Abstract
KM3NeT/ORCA is a water Cherenkov neutrino detector under construction and anchored at the bottom of the Mediterranean Sea. The detector is designed to study oscillations of atmospheric neutrinos and determine the neutrino mass ordering. This paper focuses on an initial configuration of ORCA, referred to as ORCA6, which comprises six out of the foreseen 115 detection units of photo-sensors. A high-purity neutrino sample was extracted, corresponding to an exposure of 433 kton-years. The sample of 5828 neutrino candidates is analysed following a binned log-likelihood method in the reconstructed energy and cosine of the zenith angle. The atmospheric oscillation parameters are measured to be \( {\sin}^2{\theta}_{23}={0.51}_{-0.05}^{+0.04} \), and \( \Delta {m}_{31}^2={2.18}_{-0.35}^{+0.25}\times {10}^{-3}{\textrm{eV}}^2\cup \left\{-2.25,-1.76\right\}\times {10}^{-3} \) eV2 at 68% CL. The inverted neutrino mass ordering hypothesis is disfavoured with a p-value of 0.25.
Article PDF
Similar content being viewed by others
References
T. Kajita, Nobel lecture: discovery of atmospheric neutrino oscillations, Rev. Mod. Phys. 88 (2016) 030501 [INSPIRE].
B. Pontecorvo, Mesonium and anti-mesonium, Sov. Phys. JETP 6 (1957) 429 [INSPIRE].
Z. Maki, M. Nakagawa and S. Sakata, Remarks on the unified model of elementary particles, Prog. Theor. Phys. 28 (1962) 870 [INSPIRE].
I. Esteban et al., The fate of hints: updated global analysis of three-flavor neutrino oscillations, JHEP 09 (2020) 178 [arXiv:2007.14792] [INSPIRE].
P.F. de Salas et al., 2020 global reassessment of the neutrino oscillation picture, JHEP 02 (2021) 071 [arXiv:2006.11237] [INSPIRE].
F. Capozzi et al., Unfinished fabric of the three neutrino paradigm, Phys. Rev. D 104 (2021) 083031 [arXiv:2107.00532] [INSPIRE].
J. Coelho et al., joaoabcoelho/oscprob: v2.0.12, Zenodo, November 2023.
A.M. Dziewonski and D.L. Anderson, Preliminary reference earth model, Phys. Earth Planet. Interiors 25 (1981) 297 [INSPIRE].
KM3Net collaboration, Letter of intent for KM3NeT 2.0, J. Phys. G 43 (2016) 084001 [arXiv:1601.07459] [INSPIRE].
A.Y. Smirnov, The MSW effect and solar neutrinos, in the proceedings of the 10thinternational workshop on neutrino telescopes, (2003) [hep-ph/0305106] [INSPIRE].
KM3NeT collaboration, First neutrino oscillation measurement in KM3NeT/ORCA, PoS ICRC2021 (2021) 1123 [INSPIRE].
V. Carretero, Searches for neutrino physics beyond the standard model with KM3NeT/ORCA6, PoS ICHEP2022 (2022) 578 [INSPIRE].
ANTARES collaboration, ANTARES: the first undersea neutrino telescope, Nucl. Instrum. Meth. A 656 (2011) 11 [arXiv:1104.1607] [INSPIRE].
KM3NeT collaboration, Determining the neutrino mass ordering and oscillation parameters with KM3NeT/ORCA, Eur. Phys. J. C 82 (2022) 26 [arXiv:2103.09885] [INSPIRE].
KM3NeT and JUNO collaborations, Combined sensitivity of JUNO and KM3NeT/ORCA to the neutrino mass ordering, JHEP 03 (2022) 055 [arXiv:2108.06293] [INSPIRE].
KM3NeT collaboration, Sensitivity to light sterile neutrino mixing parameters with KM3NeT/ORCA, JHEP 10 (2021) 180 [arXiv:2107.00344] [INSPIRE].
KM3NeT collaboration, Probing invisible neutrino decay with KM3NeT/ORCA, JHEP 04 (2023) 090 [arXiv:2302.02717] [INSPIRE].
KM3NeT collaboration, Lorentz invariance violation with KM3NeT/ORCA115, PoS ICRC2023 (2023) 1086 [INSPIRE].
KM3NeT collaboration, The KM3NeT multi-PMT optical module, 2022 JINST 17 P07038 [arXiv:2203.10048] [INSPIRE].
D. Lefevre, M. Libes, D. Mallarino, K. Bernardet and C. Gojak, EMSO-Ligure Ouest observatory data (MII) from 2019–08, (2024) [https://doi.org/10.17882/75839].
KM3NeT collaboration, Characterisation of the Hamamatsu photomultipliers for the KM3NeT neutrino telescope, 2018 JINST 13 P05035 [INSPIRE].
S.H.D. Haddock, M.A. Moline and J.F. Case, Bioluminescence in the sea, Ann. Rev. Marine Sci. 2 (2010) 443.
ANTARES collaboration, Long-term monitoring of the ANTARES optical module efficiencies using 40K decays in sea water, Eur. Phys. J. C 78 (2018) 669 [arXiv:1805.08675] [INSPIRE].
J.A. Formaggio and G.P. Zeller, From eV to EeV: neutrino cross sections across energy scales, Rev. Mod. Phys. 84 (2012) 1307 [arXiv:1305.7513] [INSPIRE].
Particle Data Group collaboration, Review of particle physics, PTEP 2022 (2022) 083C01 [INSPIRE].
KM3NeT collaboration, The KM3NeT data acquisition system — status and evolution, EPJ Web Conf. 280 (2023) 08004 [INSPIRE].
B. Ó Fearraigh, Following the light: novel event reconstruction techniques for neutrino oscillation analyses in KM3NeT/ORCA, Ph.D. thesis, https://www.nikhef.nl/pub/services/biblio/theses_pdf/thesis_B_Ofearraigh.pdf, Amsterdam U., Amsterdam, The Netherlands (2024).
A. Domi, Shower reconstruction and sterile neutrino analysis with KM3NeT/ORCA and ANTARES, Ph.D. thesis, Aix-Marseille U., Genoa U., (2019) [INSPIRE].
KM3NeT collaboration, Nanobeacon: a time calibration device for the KM3NeT neutrino telescope, Nucl. Instrum. Meth. A 1040 (2022) 167132 [arXiv:2111.00223] [INSPIRE].
KM3NeT collaboration, Time, position and orientation calibration using atmospheric muons in KM3NeT, PoS ICRC2023 (2023) 218 [INSPIRE].
KM3NeT collaboration, KM3NeT time calibration with nanobeacons, PoS ICRC2023 (2023) 1062 [INSPIRE].
F. Bretaudeau, C.G. Oliver, M. de Jong and L. Martin, Dynamical position and orientation calibration of the KM3NeT telescope, PoS ICRC2023 (2023) 1033 [INSPIRE].
KM3NeT collaboration, gSeaGen: the KM3NeT GENIE-based code for neutrino telescopes, Comput. Phys. Commun. 256 (2020) 107477 [arXiv:2003.14040] [INSPIRE].
C. Andreopoulos et al., The GENIE neutrino Monte Carlo generator: physics and user manual, arXiv:1510.05494 [INSPIRE].
A.G. Tsirigotis, A. Leisos and S.E. Tzamarias, HOU Reconstruction & Simulation (HOURS): a complete simulation and reconstruction package for very large volume underwater neutrino telescopes, Nucl. Instrum. Meth. A 626-627 (2011) S185 [INSPIRE].
GEANT4 collaboration, GEANT4 — a simulation toolkit, Nucl. Instrum. Meth. A 506 (2003) 250 [INSPIRE].
G. Carminati, A. Margiotta and M. Spurio, Atmospheric MUons from PArametric formulas: a Fast GEnerator for neutrino telescopes (MUPAGE), Comput. Phys. Commun. 179 (2008) 915 [arXiv:0802.0562] [INSPIRE].
Y. Becherini, A. Margiotta, M. Sioli and M. Spurio, A parameterisation of single and multiple muons in the deep water or ice, Astropart. Phys. 25 (2006) 1 [hep-ph/0507228] [INSPIRE].
T. Chen and C. Guestrin, XGBoost: a scalable tree boosting system, in Proceedings of the 22ndACM SIGKDD international conference on knowledge discovery and data mining, KDD ’16, New York, NY, U.S.A., Association for Computing Machinery, U.S.A. (2016), p. 785 [https://doi.org/10.1145/2939672.2939785] [arXiv:1603.02754] [INSPIRE].
S. Bourret, Neutrino oscillations and earth tomography with KM3NeT-ORCA, Ph.D. thesis, https://tel.archives-ouvertes.fr/tel-02491394/file/BOURRET_Simon_1_complete_20181130.pdf, APC, Paris, France (2018).
M. Honda et al., Atmospheric neutrino flux calculation using the NRLMSISE-00 atmospheric model, Phys. Rev. D 92 (2015) 023004 [arXiv:1502.03916] [INSPIRE].
R.D. Cousins, Generalization of Chisquare Goodness-ofFit test for binned data using saturated models, with application to histograms, https://api.semanticscholar.org/CorpusID:5936965 (2013).
S. Baker and R.D. Cousins, Clarification of the use of chi square and likelihood functions in fits to histograms, Nucl. Instrum. Meth. 221 (1984) 437 [INSPIRE].
R.J. Barlow and C. Beeston, Fitting using finite Monte Carlo samples, Comput. Phys. Commun. 77 (1993) 219 [INSPIRE].
J.S. Conway, Incorporating nuisance parameters in likelihoods for multisource spectra, in the proceedings of the PHYSTAT 2011, (2011), p. 115 [https://doi.org/10.5170/CERN-2011-006.115] [arXiv:1103.0354] [INSPIRE].
G.D. Barr, T.K. Gaisser, S. Robbins and T. Stanev, Uncertainties in atmospheric neutrino fluxes, Phys. Rev. D 74 (2006) 094009 [astro-ph/0611266] [INSPIRE].
J. Evans et al., Uncertainties in atmospheric muon-neutrino fluxes arising from cosmic-ray primaries, Phys. Rev. D 95 (2017) 023012 [arXiv:1612.03219] [INSPIRE].
T.N. Chau, Study of atmospheric neutrino oscillations with the deep-sea Cherenkov detector KM3NeT/ORCA and synergies with reactor neutrinos, Ph.D. thesis, Paris U., Paris, France (2021) [INSPIRE].
K. Cranmer, Practical statistics for the LHC, in the proceedings of the 2011 European school of high-energy physics, (2014) [https://doi.org/10.5170/CERN-2014-003.267] [arXiv:1503.07622] [INSPIRE].
G.J. Feldman and R.D. Cousins, A unified approach to the classical statistical analysis of small signals, Phys. Rev. D 57 (1998) 3873 [physics/9711021] [INSPIRE].
S.S. Wilks, The large-sample distribution of the likelihood ratio for testing composite hypotheses, Annals Math. Statist. 9 (1938) 60 [INSPIRE].
IceCube collaboration, Measurement of atmospheric neutrino oscillation parameters using convolutional neural networks with 9.3 years of data in IceCube DeepCore, arXiv:2405.02163 [INSPIRE].
T2K collaboration, Updated T2K measurements of muon neutrino and antineutrino disappearance using 3.6 × 1021 protons on target, Phys. Rev. D 108 (2023) 072011 [arXiv:2305.09916] [INSPIRE].
Super-Kamiokande collaboration, Recent results and future prospects of Super-Kamiokande, Nucl. Instrum. Meth. A 952 (2020) 161634 [INSPIRE].
A. Aurisano, Recent results from MINOS and MINOS+, in XXVIII international conference on neutrino physics and astrophysics, Zenodo, June 2018, p. 423.
A. Himmel, New oscillation results from the NOvA experiment, Zenodo, October 2020.
S. Algeri, J. Aalbers, K. Dundas Morå and J. Conrad, Searching for new phenomena with profile likelihood ratio tests, Nature Rev. Phys. 2 (2020) 245 [arXiv:1911.10237] [INSPIRE].
Acknowledgments
The authors acknowledge the financial support of the funding agencies: Funds for Scientific Research (FRS-FNRS), Francqui foundation, BAEF foundation. Czech Science Foundation (GAČR 24-12702S); Agence Nationale de la Recherche (contract ANR-15-CE31-0020), Centre National de la Recherche Scientifique (CNRS), Commission Européenne (FEDER fund and Marie Curie Program), LabEx UnivEarthS (ANR-10-LABX-0023 and ANR-18-IDEX-0001), Paris Île-de-France Region, Normandy Region (Alpha, Blue-waves and Neptune), France; Shota Rustaveli National Science Foundation of Georgia (SRNSFG, FR-22-13708), Georgia; This work is part of the MuSES project which has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement No 101142396). The General Secretariat of Research and Innovation (GSRI), Greece; Istituto Nazionale di Fisica Nucleare (INFN) and Ministero dell’Università e della Ricerca (MUR), through PRIN 2022 program (Grant PANTHEON 2022E2J4RK, Next Generation EU) and PON R&I program (Avviso n. 424 del 28 febbraio 2018, Progetto PACK-PIR01 00021), Italy; IDMAR project Po-Fesr Sicilian Region az. 1.5.1; A. De Benedittis, W. Idrissi Ibnsalih, M. Bendahman, A. Nayerhoda, G. Papalashvili, I. C. Rea, A. Simonelli have been supported by the Italian Ministero dell’Università e della Ricerca (MUR), Progetto CIR01 00021 (Avviso n. 2595 del 24 dicembre 2019); KM3NeT4RR MUR Project National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 3.1, Funded by the European Union – NextGenerationEU,CUP I57G21000040001, Concession Decree MUR No. n. Prot. 123 del 21/06/2022; Ministry of Higher Education, Scientific Research and Innovation, Morocco, and the Arab Fund for Economic and Social Development, Kuwait; Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO), the Netherlands; Ministry of Research, Innovation and Digitalisation, Romania; Slovak Research and Development Agency under Contract No. APVV-22-0413; Ministry of Education, Research, Development and Youth of the Slovak Republic; MCIN for PID2021-124591NB-C41, -C42, -C43 and PDC2023-145913-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”, for ASFAE/2022/014 and ASFAE/2022 /023 with funding from the EU NextGenerationEU (PRTR-C17.I01) and Generalitat Valenciana, for Grant AST22_6.2 with funding from Consejería de Universidad, Investigación e Innovación and Gobierno de España and European Union - NextGenerationEU, for CSIC-INFRA23013 and for CNS2023-144099, Generalitat Valenciana for CIDEGENT/2018/034, /2019/043, /2020/049, /2021/23, for CIDEIG/2023/20, for CIPROM/2023/51 and for GRISOLIAP/2021/192 and EU for MSC/101025085, Spain; Khalifa University internal grants (ESIG-2023-008 and RIG-2023-070), United Arab Emirates; The European Union’s Horizon 2020 Research and Innovation Programme (ChETEC-INFRA - Project no. 101008324).
Data Availability Statement. This article has no associated data or the data will not be deposited.
Code Availability Statement. This article has no associated code or the code will not be deposited.
Author information
Authors and Affiliations
Consortia
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
ArXiv ePrint: 2408.07015
Rights and permissions
Open Access . This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.
About this article
Cite this article
The KM3NeT collaboration., Aiello, S., Albert, A. et al. Measurement of neutrino oscillation parameters with the first six detection units of KM3NeT/ORCA. J. High Energ. Phys. 2024, 206 (2024). https://doi.org/10.1007/JHEP10(2024)206
Received:
Revised:
Accepted:
Published:
Version of record:
DOI: https://doi.org/10.1007/JHEP10(2024)206