Abstract
Neutrino oscillations (see refs. 1,2 and references therein), a quantum effect manifesting at macroscopic scales, are governed by lepton flavour mixing angles and neutrino mass-squared differences3 that are fundamental parameters of particle physics, representing phenomena beyond the Standard Model. Precision measurements of these parameters are essential for testing the completeness of the three-flavour framework, determining the mass ordering of neutrinos and probing possible new physics. The Jiangmen Underground Neutrino Observatory (JUNO)4 is a 20-ktonne liquid-scintillator detector located 52.5 km from multiple reactor cores, designed to resolve the interference pattern of reactor neutrinos with sub-percent precision5,6. Here we report, using the first 59.1 days of data collected since detector completion in August 2025, the first simultaneous high-precision determination of two neutrino oscillation parameters, \({\sin }^{2}{\theta }_{12}=0.3092\,\pm \,0.0087\) and \(\Delta {m}_{21}^{2}=(7.50\,\pm \,0.12)\times 1{0}^{-5}\,{\mathrm{eV}}^{2}\) for the normal mass ordering scenario, improving the precision by a factor of 1.6 relative to the combination of all previous measurements. These results advance the basic understanding of neutrinos, validate the design of the detector and indicate the readiness of JUNO for resolving the neutrino mass ordering with a larger dataset. The rapid achievement with a short exposure highlights the potential of JUNO to push the frontiers of precision neutrino physics and paves the way for its broad scientific programme.
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Data availability
Raw experimental data from the JUNO detectors are not publicly accessible due to their complexity and volume. Source data for Figs. 3, 4, and Extended Data Fig. 2 are, however, provided. Furthermore, inquiries regarding the data and posteriors used in this result may be directed to the collaboration.
Code availability
The JUNO Collaboration develops and maintains the code used for the simulation of the experimental apparatus and statistical analysis of the raw data used in this result. This code is shared among the Collaboration, but because of the size and complexity of the codebases, it is not publicly distributed. Inquiries regarding the algorithms and methods used in this result may be directed to the Collaboration.
References
Kajita, T. Nobel lecture: discovery of atmospheric neutrino oscillations. Rev. Mod. Phys. 88, 030501 (2016).
McDonald, A. B. Nobel lecture: the Sudbury Neutrino Observatory: observation of flavor change for solar neutrinos. Rev. Mod. Phys. 88, 030502 (2016).
Navas, S. et al. Review of particle physics. Phys. Rev. D 110, 030001 (2024).
JUNO Collaboration. JUNO physics and detector. Prog. Part. Nucl. Phys. 123, 103927 (2022).
Abusleme, A. et al. Potential to identify neutrino mass ordering with reactor antineutrinos at JUNO. Chin. Phys. C 49, 033104 (2025).
Abusleme, A. et al. Sub-percent precision measurement of neutrino oscillation parameters with JUNO. Chin. Phys. C 46, 123001 (2022).
Pontecorvo, B. Neutrino experiments and the problem of conservation of leptonic charge. Sov. Phys. JETP 26, 984–988 (1968).
Maki, Z., Nakagawa, M. & Sakata, S. Remarks on the unified model of elementary particles. Prog. Theor. Phys. 28, 870–880 (1962).
Xing, Z.-Z. & Zhou, S. Neutrinos in Particle Physics, Astronomy and Cosmology. Advanced Topics in Science and Technology in China (Springer, 2011).
Fukuda, S. et al. Solar 8B and hep neutrino measurements from 1258 days of Super-Kamiokande data. Phys. Rev. Lett. 86, 5651–5655 (2001).
Ahmad, Q. R. et al. Direct evidence for neutrino flavor transformation from neutral-current interactions in the Sudbury Neutrino Observatory. Phys. Rev. Lett. 89, 011301 (2002).
Eguchi, K. et al. First results from KamLAND: evidence for reactor antineutrino disappearance. Phys. Rev. Lett. 90, 021802 (2003).
Gando, A. et al. Reactor on-off antineutrino measurement with KamLAND. Phys. Rev. D 88, 033001 (2013).
Fukuda, Y. et al. Evidence for oscillation of atmospheric neutrinos. Phys. Rev. Lett. 81, 1562–1567 (1998).
Ahn, M. H. et al. Indications of neutrino oscillation in a 250 km long-baseline experiment. Phys. Rev. Lett. 90, 041801 (2003).
Michael, D. G. et al. Observation of muon neutrino disappearance with the MINOS detectors in the NuMI neutrino beam. Phys. Rev. Lett. 97, 191801 (2006).
An, F. P. et al. Observation of electron-antineutrino disappearance at Daya Bay. Phys. Rev. Lett. 108, 171803 (2012).
Petcov, S. & Piai, M. The LMA MSW solution of the solar neutrino problem, inverted neutrino mass hierarchy and reactor neutrino experiments. Phys. Lett. B 533, 94–106 (2002).
Learned, J. G., Dye, S. T., Pakvasa, S. & Svoboda, R. C. Determination of neutrino mass hierarchy and θ13 with a remote detector of reactor antineutrinos. Phys. Rev. D 78, 071302 (2008).
Zhan, L., Wang, Y., Cao, J. & Wen, L. Determination of the neutrino mass hierarchy at an intermediate baseline. Phys. Rev. D 78, 111103(R) (2008).
Zhan, L., Wang, Y., Cao, J. & Wen, L. Experimental requirements to determine the neutrino mass hierarchy using reactor neutrinos. Phys. Rev. D 79, 073007 (2009).
Li, Y.-F., Cao, J., Wang, Y. & Zhan, L. Unambiguous determination of the neutrino mass hierarchy using reactor neutrinos. Phys. Rev. D 88, 013008 (2013).
An, F. et al. Neutrino physics with JUNO. J. Phys. G Nucl. Part. Phys. 43, 030401 (2016).
Abusleme, A. et al. The design and technology development of the JUNO central detector. Eur. Phys. J. Plus 139, 1128 (2024).
Abusleme, A. et al. Prediction of energy resolution in the JUNO experiment. Chin. Phys. C 49, 013003 (2025).
Abusleme, A. et al. Real-time monitoring for the next core-collapse supernova in JUNO. J. Cosmol. Astropart. Phys. 01, 057 (2024).
Abusleme, A. et al. Prospects for detecting the diffuse supernova neutrino background with JUNO. J. Cosmol. Astropart. Phys. 10, 033 (2022).
Li, Y.-F. Prospects for geoneutrino detection with JUNO. Chin. Phys. C https://doi.org/10.1088/1674-1137/ae457d (2026).
Abusleme, A. et al. JUNO sensitivity to low energy atmospheric neutrino spectra. Eur. Phys. J. C 81, 887 (2021).
Abusleme, A. et al. JUNO sensitivity to 7Be, pep, and CNO solar neutrinos. J. Cosmol. Astropart. Phys. 10, 022 (2023).
Abusleme, A. et al. Feasibility and physics potential of detecting 8B solar neutrinos at JUNO. Chin. Phys. C 45, 023004 (2021).
Zhao, J. et al. Model-independent approach of the JUNO 8B solar neutrino program. Astrophys. J. 965, 122 (2024).
Abusleme, A. et al. JUNO sensitivity on proton decay \(p\to \overline{\nu }{K}^{+}\) searches. Chin. Phys. C 47, 113002 (2023).
Abusleme, A. et al. JUNO sensitivity to the annihilation of MeV dark matter in the galactic halo. J. Cosmol. Astropart. Phys. 09, 001 (2023).
JUNO Collaboration. JUNO sensitivity to invisible decay modes of neutrons. Eur. Phys. J. C 85, 5 (2025).
Abusleme, A. et al. Initial performance results of the JUNO detector. Chin. Phys. C 50, 043001 (2026)
Abusleme, A. et al. Calibration strategy of the JUNO experiment. J. High Energy Phys. 03, 004 (2021).
Vogel, P. & Beacom, J. F. Angular distribution of neutron inverse beta decay, \({\bar{\nu }}_{e}+p\to {e}^{+}+n\). Phys. Rev. D 60, 053003 (1999).
Adey, D. et al. A high precision calibration of the nonlinear energy response at Daya Bay. Nucl. Instrum. Methods Phys. Res. A 940, 230–242 (2019).
An, F. P. et al. First measurement of the yield of 8He isotopes produced in liquid scintillator by cosmic-ray muons at Daya Bay. Phys. Rev. D 110, L011101 (2024).
An, F. P. et al. Precision measurement of reactor antineutrino oscillation at kilometer-scale baselines by Daya Bay. Phys. Rev. Lett. 130, 161802 (2023).
Enomoto, S. Geoneutrino Spectrum and Luminosity. https://www.awa.tohoku.ac.jp/~sanshiro/research/geoneutrino/spectrum/ (2006).
Li, Y.-F. & Xin, Z. New calculation of the geo-neutrino energy spectrum and its implication. Preprint at https://arxiv.org/abs/2412.07711 (2024).
China Nuclear Energy Association. National Nuclear Power Status Reports 2025 https://m.china-nea.cn/site/content/50357.html (CNEA, 2025).
IAEA. Operating Experience with Nuclear Power Stations in Member States 2025 Edition. IAEA/OPEX/2025. https://www.iaea.org/publications/15944 (2025).
An, F. P. et al. Comprehensive measurement of the reactor antineutrino spectrum and flux at Daya Bay. Phys. Rev. Lett. 134, 201802 (2025).
Daya Bay Collaboration. Full data release of the Daya Bay reactor neutrino experiment. v1.0.0. Zenodo https://doi.org/10.5281/zenodo.17587229 (2025).
Huber, P. Determination of antineutrino spectra from nuclear reactors. Phys. Rev. C 84, 024617 (2011).
Mueller, Th. A. et al. Improved predictions of reactor antineutrino spectra. Phys. Rev. C 83, 054615 (2011).
Li, Y., Wang, Y. & Xing, Z. Terrestrial matter effects on reactor antineutrino oscillations at JUNO or RENO-50: how small is small? Chin. Phys. C 40, 091001 (2016).
Ji, X., Gu, W., Qian, X., Wei, H. & Zhang, C. Combined Neyman–Pearson chi-square: an improved approximation to the Poisson-likelihood chi-square. Nucl. Instrum. Meth. A 961, 163677 (2020).
Abe, K. et al. Solar neutrino measurements using the full data period of Super-Kamiokande-IV. Phys. Rev. D 109, 092001 (2024).
Aharmim, B. et al. Combined analysis of all three phases of solar neutrino data from the Sudbury Neutrino Observatory. Phys. Rev. C 88, 025501 (2013).
Abusleme, A. et al. TAO conceptual design report: a precision measurement of the reactor antineutrino spectrum with sub-percent energy resolution. Preprint at https://arxiv.org/abs/2005.08745 (2020).
Abreu, M. et al. Measurement of reactor antineutrino oscillation at SNO+. Phys. Rev. Lett. 135, 121801 (2025).
Adamson, P. et al. Precision constraints for three-flavor neutrino oscillations from the full MINOS+ and MINOS dataset. Phys. Rev. Lett. 125, 131802 (2020).
Wester, T. et al. Atmospheric neutrino oscillation analysis with neutron tagging and an expanded fiducial volume in Super-Kamiokande I–V. Phys. Rev. D 109, 072014 (2024).
Abbasi, R. et al. Measurement of atmospheric neutrino oscillation parameters using convolutional neural networks with 9.3 years of data in IceCube DeepCore. Phys. Rev. Lett. 134, 091801 (2025).
Abe, K. et al. First joint oscillation analysis of Super-Kamiokande atmospheric and T2K accelerator neutrino data. Phys. Rev. Lett. 134, 011801 (2025).
The KM3NeT Collaboration. Measurement of neutrino oscillation parameters with the first six detection units of KM3NeT/ORCA. J. High Energ. Phys. 10, 206 (2024).
Abe, K. et al. Results from the T2K experiment on neutrino mixing including a new far detector μ-like sample. Phys. Rev. Lett. 135, 261801 (2025).
Abubakar, S. et al. Precision measurement of neutrino oscillation parameters with 10 years of data from the NOvA experiment. Phys. Rev. Lett. 136, 011802 (2026).
The NOvA Collaboration & The T2K Collaboration. Joint neutrino oscillation analysis from the T2K and NOvA experiments. Nature 646, 818–824 (2025).
Jeon, S. et al. Measurement of reactor antineutrino oscillation parameters using the full 3800-day dataset of the RENO experiment. Phys. Rev. D 111, 112006 (2025).
The Double Chooz Collaboration. Double Chooz θ13 measurement via total neutron capture detection. Nat. Phys. 16, 558–564 (2020).
Acero, M. A. et al. Improved measurement of neutrino oscillation parameters by the NOvA experiment. Phys. Rev. D 106, 032004 (2022).
Abe, K. et al. Measurements of neutrino oscillation parameters from the T2K experiment using 3.6 x 1021 protons on target. Eur. Phys. J C. Part Fields 83, 782 (2023).
An, F. P. et al. Measurement of electron antineutrino oscillation amplitude and frequency via neutron capture on hydrogen at Daya Bay. Phys. Rev. Lett. 133, 151801 (2024).
Wolfenstein, L. Neutrino oscillations in matter. Phys. Rev. D 17, 2369–2374 (1978).
Mikheyev, S. P. & Smirnov, A. Y. Resonance amplification of oscillations in matter and spectroscopy of solar neutrinos. Sov. J. Nucl. Phys. 42, 913–917 (1985).
Li, Y.-F., Wang, A., Xu, Y. & Zhu, J. Terrestrial matter effects on reactor antineutrino oscillations: constant vs. fluctuated density profiles. J. High Energ. Phys. https://doi.org/10.1007/JHEP03(2026)264 (2026).
Abusleme, A. et al. Optimization of the JUNO liquid scintillator composition using a Daya Bay antineutrino detector. Nucl. Instrum. Methods Phys. Res. A 988, 164823 (2021).
Zhang, Y., Yu, Z. Y., Li, X. Y., Deng, Z. Y. & Wen, L. J. A complete optical model for liquid-scintillator detectors. Nucl. Instrum. Methods Phys. Res. A 967, 163860 (2020).
Acquafredda, R. et al. The OPERA experiment in the CERN to Gran Sasso neutrino beam. J. Instrum. 4, P04018 (2009).
Abusleme, A. et al. The JUNO experiment top tracker. Nucl. Instrum. Methods Phys. Res. A 1057, 168680 (2023).
Abusleme, A. et al. The design and sensitivity of JUNO’s scintillator radiopurity pre-detector OSIRIS. Eur. Phys. J. C. 81, 973 (2021).
The JUNO Collaboration. Radioactivity control strategy for the JUNO detector. J. High Energ. Phys. 2021, 102 (2021).
Huang, G.-H., Jiang, W., Wen, L.-J., Wang, Y.-F. & Luo, W.-M. Data-driven simultaneous vertex and energy reconstruction for large liquid scintillator detectors. Nucl. Sci. Tech. 34, 83 (2023).
Liu, Q., He, M., Ding, X., Li, W. & Peng, H. A vertex reconstruction algorithm in the central detector of JUNO. J. Instrum. 13, T09005 (2018).
Takenaka, A. et al. Neutron source-based event reconstruction algorithm in large liquid scintillator detectors. Eur. Phys. J. C 85, 1097 (2025).
Berger, M. J., Coursey, J. S., Zucker, M. A. & Chang, J. ESTAR, PSTAR and ASTAR: Computer Programs for Calculating Stopping-Power and Rrange Tables for Electrons, Protons, and Helium Ions (version 1). NIST Standard Reference Database 124 (2017).
Birks, J. B. The Theory and Practice of Scintillation Counting (Pergamon, 1964).
Frank, I. M. & Tamm, I. E. Coherent visible radiation of fast electrons passing through matter. Compt. Rend. Acad. Sci. URSS 14, 109–114 (1937).
Agostinelli, S. et al. GEANT4–a simulation toolkit. Nucl. Instrum. Methods Phys. Res. A 506, 250–303 (2003).
Battistoni, G. et al. Overview of the FLUKA code. Ann. Nucl. Energy 82, 10–18 (2015).
Ahdida, C. et al. New capabilities of the FLUKA multi-purpose code. Front. Phys. 9, 788253 (2022).
An, F. P. et al. Measurement of electron antineutrino oscillation based on 1230 days of operation of the Daya Bay experiment. Phys. Rev. D 95, 072006 (2017).
Strati, V. et al. Expected geoneutrino signal at JUNO. Prog. Earth Planet. Sci. 2, 5 (2015).
Reguzzoni, M. et al. GIGJ: a crustal gravity model of the Guangdong province for predicting the geoneutrino signal at the JUNO experiment. J. Geophys. Res. Solid Earth 124, 4231–4249 (2019).
Gao, R. et al. JULOC: a local 3-D high-resolution crustal model in South China for forecasting geoneutrino measurements at JUNO. Phys. Earth Planet. Interiors 299, 106409 (2020).
Han, R. et al. Expected geoneutrino signal at JUNO using local integrated 3-D refined crustal model. Preprint at https://arxiv.org/abs/2210.09165 (2022).
Agostini, M. et al. Comprehensive geoneutrino analysis with Borexino. Phys. Rev. D 101, 012009 (2020).
Cheng, J. et al. Neutral-current background induced by atmospheric neutrinos at large liquid-scintillator detectors: III. Comprehensive prediction for low energy neutrinos. Eur. Phys. J. C 85, 295 (2025).
Andreopoulos, C. et al. The GENIE neutrino Monte Carlo generator. Nucl. Instrum. Meth. Phys. Res. A 614, 87–104 (2010).
Golan, T., Sobczyk, J. T. & Zmuda, J. NuWro: the Wroclaw Monte Carlo generator of neutrino interactions. Nucl. Phys. B Proc. Suppl. 229-232, 499 (2012).
Honda, M., Sajjad Athar, M., Kajita, T., Kasahara, K. & Midorikawa, S. Atmospheric neutrino flux calculation using the NRLMSISE-00 atmospheric model. Phys. Rev. D 92, 023004 (2015).
Koning, A. J., Hilaire, S. & Duijvestijn, M. C. TALYS: comprehensive nuclear reaction modeling. AIP Conf. Proc. 769, 1154–1159 (2005).
JUNO Collaboration. Simulation of the background from 13C(α, n)16 O reaction in the JUNO scintillator. Eur. Phys. J. C 85, 1080 (2025).
Ma, X. B., Zhong, W. L., Wang, L. Z., Chen, Y. X. & Cao, J. Improved calculation of the energy release in neutron-induced fission. Phys. Rev. C 88, 014605 (2013).
Ma, X. B., Zhao, Y. F., Chen, Y. X., Zhong, W. L. & An, F. P. Antineutrino flux and spectrum calculation for spent nuclear fuel for the Daya Bay antineutrino experiment. Nucl. Phys. A 966, 294–305 (2017).
Sanchez, R. et al. APOLLO2 year 2010. Nucl. Eng. Tech. 42, 474–499 (2010).
Ma, X. B. et al. Uncertainty analysis of fission fraction for reactor antineutrino experiments. Mod. Phys. Lett. A 31, 1650120 (2016).
An, F. P. et al. Improved Measurement of the reactor antineutrino flux and spectrum at Daya Bay. Chin. Phys. C 41, 013002 (2017).
An, F. P. et al. Antineutrino energy spectrum unfolding based on the Daya Bay measurement and its applications. Chin. Phys. C 45, 073001 (2021).
Estienne, M. et al. Updated summation model: an improved agreement with the Daya Bay antineutrino fluxes. Phys. Rev. Lett. 123, 022502 (2019).
Fang, D.-L., Li, Y.-F. & Zhang, D. Ab initio calculations of reactor antineutrino fluxes with exact lepton wave functions. Phys. Lett. B 813, 136067 (2021).
Perissé, L. et al. Comprehensive revision of the summation method for the prediction of reactor \({\bar{\nu }}_{e}\) fluxes and spectra. Phys. Rev. C 108, 055501 (2023).
Feldman, G. J. & Cousins, R. D. Unified approach to the classical statistical analysis of small signals. Phys. Rev. D 57, 3873–3889 (1998).
Acknowledgements
We acknowledge the continued cooperation and support of the China General Nuclear Power Group in the construction and operation of the JUNO experiment. We also recognize the computing resources provided by the Chinese Academy of Sciences, IN2P3, INFN and JINR, which are essential for data processing and analysis within the JUNO Collaboration. We acknowledge the financial and institutional support from the Chinese Academy of Sciences, the National Key R&D Program of China, the People’s Government of Guangdong Province, the Tsung–Dao Lee Institute of Shanghai Jiao Tong University, and the China Center of Advanced Science and Technology in China. We appreciate the contributions from the Institut National de Physique Nucléaire et de Physique des Particules (IN2P3) in France, the Istituto Nazionale di Fisica Nucleare (INFN) in Italy, the Fonds de la Recherche Scientifique (F.R.S.–FNRS) and the Institut Interuniversitaire des Sciences Nucléaires (IISN) in Belgium, and the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) in Brazil. We also acknowledge the support of the Agencia Nacional de Investigación y Desarrollo (ANID) and the ANID–Millennium Science Initiative Program (ICN2019044) in Chile; the European Structural and Investment Funds, the Ministry of Education, Youth and Sports, and the Charles University Research Center in the Czech Republic; the Deutsche Forschungsgemeinschaft (DFG), the Helmholtz Association, and the Cluster of Excellence PRISMA+ in Germany; and the Joint Institute for Nuclear Research (JINR) and Lomonosov Moscow State University in Russia. We further thank the Slovak Research and Development Agency in the Slovak Republic, the National Science and Technology Council (NSCT) and MOE in Taiwan, China, the Program Management Unit for Human Resources and Institutional Development, Research and Innovation (PMU–B), Chulalongkorn University, and Suranaree University of Technology in Thailand, the Science and Technology Facilities Council (STFC) in the United Kingdom, and the University of California at Irvine and the National Science Foundation (NSF) in the United States.
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The JUNO detector was designed, constructed, commissioned and is being operated by the JUNO Collaboration. All aspects of the hardware and software developments, data taking, detector calibration, data processing, Monte Carlo simulation, as well as data analysis, were performed by JUNO members, who also discussed and approved scientific results. All authors reviewed and approved the final version of the paper.
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Extended data figures and tables
Extended Data Fig. 1 Reactor neutrino oscillation at different baselines.
Survival probability \({{\mathcal{P}}}_{ee}\) of 4 MeV reactor \({\overline{\nu }}_{e}\) vs. baseline. Red: slow solar oscillation (\(\Delta {m}_{21}^{2}\)); blue: total \({\mathcal{P}}ee\) including fast atmospheric oscillations (\(\Delta {m}_{31}^{2}\)). Experimental baselines: JUNO (orange, 52.5 km) near solar oscillation maximum, Daya Bay near/far (pink), KamLAND (green). Circle sizes scale with detector sizes.
Extended Data Fig. 2 Energy scale non-linearity calibration.
a, Scintillator non-linearity from γ calibration sources deployed at the detector’s center: single-γ sources (solid circle), multiple-γ sources (hollow rhombus), and the best-fit curve (red). b, Measured cosmogenic 12B β− spectrum (points) compared to prediction (purple line), with a best-fit 12N component (green dashed line)of 2.7% identified via its high-energy shoulder. c, Measured 11C β+ spectrum (points) and the best-fit model (green line). d, Non-linearity response model for electrons (blue), γ’s (red), and positrons (green).
Extended Data Fig. 3 Spatial distribution of reactor neutrino candidates.
Prompt-event counts versus reconstructed cubic radius (R3) are shown for the three vertex algorithms: OMILREC (blue), VTREP (green), and JVertex (orange). Horizontal bars indicate R3 bin widths; vertical dashed lines mark the FV boundary (16.5 m) and detector edge (17.7 m). The inset shows the uniform event density within the FV, with the dashed line indicating the mean count level. The rising counts beyond the FV are primarily due to accidental coincidences, with minor contributions from fast- and double-neutron backgrounds.
Extended Data Fig. 4 Reactor neutrino candidates characteristics.
a, Two-dimensional prompt-versus-delayed energies (with enlarged delayed energy window) showing the 214Bi-214Po cluster at Ed ~ 1 MeV and the IBD selection region (dashed red box). The inset shows the delayed-energy spectrum, featuring a clear neutron-capture peak at Ed ≃ 2.23 MeV (blue dashed line). b, Temporal correlation following an exponential decay with a fitted neutron-capture time of τ = 203.1 ± 7.7 μs. The deviation of the first point is due to the Δt > 5 μs cut and is therefore not included in the fit. c, Spatial separation Δd of the prompt and delayed vertices.
Extended Data Fig. 5 9Li/8He background analysis.
a, Measured 9Li/8He rates across muon visible energy, comparing data before and after the SPN veto. The residual 9Li/8He rates are shown prior to efficiency correction but after the application of all selection cuts. b, Spatial- and temporal-distribution of IBD candidates before the SPN cut shown relative to preceding SPN events. A clear cluster of 9Li/8He events is visible at the origin. The red box indicates the SPN veto criteria.
Extended Data Fig. 6 Temporal distribution of reactor neutrinos.
Rates of antineutrino candidates (after subtraction of the mean background rates), shown in one week time bins (black points with statistical error) are compared to the prediction (red line). Arrows indicate operations on the cores YJ1 and YJ4 of the Yangjiang nuclear power plants. The power plants reduced the power output due to the occurrence of Super Typhoon Ragasa on September 24th.
Extended Data Fig. 7 Three analyses post-fit comparison.
Post-fit pulls of selected nuisance parameters in the three independent JUNO analysis chains. From left to right, these parameters correspond to the detection efficiency, the double-neutron rate, the 9Li/8He rate, the geoneutrino rate, and the 214Bi-214Po rate. Coloured markers with vertical error bars show the best-fit shift and 1σ uncertainty of each parameter relative to its prior, (xfit − xprior)/σprior, for Analyses I–III, while the shaded band denotes the ± 1σ prefit range and the dashed line indicates zero pull.
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The JUNO Collaboration. Measurement of reactor neutrino oscillation with the first JUNO data. Nature 654, 343–348 (2026). https://doi.org/10.1038/s41586-026-10538-z
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DOI: https://doi.org/10.1038/s41586-026-10538-z
