Open Access
| Issue |
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
|
|
|---|---|---|
| Article Number | 01119 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/epjconf/202533701119 | |
| Published online | 07 October 2025 | |
- F. An et al. (JUNO), Neutrino physics with JUNO, J. Phys. G 43, 030401 (2016). 10.1088/0954-3899/43/3/030401 [Google Scholar]
- A. Abusleme et al. (JUNO), JUNO physics and detector, Prog. Part. Nucl. Phys. 123, 103927 (2022). 10.1016/j.ppnp.2021.103927 [CrossRef] [Google Scholar]
- A. Abusleme et al. (JUNO), Potential to identify neutrino mass ordering with reactor antineutrinos at JUNO, Chin. Phys. C 49, 033104 (2025). 10.1088/1674-1137/ad7f3e [Google Scholar]
- A. Abusleme et al. (JUNO), Sub-percent precision measurement of neutrino oscillation parameters with JUNO, Chin. Phys. C 46, 123001 (2022). 10.1088/1674-1137/ac8bc9 [Google Scholar]
- A. Abusleme et al. (JUNO), Prediction of Energy Resolution in the JUNO Experiment, Chin. Phys. C 49, 013003 (2025). 10.1088/1674-1137/ad83aa [Google Scholar]
- T. Lin et al., Simulation software of the JUNO experiment, Eur. Phys. J. C 83, 382 (2023). 10.1140/epjc/s10052-023-11514-x [CrossRef] [Google Scholar]
- Z. Qian et al., Vertex and energy reconstruction in JUNO with machine learning methods, Nucl. Instrum. Meth. A 1010, 165527 (2021). 10.1016/j.nima.2021.165527 [Google Scholar]
- A. Gavrikov, Y. Malyshkin, F. Ratnikov, Energy reconstruction for large liquid scintillator detectors with machine learning techniques: aggregated features approach, Eur. Phys. J. C 82, 1021 (2022). 10.1140/epjc/s10052-022-11004-6 [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]
- Z. Yang et al., First attempt of directionality reconstruction for atmospheric neutrinos in a large homogeneous liquid scintillator detector, Phys. Rev. D 109, 052005 (2024). 10.1103/PhysRevD.109.052005 [Google Scholar]
- A. Gavrikov et al., Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector, Phys. Lett. B 860, 139141 (2025). 10.1016/j.physletb.2024.139141 [Google Scholar]
- W. Jiang, G. Huang, Z. Liu, W. Luo, L. Wen, J. Luo, Machine-learning based photon counting for PMT waveforms and its application to the improvement of the energy resolution in large liquid scintillator detectors, Eur. Phys. J. C 85, 69 (2025). 10.1140/epjc/s10052-024-13724-3 [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]
- A. Abusleme et al. (JUNO), Calibration Strategy of the JUNO Experiment, JHEP 03, 004 (2021). 10.1007/JHEP03(2021)004 [Google Scholar]
- J. Goodman, J. Weare, Ensemble samplers with affine invariance, Commun. Appl. Math. Comput. Sc. 5, 65 (2010). 10.2140/camcos.2010.5.65 [Google Scholar]
- D. Foreman-Mackey, D.W. Hogg, D. Lang, J. Goodman, emcee: the mcmc hammer, Publications of the Astronomical Society of the Pacific 125, 306 (2013). 10.1086/670067 [Google Scholar]
- A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin, Attention Is All You Need (2017), arXiv:1706.03762, https://arxiv.org/abs/1706.03762 [Google Scholar]
- I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative Adversarial Networks (2014), arXiv:1406.2661, https://arxiv.org/abs/1406.2661 [Google Scholar]
- I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, A. Courville, Improved Training of Wasserstein GANs (2017), arXiv:1704.00028, https://arxiv.org/abs/1704.00028 [Google Scholar]
- D.J. Rezende, S. Mohamed, Variational Inference with Normalizing Flows (2015), arXiv:1505.05770, https://arxiv.org/abs/1505.05770 [Google Scholar]
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