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Application of interpretable data-driven methods for the reconstruction of supernova neutrino energy spectra following fast neutrino flavor conversions

文献类型：期刊论文

作者Shi HH(史海浩)1,2,3,3,4,5; Huang, Zhenyang1,2,3; Yan, Qiyu6; Zhou JD(周均达)1,4,5; Lü, Guoliang2,3; Chen XF(陈雪飞)1,4,5
刊名PHYSICS LETTERS B
出版日期2026-04
卷号875
关键词Interpretable machine learning Supernova neutrinos Fast flavor conversions
ISSN号0370-2693
DOI10.1016/j.physletb.2026.140371
产权排序第4完成单位
文献子类Article
英文摘要Neutrinos can experience fast flavor conversions (FFCs) in highly dense astrophysical environments, such as core-collapse supernovae and neutron star mergers, potentially affecting energy transport and other processes. The simulation of fast flavor conversions under realistic astrophysical conditions requires substantial computational resources and involves significant analytical challenges. While machine learning methods like Multilayer Perceptrons have been used to accurately predict the asymptotic outcomes of FFCs, their 'black-box' nature limits the extraction of direct physical insight. To mitigate this limitation, we employ two distinct interpretable machine learning frameworks-Kolmogorov-Arnold Networks (KANs) and Sparse Identification of Nonlinear Dynamics (SINDy)-to learn interpretable surrogates for the asymptotic input-output mapping from a FFC simulation dataset. Our analysis reveals a fundamental trade-off between predictive accuracy and model simplicity. The KANs demonstrates high fidelity in reconstructing post-conversion neutrino energy spectra, achieving accuracies of up to 90%. In contrast, SINDy yields a low rank, compact closed-form approximation of the input-output mapping, at the expense of some predictive accuracy. Critically, using these structured and sparse surrogates as diagnostic tools, we identify that the system's evolution is most sensitive to the initial number density of heavy-lepton neutrinos when FCCs are triggered, compared to other physical quantities. Ultimately, this work provides a methodological framework for interpretable machine learning that supports genuine data-driven scientific discovery in astronomy and astrophysics, going beyond prediction alone.
学科主题天文学 ; 恒星与银河系
URL标识查看原文
出版地RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
WOS关键词OSCILLATIONS
资助项目National Natural Science Foundation of China[12541303]; National Natural Science Foundation of China[12288102]; National Natural Science Foundation of China[12373038]; National Natural Science Foundation of China[12125303]; National Natural Science Foundation of China[12090040/3]; National Natural Science Foundation of China[U2031204]; National Natural Science Foundation of China[12433009]; Natural Science Foundation of Xinjiang[2022TSYCLJ0006]; China Manned Space Project[CMS-CSST-2021-A10]; National Key R&D Program of China[2021YFA1600401]; National Key R&D Program of China[2021YFA1600403]; Natural Science Foundation of Yunnan Province[202201BC070003]; Natural Science Foundation of Yunnan Province[202001AW070007]; International Centre of Supernovae, Yunnan Key Laboratory[202302AN360001]; Yunnan Revitalization Talent Support Program-Science & Technology Champion[202305AB350003]
WOS研究方向Astronomy & Astrophysics ; Physics
语种英语
WOS记录号WOS:001729634800001
出版者ELSEVIER
资助机构National Natural Science Foundation of China[12541303, 12288102, 12373038, 12125303, 12090040/3, U2031204, 12433009] ; Natural Science Foundation of Xinjiang[2022TSYCLJ0006] ; China Manned Space Project[CMS-CSST-2021-A10] ; National Key R&D Program of China[2021YFA1600401, 2021YFA1600403] ; Natural Science Foundation of Yunnan Province[202201BC070003, 202001AW070007] ; International Centre of Supernovae, Yunnan Key Laboratory[202302AN360001] ; Yunnan Revitalization Talent Support Program-Science & Technology Champion[202305AB350003]
版本出版稿
源URL[http://ir.ynao.ac.cn/handle/114a53/29072]  
专题云南天文台_大样本恒星演化研究组
通讯作者Lü, Guoliang; Chen XF(陈雪飞)
作者单位1.School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing, 101408, People's Republic of China;
2.Xinjiang Astronomical Observatory, Chinese Academy of Sciences, Urumqi, 830011, People's Republic of China;
3.School of Physical Science and Technology, Xinjiang University, Urumqi, 830046, People's Republic of China;
4.International Centre of Supernovae, Yunnan Observatories, Yunnan Key Laboratory of Supernova Research, Chinese Academy of Sciences, Kunming, 650216, People's Republic of China;
5.Key Laboratory for Structure and Evolution of Celestial Objects, Chinese Academy of Sciences, Kunming, 650216, People's Republic of China;
6.School of Physical Science and Technology, Guangxi Key Laboratory for Relativistic Astrophysics, Guangxi University, Nanning, 530004, People's Republic of China
推荐引用方式
GB/T 7714		 Shi HH,Huang, Zhenyang,Yan, Qiyu,et al. Application of interpretable data-driven methods for the reconstruction of supernova neutrino energy spectra following fast neutrino flavor conversions[J]. PHYSICS LETTERS B,2026,875.
APA 史海浩,Huang, Zhenyang,Yan, Qiyu,周均达,Lü, Guoliang,&陈雪飞.(2026).Application of interpretable data-driven methods for the reconstruction of supernova neutrino energy spectra following fast neutrino flavor conversions.PHYSICS LETTERS B,875.
MLA 史海浩,et al."Application of interpretable data-driven methods for the reconstruction of supernova neutrino energy spectra following fast neutrino flavor conversions".PHYSICS LETTERS B 875(2026).

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来源：云南天文台

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