM3-230213A is  N=3,6 72 trig PMT , corresponding to about 35% of the active PMTs in the detector at the time of the event. This is much larger than for any other neutrino-induced event observed so far in the detector. Distributions of N trig PMT as a function of the muon energy, for modelled muons arriving from the same position and direction of KM3-230213A, are obtained from Monte Carlo simulations. These are used to build frequentist confidence intervals12,13 on the true muon energy, as described in Methods. Systematic uncertainties are included in the estimation by varying the simulated optical module efficiencies, and the scattering and absorption length of light, with respect to the nominal values. The trig PMT N distributions for simulations of 10-PeV, 100-PeV and 1,000-PeV muons are shown in Fig.2. The estimated muon energy is 120 PeV −60 +110 , with a 90% confidence level interval of 35–380 PeV. Uncertainties on the muon energy estimate are dominated by the knowledge of the absorption length of light in the seawater. The considered range of variations for photon absorption (±10%) is derived from the observed variations of the water transparency in dedicated measurements14. Several studies in recent data have confirmed this. The number of first hits with small residuals (that is, compatible with a direct photon path from the track to the PMT) has been studied in KM3-230213A. At large distances, this number is sensitive to the absorption length. Data were found to be in the ±10% range obtained from simulations with modified absorption. A similar study was carried out using downgoing atmospheric muons, which also confirmed that deviations from nominal are at most on the order of 10%. Finally, the absorption can be determined from the observed rate of photons from radioactive 40K in the seawater, which scales linearly with the absorption length. A first-principles computation 15 using the nominal absorption model predicts 5.1 ± 0.6 kHz, for which the uncertainty comes from the PMT efficiency and the number of photons created in the decay. The observed count rate is 5.6 kHz after accounting for dark noise and afterpulses, which is within the assigned 10% uncertainty range. Figure3 is an illustration of the position of KM3-230213A in the ( Ntrig PMT , cos(zenith angle)) phase space. Simulated Monte Carlo events are shown in Fig.3a, with the expected annual rates of atmospheric muons 16 and cosmic neutrinos 5 in ARCA. The distribution for the ARCA data is shown in Fig.3b, also highlighting KM3-230213A. Events are selected choosing well-reconstructed tracks, as defined on the basis of the observed track length in the detector (track length larger than 250 m) and the track reconstruction likelihood: log-likelihood ratio larger than 500, selecting 0.02% of all reconstructed atmospheric muon and neutrino tracks and 2% of the cosmic tracks assuming the flux from ref. 5. Given the reconstructed energy and direction, an upper limit on the background of atmospheric muons is estimated using dedicated simulations, as described in Methods. A muon with the observed direction would have traversed about 300 km water-equivalent of material, which exceeds the maximal range of any atmospheric muon (≤60 km water-equivalent for 100-EeV muons). The upper limit on the muon contamination at 100 PeV, considering an error on the zenith angle estimate as large as 2°, is 10 −10 events per year. This number becomes on the order of 10 −9 events per year if the muon energy is instead 10 PeV. In the very unlikely scenario that the detector is misaligned and the true zenith angle would deviate by 5σ from nominal (that is, arrival direction 5.6° above the horizon), muons would need to travel through 28 km water-equivalent and the upper limit on the rate becomes 10−4 muons per year and 10 −3 for muon bundles in which several parallel muons from the same cosmic-ray air shower could reach the detector. Atmospheric neutrinos could reach the detector, but their number decreases substantially above PeV energies. The expected rate of atmospheric neutrinos above 100 PeV is on the order of (1–5) × 10 −5 events per year, dominated by the prompt atmospheric component owing to the decay of short-lived hadrons from cosmic-ray interactions in the atmosphere. The probability that KM3-230213A is of cosmic origin is much greater than any hypothesis involving an atmospheric origin, and various estimations are provided in Methods and Supplementary Materials. Beyond Standard Model hypotheses on its origin have not been investigated here. The measured muon energy serves as a lower limit on the incoming neutrino energy. Given the estimated muon energy and its uncertainty, the median neutrino energy that produces such muons in the simulations of the ARCA detector is 220 PeV; 68% (90%) of simulated events from the whole sky fall in the 110–790 PeV (72 PeV–2.6 EeV) energy range, under the assumption that the incoming neutrino energy spectrum is E ∝ν −2 . An isotropic flux of neutrinos at ultra-high energies would give rise to events detected near the horizon: downgoing neutrinos are hidden in an overwhelming background of atmospheric muons, whereas the upgoing neutrino flux is severely suppressed, because neutrinos of such large energies would interact in the Earth. The arrival direction of KM3-230213A matches this scenario. Celestial origin The equatorial coordinates (J2000) and the detection time of KM3230213A are: RA = 94.3°, dec. = −7.8°, MJD = 59988.0533299. The different containment radii are: R(50%) = 1.2°, R(68%) = 1.5°, R(90%) = 2.2° and R(99%) = 3.0°, dominated by the systematic uncertainty on the absolute orientation of the detector (see Methods). The celestial position of KM3-230213A is shown in Fig.4, together with the different error region 2,000 4,000 6,000 Number of triggered PMTs 0 0.05 0.10 0.15 Fraction of Monte Carlo events Muon energy 10 PeV 100 PeV 1,000 PeV KM3-230213A Fig. 2 | Number of PMTs in the event. The normalized distributions of the number of PMTs participating in the triggering of the event for simulated muon energies of 10, 100 and 1,000 PeV. The vertical dashed line indicates the observed value in KM3-230213A, N =3,6 72 trig PMT . The dashed histograms represent the distributions from the nominal simulations, whereas, in the filled histograms, systematic uncertainties are included by weighting the simulations according to a normal distribution, centred at the nominal value of the nuisance parameter and with a ±10% uncertainty. At the highest energy, the distributions seem to be truncated around N=6, 000 trig PMT because the track crosses the detector in its periphery. Nature | Vol 638 | 13 February 2025 | 379 contours. Searches were performed for a potential source counterpart within a 3° radius around the event coordinates with publicly available multiwavelength data. Four hypotheses were tested: galactic, local Universe, transient and extragalactic origin. As the direction of the event is compatible with the extension of the galactic interstellar medium (about 10° above and below the galactic plane), galactic counterpart was searched for in high-energy (4FGL-DR4 (ref. 17)) and very-high-energy (TeVCat 18 ) gamma-ray catalogues, as well as in the 3HWC survey data 19 . Despite the presence of the Orion molecular clouds in the error region, no catalogued source was found in the 99% error region. The direction of the event was cross-matched with the MANGROVE catalogue20 for distances up to 100 Mpc to explore a local origin: 40 galaxies were found. For each galaxy, optical transient sources were searched for in the ZTF public stream in a ±6-month time window, using the FINK broker21. No transient source was identified. Also, no coincident detection of transient objects (such as gamma-ray bursts, tidal-disruption events, supernovae) was found in the GCN notices and circulars (https://gcn.nasa.gov/), in the Astronomer’s Telegram (https://astronomerstelegram.org/) and in the Transient Name Server (https://www.wis-tns.org/). Extragalactic neutrino sources should be dominated by active galactic nuclei, and blazars are of particular interest considering the very-high energy of KM3-230213A. To compile a census of potential blazar counterparts within the 99% confidence region of KM3-230213A, archival multiwavelength data were also explored. The following catalogues were cross-matched to investigate a possible blazar counterpart: the 4FGL-DR4 Fermi-LAT gamma-ray catalogue17, the first eROSITA X-ray catalogue22, the Wide-field Infrared Survey Explorer (WISE) optical catalogue 23 , the RFC 2024b (https://astrogeo.org/rfc/) and NRAO VLA Sky Survey (NVSS) 24 radio catalogues and Roma-BZCAT 25 . Four different strategies were pursued, leading to a total of 12 objects. The selection criteria are described in Methods, together with the properties of these sources. The celestial positions of the selected sources are shown in Fig.4. Given the large number of blazars in the sky, none of these associations can be considered compelling so far, and further investigations will be needed. Given that a hypothetical astrophysical source associated with KM3230213A may have also produced lower-energy neutrinos, data from the ARCA and ORCA detectors, as well as public data from the ANTARES and IceCube detectors, were checked for the presence of a neutrino signal compatible with a point-like source hypothesis in the vicinity of KM3-230213A. Details on the datasets, search approaches and results are given in Methods. The largest excess was found in the IceCube data at a distance of 2.4° from KM3-230213A with a pre-trial P-value of 1.6 × 10−4 and a post-trial P-value of 0.07. No significant excess was 1% 5% KM3-230213A 100 101 102 103 Number of data events –1.00 –0.75 –0.50 –0.25 0 0.25 0.50 0.75 1.00 cos(zenith angle) –1.00 –0.75 –0.50 –0.25 0 0.25 0.50 0.75 1.00 cos(zenith angle) 2.00 2.25 2.50 2.75 3.00 3.25 3.50 3.75 4.00 log10(number of triggered PMTs) 1% 5% 1% 5% KM3-230213A 0 0.002 0.004 0.006 0.008 0.010 0.012 Yearly rate of cosmic neutrinos 100 101 102 103 Yearly rate of atmospheric muons a 2.00 2.25 2.50 2.75 3.00 3.25 3.50 3.75 4.00 log10(number of triggered PMTs) b Fig. 3 | Background rates. a, Expected yearly rate of atmospheric muons and cosmic neutrinos (according to the best-fit flux of ref. 5) in ARCA per bin of Ntrig PMT and cos(zenith angle). The solid (dashed) lines mark the boundary of the phase space outside which 5% (1%) of the muon and neutrino distributions are contained. KM3-230213A is shown by the cross. b, Number of events collected in the ARCA detector over the 287 days of data taking with 21 detection lines, with the same selection cuts. Two upgoing, lower-energy events are visible as well as KM3-230213A, which are candidate neutrino events, subject to future analysis. 9192939495969798 RA J2000 (°) –11 –10 –9 –8 –7 –6 –5 –4 dec. J2000 (°) #1 #2 #3 #4 #5 #6 #7 #8 #9 #10 #11 #12 KM3-230213A R(68%) R(90%) R(99%) 5BZCAT X-ray + radio + infrared VLBI Gamma ray Fig. 4 | Sky map in the direction of KM3-230213A. KM3-230213A is indicated by the red star, with the error regions within R(68%), R(90%) and R(99%) shown as dotted, dashed and solid contours, respectively. The directions of the selected source candidates are shown as coloured markers, whose colours and marker type indicate the criterion according to which the source was selected. The sources are numbered according to their proximity to KM3-230213A, as reported in Methods. 380 | Nature | Vol 638 | 13 February 2025 Article observed at the coordinates of KM3-230213A and 90% confidence level upper limits on the one-flavour neutrino flux normalization at 1 GeV, Φνν+ 1GeV , assuming a neutrino spectrum of ΦEΦ E()=((GeV)) νν νν++ 1GeV −2 , were set and are reported in Methods. The most stringent limit on the point-source origin is 1.2 × 10 −9 GeV −1 cm −2 s −1 . Although these searches are also sensitive to very-high-energy events, the signal for an E−2 spectrum is expected in the TeV–PeV range and the reported limits are therefore applicable in this area. Cosmic neutrino flux To associate a flux to the event, the exposure of the detector for veryhigh-quality and high-energy tracks is computed through simulations. The exposure corresponds to selection criteria that require a good track-reconstruction likelihood (log-likelihood ratio larger than 500), a long track length within the detector (larger than 250 m) and N>1 ,500 trig PMT . Considering the central (90%) 72 PeV–2.6 EeV energy range, the steady isotropic flux that would produce one event is EΦE()=5.8 ×10GeV cmssr , 2−3.7 +10.1−8−2−1−1 for which the confidence intervals are computed according to ref. 26. The 95% and 99.7% confidence level intervals are [0.30–29.8] and [0.02–47.7] × 10−8 GeV cm−2 s−1 sr−1, respectively. This represents the KM3NeT standalone flux measurement in the 335 days of livetime of ARCA with 19 and 21 detection lines. In Fig.5, the flux measurement is compared with measured and predicted neutrino fluxes and limits. The KM3NeT standalone flux measurement exceeds present limits from IceCube27 and Auger28. A possible interpretation is that the KM3NeT event is an upward fluctuation. In such a scenario, described in Methods, one event such as KM3230213A would be expected in 70 years of observation with this detector configuration, and the event is an upward fluctuation at the level of 2.2σ. The expected event rates in ARCA for various extrapolations of the flux measured by IceCube are discussed in theSupplementary Material. Considering extrapolations of the power-law fit of the IceCube measurements, these would yield at most 0.12 events in the 335 days of analysed KM3NeT data with 19 and 21 detection lines after the selection for track events described above. The observation of KM3-230213A, marginally consistent with such expectation, may hint at the emergence of a new component in the flux. A viable alternative hypothesis is cosmogenic neutrino production8,29,30, in which neutrinos are generated by the interaction of cosmic rays with extragalactic background light or the cosmic microwave background. The expected number of cosmogenic events in the selected data varies between 1.5 × 10−3 (ref. 31) and 0.47 (ref. 32), depending on the assumed injection spectrum and cosmic-ray mass composition, as well as the cosmological evolution of sources31–40. The envelope of a selection of cosmogenic models is shown as a grey-shaded band in Fig.5. Other scenarios of diffuse emission from neutrino production in the source environment are shown as the yellow-shaded band in Fig.5. Among these are transient emitters such as gamma-ray-bursts and tidal-disruption events34,39,41–44, low-luminosity BL Lacs45 and flat-spectrum radio quasars46. Overall, the detection of a muon neutrino with an energy greater than 100 PeV provides evidence for the existence of ultra-high-energy neutrinos in nature. The new multiPMT optical module design and the excellent optical properties of Mediterranean seawater have allowed the characterization of the neutrino interaction and have facilitated this breakthrough in neutrino astronomy. 1041051061071081091010 1011 Neutrino energy (GeV) 10–11 10–10 10–9 10–8 10–7 10 –6 ANTARES (2024) Auger (2022) Upper limits KM3-230213A IceCube fits NST (2022) HESE (2021) Glashow (2021) SPL 68% NST (2022) SPL 68% HESE (2021) Models Cosmogenic band Sources band E2 + (GeV cm–2 s–1 sr–1) 1f Φ IceCube/EHE (2018) Fig. 5 | Comparison with models and earlier measurements. Shown is the energy-squared per-flavour astrophysical flux derived from the observation of KM3-230213A with measurements and theoretical predictions, assuming equipartition (νe:νμ:ντ = 1:1:1). The blue cross corresponds to the flux needed to achieve one expected event after the track selection described in the text, in the central 90% neutrino energy range associated with KM3-230213A, illustrated with the horizontal span; the vertical bars represent the 1σ,2σ and 3σ Feldman–Cousins confidence intervals on this estimate. The purple and pink shaded regions represent the 68% confidence level contours of the IceCube single-power-law (SPL) fits (Northern Sky Tracks, NST5) and High-Energy Starting Events (HESE)7, respectively: the darker-shaded regions are the respective 90% central energy range at the best fit (dashed line), whereas the lighter-shaded regions are extrapolations to higher energies. The purple and pink crosses are the piece-wise fit from the same analyses, whereas the orange cross corresponds to the IceCube Glashow resonance event11. The dotted lines are upper limits from ANTARES (95% confidence level47), Pierre Auger (90% confidence level, for an E−2 neutrino spectrum28, corrected to convert from limits in half-decade to one-decade bins) and IceCube (90% confidence level, estimated assuming an E−1 neutrino spectrum in sliding one-decade bins27). The grey-shaded band comprises a variety of cosmogenic neutrino expectations following several models of cosmic-ray acceleration and propagation, whereas the yellow-shaded band comprises several scenarios of diffuse transient and variable extragalactic sources, both reported in theSupplementary Material. 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(ANTARES Collaboration), Constraints on the energy spectrum of the diffuse cosmic neutrino flux from the ANTARES neutrino telescope. J. Cosmol. Astropart. Phys. 08, 038 (2024). Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons AttributionNonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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Berbee26, V. Bertin5, S. Biagi27, M. Boettcher28, D. Bonanno27, A. B. Bouasla29, J. Boumaaza16, M. Bouta30, M. Bouwhuis26, C. Bozza7,31, R. M. Bozza7,8, H. Brânzaş32, F. Bretaudeau12, M. Breuhaus5, R. Bruijn26,33, J. Brunner5, R. Bruno1, E. Buis26,34, R. Buompane7,24, S. Buson35,36, J. Busto5, B. Caiffi11, D. Calvo6, A. Capone9,37, F. Carenini22,23, V. Carretero26,33, T. Cartraud17, P. Castaldi22,38, V. Cecchini6, S. Celli9,37, L. Cerisy5, M. Chabab39, A. Chen40, S. Cherubini27,41, T. Chiarusi22, M. Circella42, R. Cocimano27, J. A. B. Coelho17, A. Coleiro17, S. Colonges17, A. Condorelli7,8, R. Coniglione27, P. Coyle5, A. Creusot17, G. Cuttone27, A. D’Amico26, R. Dallier12, A. De Benedittis7, B. De Martino5, G. De Wasseige43, V. Decoene12, I. Del Rosso22,23, L. S. Di Mauro27, I. Di Palma9,37, A. F. Diaz44, D. Diego-Tortosa27, C. Distefano27, A. Domi45, C. Donzaud17, D. Dornic5, E. Drakopoulou46, D. Drouhin2,3, J.-G. Ducoin5, R. Dvornický21, T. Eberl45, E. Eckerová20,21, A. Eddymaoui16, T. van Eeden26, M. Eff17, D. van Eijk26, I. El Bojaddaini30, S. El Hedri17, V. Ellajosyula11,18, A. Enzenhöfer5, G. Ferrara1,27, M. D. Filipović47, F. Filippini23, D. Franciotti27, L. A. Fusco7,31, S. Gagliardini9,37, T. Gal45, J. García Méndez13, A. Garcia Soto6, C. Gatius Oliver26, N. Geißelbrecht45, E. Genton43, H. Ghaddari30, L. Gialanella7,24, B. K. Gibson25, E. Giorgio27, I. Goos17, P. Goswami17, S. R. Gozzini6, R. Gracia45, K. Graf45, C. Guidi11,18, B. Guillon19, M. Gutiérrez48, C. Haack45, H. van Haren49, A. Heijboer26, L. Hennig45, S. Henry5, J. J. Hernández-Rey6, W. Idrissi Ibnsalih7, A. Ilioni17, G. Illuminati23, D. Joly5, M. de Jong26,50, P. de Jong26,33, B. J. Jung26, P. Kalaczyński51,52, O. Kalekin45, N. Kamp14,15, U. F. Katz45, G. Kistauri53,54, C. Kopper45, A. Kouchner17,55, Y. Y. Kovalev56, V. Kueviakoe26, V. Kulikovskiy11, R. Kvatadze54, M. Labalme19, R. Lahmann45, M. Lamoureux43, S. Lancelin5, G. Larosa27, C. Lastoria19, J. Lazar43, A. Lazo6, S. Le Stum5, G. Lehaut19, V. Lemaitre43, E. Leonora1, N. Lessing6, G. Levi22,23, M. Lincetto36, M. Lindsey Clark17, F. Longhitano1, N. Lumb5, F. Magnani5, J. Majumdar26, L. Malerba11,18, F. Mamedov20, A. Manfreda7, M. Marconi11,18, A. Margiotta22,23, A. Marinelli7,8, C. Markou46, L. Martin12, F. Marzaioli7,24, M. Mastrodicasa9,37, S. Mastroianni7, J. Mauro43, G. Miele7,8, P. Migliozzi7, E. Migneco27, M. L. Mitsou7,24, C. M. Mollo7, M. Mongelli42, L. Morales-Gallegos7,24, A. Moussa30, I. Mozun Mateo19, R. Muller22, M. R. Musone7,24, M. Musumeci27, S. Navas48, A. Nayerhoda42, C. A. Nicolau9, 382 | Nature | Vol 638 | 13 February 2025 Article B. Nkosi40, B. Ó Fearraigh11, V. Oliviero7,8, A. Orlando27, E. Oukacha17, D. Paesani27, J. Palacios González6, G. Papalashvili42,53, C. Paries5, V. Parisi11,18, E. J. Pastor Gomez6, C. Pastore42, A. M. Păun32, G. E. Păvălaş32, S. Peña Martínez17, M. Perrin-Terrin5, V. Pestel19, R. Pestes17, L. Pfeiffer36, P. Piattelli27, A. Plavin56,57, C. Poirè7,31, V. Popa32,62, T. Pradier2, J. Prado6, S. Pulvirenti27, C. A. Quiroz-Rangel13, N. Randazzo1, S. Razzaque58, I. C. Rea7, D. Real6, G. Riccobene27, J. Robinson28, A. Romanov11,18,19, E. Ros56, A. Šaina6, F. Salesa Greus6, D. F. E. Samtleben26,50, A. Sánchez Losa6, S. Sanfilippo27, M. Sanguineti11,18, D. Santonocito27, P. Sapienza27, J. Schmelling26, J. Schnabel45, J. Schumann45, H. M. Schutte28, J. Seneca26, N. Sennan30, P. Sevle43, I. Sgura42, R. Shanidze53, A. Sharma17, Y. Shitov20, F. Šimkovic21, A. Simonelli7, A. Sinopoulou1, B. Spisso7, M. Spurio23,24, D. Stavropoulos46, I. Štekl20, M. Taiuti11,18, Y. Tayalati16,59, H. Thiersen28, S. Thoudam4, I. Tosta e Melo1,41, B. Trocmé17, V. Tsourapis46, A. Tudorache9,37, E. Tzamariudaki46, A. Ukleja60, A. Vacheret19, V. Valsecchi27, V. Van Elewyck17,55, G. Vannoye5, G. Vasileiadis61, F. Vazquez de Sola26, C. Verilhac17, A. Veutro9,37, S. Viola27, D. Vivolo7,24, A. van Vliet4, A. Y. Wen14,15, E. de Wolf26,33, I. Lhenry-Yvon17, S. Zavatarelli11, A. Zegarelli9,37, D. Zito27, J. D. Zornoza6, J. Zúñiga6 & N. Zywucka28 1INFN, Sezione di Catania (INFN-CT), Catania, Italy. 2Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France. 3Université de Haute Alsace, Mulhouse, France. 4Department of Physics, Khalifa University, Abu Dhabi, United Arab Emirates. 5Aix Marseille Université, CNRS/IN2P3, CPPM, Marseille, France. 6IFIC - Instituto de Física Corpuscular (CSIC - Universitat de València), Paterna, Spain. 7INFN, Sezione di Napoli, Complesso Universitario di Monte S. Angelo, Napoli, Italy. 8Università di Napoli “Federico II”, Dip. Scienze Fisiche “E. Pancini”, Complesso Universitario di Monte S. Angelo, Napoli, Italy. 9INFN, Sezione di Roma, Roma, Italy. 10Laboratori d’Aplicacions Bioacústiques, Centre Tecnològic de Vilanova i la Geltrú, Universitat Politècnica de Catalunya, Vilanova i la Geltrú, Spain. 11INFN, Sezione di Genova, Genova, Italy. 12Subatech, IMT Atlantique, IN2P3-CNRS, Nantes Université, Nantes, France. 13Instituto de Investigación para la Gestión Integrada delas Zonas Costeras, Universitat Politècnica de València, Gandia, Spain. 14Department of Physics, Harvard University, Cambridge, MA, USA. 15Laboratory for Particle Physics and Cosmology, Lyman Laboratory, Harvard University, Cambridge, MA, USA. 16Faculty of Sciences, University Mohammed V in Rabat, Rabat, Morocco. 17Astroparticule et Cosmologie, Université Paris Cité, CNRS, Paris, France. 18Università di Genova, Genova, Italy. 19LPC CAEN, Normandie Université, ENSICAEN, UNICAEN, CNRS/IN2P3, Caen, France. 20Institute of Experimental and Applied Physics, Czech Technical University in Prague, Prague, Czech Republic. 21Department of Nuclear Physics and Biophysics, Comenius University, Bratislava, Slovak Republic. 22INFN, Sezione di Bologna, Bologna, Italy. 23Dipartimento di Fisica e Astronomia, Università di Bologna, Bologna, Italy. 24Dipartimento di Matematica e Fisica, Università degli Studi della Campania “Luigi Vanvitelli”, Caserta, Italy. 25E. A. Milne Centre for Astrophysics, University of Hull, Hull, United Kingdom. 26Nikhef, National Institute for Subatomic Physics, Amsterdam, The Netherlands. 27INFN, Laboratori Nazionali del Sud (LNS), Catania, Italy. 28Centre for Space Research, North-West University, Potchefstroom, South Africa. 29Département de Physique, Faculté des Sciences, Laboratoire de Physique des Rayonnements, Université Badji Mokhtar-Annaba, Annaba, Algeria. 30Faculty of Sciences, University Mohammed I, Oujda, Morocco. 31Dipartimento di Fisica, Università di Salerno e INFN Gruppo Collegato di Salerno, Fisciano, Italy. 32Institute of Space Science (ISS), Măgurele, Romania. 33Institute of Physics/IHEF, University of Amsterdam, Amsterdam, The Netherlands. 34Technical Sciences, TNO, Delft, The Netherlands. 35Deutsches ElektronenSynchrotron (DESY), Zeuthen, Germany. 36Fakultät für Physik und Astronomie, Institut für Theoretische Physik und Astrophysik, Lehrstuhl für Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany. 37Dipartimento di Fisica, Università La Sapienza, Roma, Italy. 38Dipartimento di Ingegneria dell’Energia Elettrica e dell’Informazione “Guglielmo Marconi”, Università di Bologna, Cesena, Italy. 39Physics Department, Faculty of Science Semlalia, Cadi Ayyad University, Marrakech, Morocco. 40School of Physics, University of the Witwatersrand, Johannesburg, South Africa. 41Dipartimento di Fisica e Astronomia “Ettore Majorana”, Università di Catania (INFN-CT), Catania, Italy. 42INFN, Sezione di Bari, Bari, Italy. 43Centre for Cosmology, Particle Physics and Phenomenology, UCLouvain, Louvain-la-Neuve, Belgium. 44Department of Computer Engineering, Automation, and Robotics, CITIC, University of Granada, Granada, Spain. 45Erlangen Centre for Astroparticle Physics, Friedrich-AlexanderUniversität Erlangen-Nürnberg (FAU), Erlangen, Germany. 46Institute of Nuclear and Particle Physics, NCSR Demokritos, Athens, Greece. 47School of Computing, Engineering and Mathematics, Western Sydney University, Penrith, New South Wales, Australia. 48Departamento de Física Teórica y del Cosmos and CAFPE, University of Granada, Granada, Spain. 49Royal Netherlands Institute for Sea Research (NIOZ Texel), Den Burg, The Netherlands. 50Leiden Institute of Physics, Leiden University, Leiden, The Netherlands. 51AstroCeNT, Nicolaus Copernicus Astronomical Center, Polish Academy of Sciences, Warsaw, Poland. 52Center of Excellence in Artificial Intelligence, AGH University of Krakow, Krakow, Poland. 53Department of Physics, Tbilisi State University, Tbilisi, Georgia. 54Institute of Physics, University of Georgia, Tbilisi, Georgia. 55Institut Universitaire de France, Paris, France. 56Max-Planck-Institut für Radioastronomie, Bonn, Germany. 57Black Hole Initiative, Harvard University, Cambridge, MA, USA. 58Department Physics, University of Johannesburg, Auckland Park, South Africa. 59Institute of Applied Physics, Mohammed VI Polytechnic University, Ben Guerir, Morocco. 60National Centre for Nuclear Research, Warsaw, Poland. 61Laboratoire Univers et Particules de Montpellier, Montpellier, France. 62Deceased: V. Popa. Methods The detector The KM3NeT detectors 4 are three-dimensional arrays of photosensors installed at great depths in the Mediterranean Sea. The sensors detect the Cherenkov radiation induced in seawater by relativistic charged particles. They are housed in optical modules9, which are 44-cm-diameter pressure-resistant glass spheres, each with 31 3-inch PMTs. Each optical module contains data-acquisition electronics and calibration instrumentation. The modules of the ARCA detector, located at a depth of 3,450 m offshore Portopalo di Capo Passero, Sicily, in the Mediterranean Sea, are chained together in groups of 18, spaced by 36 m, along 700-m-long vertical detection lines anchored to the seabed and kept taut by the buoyancy of the optical modules and top buoys. An electro-optical cable runs along the detection lines, powering the optical modules and transporting data through optical fibres. Detection lines are placed on the seafloor with an average horizontal spacing of 95 m. At the time of the event, the ARCA detector consisted of 21 detection lines. The instrumented volume, that is, the smallest cylinder containing all optical modules, was about 0.15 km3. In its final configuration, the array will comprise 230 detection lines. The data-acquisition system is based on the ‘all-data-to-shore’ concept: all analogue signals from the PMTs above a certain tunable threshold are digitized offshore and all digital data are sent to shore, where they are processed in real time. The data contain the time stamp of the leading edge and the pulse length of the time-over-threshold signal from a discriminator, jointly referred to as ‘hit’. The time-over-threshold is proportional to the number of converted photoelectrons on the PMT. Although the linear behaviour holds relatively well for pulses up to a few tens of photoelectrons, above 30 photoelectrons, a saturation effect is observed, producing a flattening of the time-over-threshold measurements with increasing charge9. Trigger algorithms search for clusters of hits correlated in space and time. Local coincidences of hits are identified on each optical module within a 25-ns time window. Then, three different clustering algorithms are applied, assuming that hits come from light propagating according to a possible track or shower origin. A space-time coincidence between at least five hits on five modules within 250 m, under the assumption that light expands from a point-like source, constitute a cluster for the nominal shower trigger, allowing for a 25-ns delay to light propagation in water. A similar condition is applied for the track trigger, but this time considering that the light source is a track that moves in the detector at the speed of light in vacuum, and searching for hits in a cylinder of radius 120 m. A low-threshold shower trigger is also applied, requiring coincidences of eight hits on three modules within 110 m. When one or more clusters are found, all data from an Oμ(10)-s time window are recorded as an event for offline calibration and processing. Trig - gering criteria are designed to detect events at the lower-energy threshold. In the case of KM3-230213A, 3,659 individual (overlapping) trigger clusters were found in the time window and  N=3,6 72 trig PMT PMTs participated to form at least one of those trigger clusters. PMTs that have recorded hits but that do not participate in any trigger cluster are predominantly caused by optical backgrounds that are very far away and/ or not time-correlated with the physical event. It is for this reason that  Ntrig PMT is used as observable for the energy estimate. Data-quality criteria are applied to reject periods with detector instabilities from the analysis sample. Detector simulation High-energy neutrino events (100 GeV < Eν < 100 EeV) were simulated with gSeaGen v7.4.3 (refs. 48,49), using GENIE 50 to simulate the neutrino interaction by means of the HEDIS package 51,52 . The deepinelastic scattering model CSMS11 (ref. 53) was used. PROPOSAL 54 and TAUSIC 55 were used by gSeaGen to propagate muons and taus up to the detector. The accurate simulation of the light produced by a muon of a given energy is crucial to the muon energy estimate. KM3NeT uses proprietary code, which simulates the continuous and stochastic energy losses owing to bremsstrahlung, pair production, photonuclear interactions, delta rays and ionization, as well as the multiple Coulomb scattering and deep-inelastic scattering. Differential cross-sections for the main processes are extracted from refs. 56,57. The light produced by the muon and the secondary particles is simulated by sampling photon tables that contain the probability density functions of the arrival time of Cherenkov light on a PMT as a function of the distance from the emission point and the PMT orientation with respect to the particle 58 . For secondary particles, equivalent tabulated values for electromagnetic shower light are used. The amount of photons generated depends on the type and energy of the particle and has been adjusted according to Geant4 simulations59. The photon tables account for light absorption, scattering and chromatic dispersion. Absorption is modelled on the basis of insitu measurements 14 ; the scattering model for seawater accounts for pure-water scattering, following the Einstein and Smoluchowski description60,61, and particle scattering, accounting for the wavelength dependence using the Kopelevich parameterization62 and considering the Petzold data 63 for the angular dependence. The angular acceptance and average quantum efficiency of the PMTs are also accounted for in the tables, as derived from detailed simulations of the PMT and the structure of the optical module64, and from laboratory measurements65. The simulation of the stochastic energy losses has been cross-checked by comparing the total simulated amount of energy lost by the muon over a given distance with the same quantity computed using the PROPOSAL software 54 . Agreement at better than the 10% level was found over the whole energy range of interest. Moreover, PROPOSAL has also been used to check that varying the theoretical models used to describe energy losses 66–69 yields differences that are within the stochastic fluctuations of the energy-loss processes. Because no external data are available in this energy range to validate the simulation procedure, the particle-propagation and light-simulation code has also been compared with state-of-the-art Geant4-based59 simulation and with a custom GPU-based photon-tracking code (https:// github.com/PLEnuM-group/PhotonPropagation.jl) in which the same water model and detector response have been implemented in an independent way. The output of these simulations are in good agreement; when using the alternative simulations for the energy measurement, the result is within 10% of the nominal value. After light simulation, the readout is simulated. The conversion from photoelectrons on the cathode of the PMT to a time-over-threshold measurement and the transit-time distributions of the PMTs reproduce laboratory measurements65. The gain, gain spread and relative PMT efficiencies come from insitu measurements70. Afterpulses in the PMTs are at present not simulated. Optical background rates and the status of each PMT in the detector are simulated using the rates measured in the detector, following the run-by-run approach pioneered by the ANTARES Collaboration71,72. Subsequently, the simulated data are subjected to the same trigger and reconstruction algorithms that are applied to the data. Comparisons between data and Monte Carlo simulations are provided in Extended Data Fig.1 for a loose event selection in which the sample is dominated by atmospheric muons. Wrongly reconstructed atmospheric muons that appear as upgoing events in the zenith distribution are completely removed once the selection on the reconstruction log-likelihood is applied. Event reconstruction The directional reconstruction of the muon track is performed with the standard algorithm, which is based on the arrival time of the Cherenkov photons at the PMTs 73 . Under the hypothesis that a muon travelling in direction → d is at position → p 0 at time t 0 , the arrival time of the Cherenkov light at position → q is Article tt zc D θv=+/+ sin( ),(1 ) 0 C g −1 in which D is the distance of closest approach of the muon to → q and z is the distance the muon travels before emitting a photon under angle θC, →→ zd qp=⋅(− → )− D θ0tan C ; vg is the group velocity of light at a reference wavelength of 460 nm. The reconstruction algorithm maximizes the likelihood of the arrival time residuals L ∏ pr dϕθ=(,,,),(2 ) i iiii in which p denotes the probability density function of the arrival time residual ri, obtained from interpolated photon tables, at a distance di from the emission point. The angles ϕi and θi describe the orientation of the PMT with respect to the track direction. The photon tables are the same as those that have been described above for the simulation of light from the muon trajectory. They include the contribution of optical background. The algorithm uses only the first hit on each PMT, as they carry most of the information on the muon direction. As a result of this choice, the reconstruction is robust against PMT afterpulses and other details of modelling of later hits. For the likelihood maximization, only the first hits in a cylinder of radius 175 m and axis defined by the prefit direction are used. This is the standard setting, which was chosen as it optimizes the speed of the algorithm. In the case of KM3-230213A, there are hits outside this cylinder, but it was tested that including them alters the reconstructed track by less than the statistical uncertainty on the direction. To mitigate the effect of local minima on the likelihood function, the maximization is preceded by a prefit, scanning in 4π sr over assumed track directions. This procedure generates a set of starting points for the likelihood maximization. The track with the largest likelihood is retained. For ascertaining the quality of the events, the log-likelihood ratio, LLlog(/ ) b is used, with b L the likelihood computed for the case of only optical background hits. This quantity effectively quantifies the number of hits whose arrival time matches the expectation from the track hypothesis. Typical well-reconstructed muons have a value ≳50, with a tail of larger values resulting from well-reconstructed events with many hits. KM3-230213A has a log-likelihood ratio of 1,415.2, which is the highest value observed in the 21-line ARCA data. To illustrate the quality of the reconstruction, Extended Data Fig.2 presents the photon arrival time residuals, which represent the difference between the measured time and the expected time from the reconstructed muon trajectory hypothesis, shown here for the first hits on the PMTs. Many hits are compatible with the muon hypothesis with nanosecond accuracy, even for PMTs located far from the track. Hits arriving after the main peak are because of photons that have scattered in the water and/or that were emitted under some angle other than θC from the muon track. These contributions are accounted for in the reconstruction and the large log-likelihood ratio value reflects the agreement of these residuals with the detailed expectation. Pointing of the telescope The directional uncertainty on the event is dominated by uncertainty on the absolute orientation of the detector on Earth. Compasses and accelerometers in the optical modules allow for an estimation of their orientation. The detection lines move with the sea current, which can displace the top modules by O(10)m . The continuous monitoring of the optical module positions is therefore mandatory. For this purpose, a system of autonomous acoustic emitters is used, located in and up to 1 km outside the detector on the seabed74. The acoustic signals are recorded by piezoelectric sensors in the optical modules. A χ2 fit of the arrival times of the sound is used to determine the orientation and shape of the detection line as parameterized by a mechanical model. In this way, the relative positions of the optical modules can be determined to within 0.15 m. Acoustic signals are processed at 10-min intervals; the results of the fit are interpolated to provide the relative positions of the optical modules over time 75 . At the time of the event, the string tilts changed steadily by about 2° over a time span of 2 h, corresponding to less than 0.1° in 10 min. The uncertainty on the position of the detector elements owing to the interpolation of the acoustic data is thus negligible. The acoustic system measures distances between the optical modules and acoustic emitters but this does not constrain the absolute orientation of the telescope on Earth. During sea campaigns, the positions of the detection lines and acoustic emitters are measured. The emitter positions are used to determine the nominal absolute orientation. These data are at a present accurate to approximately 10 m. This is supported by comparisons with two bathymetry datasets (for the vertical positions) and internal cross-checks with the acoustic system (for the horizontal positions). The position uncertainty translates, after conservatively rounding the result, to an uncertainty of 1° on rotations of the detector around each of the three axes. An independent cross-check of the pointing was performed by means of a measurement of the directional deficit of atmospheric muons owing to the absorption of cosmic rays in the Moon, similar to ref. 76. This anti-signal of the Moon was studied in 335 days of data when the detector consisted of 19 and 21 detection lines. The Moon shadow signal was found at a significance of 3.2σ. In evaluating the Moon shadow for different assumed rotations around the vertical axis, in the range ±3° in steps of 0.25°, the largest significance was found for the nominal orientation. The corresponding uncertainty is evaluated by means of simulations to 0.24°. A comparison of detector-line depths determined with the acoustic system and the two bathymetry datasets yields further evidence that the system is aligned to within 1°. Propagating the 1° uncertainty to the celestial coordinates of the event yields a circular 68% confidence region on the sky with a radius of 1.5°. This uncertainty is the dominant source of (systematic) uncertainty in the determination of the celestial coordinates of KM3-230213A. Simulations of muons in the same location as KM3-230213A were performed at energies from 1 to 1,000 PeV to evaluate the statistical uncertainty on the direction estimation. At 100 PeV, 50% (90%) of the muons are reconstructed within 0.12° (0.28°) from the nominal direction. The azimuthal uncertainty increases with energy, so that, for an energy of 500 PeV, 50% (90%) of the muons were reconstructed within 0.17° (0.38°). These uncertainties are negligible with respect to the 1.5° 68% confidence region and are mentioned here only to indicate the future potential of a fully aligned detector. We foresee upgrading the detector in the next sea campaign by using new acoustic emitters whose absolute position will be measured with <1-m accuracy in each direction. This, as well as the extra collected data for the Moon shadow analysis, will allow for a recalibration of the data and a more precise determination of the celestial origin of KM3-230213A. Energy estimate The energy of a muon above a few TeV can be estimated by measuring its energy loss. Radiative energy losses produce showers of charged particles along the muon trajectory that induce excess Cherenkov photons along the track. The photons arrive on the PMTs very close in time, producing a large number of photoelectrons that translate into hits with a large time-over-threshold. In the case of KM3-230213A, the large number of photons induced by the muon saturates most of the PMTs within about 100 m from the track, and hits are recorded even up to a distance of 300 m. This saturation effect is visible in more than 25% of the PMTs that participated in the triggering of KM3-230213A Extended Data Fig. 4 | Illustration of the topography. Using bathymetric data from EMODnet90, a sectional view along the incoming direction and position of the event is shown, with the sea shown in blue and the seabed and the rock beneath in brown. The x axis indicates the total distance from the ARCA site and the y axis and grey lines represent the depth with respect to the sea level. The shaded area shows the effect of a variation of ±1.5° in the direction reconstruction, corresponding to the 68% error region from the evaluation of systematic uncertainties. Article Extended Data Fig. 5 | All-flavour sky-averaged effective area for KM3NeT/ARCA. The area in the 21 detection line configuration is shown as a function of neutrino energy. This effective area is computed after applying the event selection described in the text and is averaged between neutrinos and antineutrinos. Extended Data Table 1 | Datasets and methods used in the searches for a cosmic point-like neutrino source in the direction of KM3-230213A. Refs. 91–93 For each investigated dataset, the corresponding detector, covered period, livetime, type of data, analysis method and radius of the circular inspected region centred at the location of KM3-230213A are reported. The ‘Type of data’ column refers to fully calibrated data (offline) or using preliminary calibrations (online). KM3-230213A was removed from the ARCA6-21 dataset. The ANTARES dataset is taken from https://antares.in2p3.fr/data/data-set-for-the-2007-2017-antares-search-for-cosmic-neutrino-point-sources/; it is analysed with a method from ref. 94, with the difference that no energy information is considered in the likelihood and that a two-dimensional Gaussian is used to describe the signal spatial distribution. For the IceCube data, the IceCubePy framework95 is used, without including the energy information in the likelihood. Article Extended Data Table 2 | Results of the searches for a cosmic point-like neutrino source in the direction of KM3-230213A For each investigated dataset, the corresponding results are shown in terms of the number of signal events (either observed in the ON region or fitted by the likelihood maximization), pre-trial P-value and 90% confidence level upper limit on the one-flavour neutrino flux normalization at 1 GeV, + Φνν 1GeV , assuming a neutrino spectrum of = ++ − ΦEΦE() ((GeV)) νν νν 1GeV 2 . In the case of likelihood scan (ARCA6-21, ANTARES, IceCube), results are given both for the location of KM3-230213A (RA = 94.3°, dec. = −7.8°) and for the most significant direction in the inspected region, together with equatorial coordinates and distance from the event. For the most significant direction, the post-trial P-value (P-value) is also provided. The fifth row reports the combined limit of the two ORCA analyses obtained with the dedicated framework MOMENTA96. Extended Data Table 3 | Potential blazars pinpointed using the strategies described in Methods, located within 68%, 90% and 99% error regions around KM3-230213A Their positions are given in equatorial (J2000) coordinates. Sources are numbered according to their distance from KM3-230213A, corresponding to the source listed in the ‘Name’ column. The ‘Association’ column includes catalogued objects at other wavelengths that are associated with this source. The last column indicates the method that led to the identification of the source.