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High-performance statistical methods for reactor neutrino oscillations.

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Title: High-performance statistical methods for reactor neutrino oscillations.
Authors: Xue, Jingqin1,2 (AUTHOR) xuejingqin@ihep.ac.cn, Zhang, Han1,2 (AUTHOR), Shen, Hongfang1 (AUTHOR), Sun, Guangbao1,3 (AUTHOR), Li, Dian1,2 (AUTHOR), Fan, Liangqianjin1,2 (AUTHOR), Yao, Haifeng1,2 (AUTHOR), Zhan, Liang1,2 (AUTHOR), Zhou, Xiang3 (AUTHOR), Ding, Xuefeng1,2 (AUTHOR) dingxf@ihep.ac.cn
Source: European Physical Journal C -- Particles & Fields. Dec2025, Vol. 85 Issue 12, p1-10. 10p.
Subjects: Neutrino oscillation, Software frameworks, Mathematical statistics, Neutrino interactions, Spectrum analysis instruments, Sensitivity analysis, Neutrinos
Abstract: We present a PyTorch-based framework for forward folded reactor neutrino spectrum fitting that accelerates the two main bottlenecks: IBD mapping and detector response, using (i) result caching, (ii) banded sparse matrices, and (iii) blocked construction of the response. On an Intel Xeon Gold 6338 CPU, these techniques reduce per-fit walltime by ≈ 7 × (median over 5 runs) relative to a dense, unoptimized implementation, with < 10 - 6 relative spectral error versus a double-precision baseline. The framework has been applied to reactor-neutrino oscillation analyses and is reusable in other neutrino experiments that rely on forward-folded energy spectra, enabling practical Feldman–Cousins coverage studies and large parameter scans at substantially lower computational cost. [ABSTRACT FROM AUTHOR]
Copyright of European Physical Journal C -- Particles & Fields is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: High-performance statistical methods for reactor neutrino oscillations.
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  Data: We present a PyTorch-based framework for forward folded reactor neutrino spectrum fitting that accelerates the two main bottlenecks: IBD mapping and detector response, using (i) result caching, (ii) banded sparse matrices, and (iii) blocked construction of the response. On an Intel Xeon Gold 6338 CPU, these techniques reduce per-fit walltime by ≈ 7 &#215; (median over 5 runs) relative to a dense, unoptimized implementation, with &lt; 10 - 6 relative spectral error versus a double-precision baseline. The framework has been applied to reactor-neutrino oscillation analyses and is reusable in other neutrino experiments that rely on forward-folded energy spectra, enabling practical Feldman–Cousins coverage studies and large parameter scans at substantially lower computational cost. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;Copyright of European Physical Journal C -- Particles &amp; Fields is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1140/epjc/s10052-025-15164-z
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      – Code: eng
        Text: English
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        PageCount: 10
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    Subjects:
      – SubjectFull: Neutrino oscillation
        Type: general
      – SubjectFull: Software frameworks
        Type: general
      – SubjectFull: Mathematical statistics
        Type: general
      – SubjectFull: Neutrino interactions
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      – SubjectFull: Spectrum analysis instruments
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      – SubjectFull: Sensitivity analysis
        Type: general
      – SubjectFull: Neutrinos
        Type: general
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      – TitleFull: High-performance statistical methods for reactor neutrino oscillations.
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              M: 12
              Text: Dec2025
              Type: published
              Y: 2025
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