AI 中文总结
本研究对比9种算法分析22年超级神冈太阳$^8\rm{B}$中微子数据，建立周期搜索多指标框架，为下一代中微子观测站提供方法蓝图，还揭示了38.8天信号的瞬态性及24.3天信号的系统误差起源。
AI 中文摘要
太阳$^8\rm{B}$中微子是探测太阳内部动力学及中微子电磁性质的独特探针。我们对1996-2018年的22年超级神冈（Super-Kamiokande）太阳中微子数据集开展了系统的多方法周期图分析，对比了9种算法。通过分层时间分段，我们将天体物理信号与探测器系统误差分离开来。广义隆布-斯卡格尔（Generalized Lomb-Scargle, GLS）方法能正确处理异方差不确定性，提供统计上最稳健的探测结果，而经典隆布-斯卡格尔（classical Lomb-Scargle）方法会系统性低估显著性。拉夫勒-金曼（Lafler--Kinman）方法通常失效，而MHAOV和PDM1等独立算法能恢复一致的周期性，提供重要的交叉验证。在2001年之前及SK-I数据中，7种算法提供了约38.8天周期性的弱证据（对数贝叶斯因子$\rm{ln}\textit{B} > 0$），但该信号在统计量最高的SK-IV修正流量数据中完全消失，此时贝叶斯因子明确支持原假设（$\rm{ln}\textit{B} \textit{≪} -5$），表明它是早期低统计量时代的瞬态特征。相反，2001年后原始流量中约24.3天的信号被贝叶斯框架明确排除，且在修正流量中消失，证实其为季节系统误差起源。此外，未发现约11年太阳周期调制的证据，得出严格的振幅上限为小于平均流量的0.2%。通过突出低信噪比下频率主义显著性与贝叶斯模型选择（$\rm{ln}\textit{B}$）的显著差异，我们建立了用于周期性搜索的严格多指标最佳实践框架。本工作为下一代观测站如超级神冈（Hyper-Kamiokande）和江门中微子实验（JUNO）提供了直接的方法蓝图。
英文摘要
Solar $^8\mathrm{B}$ neutrinos offer a unique probe of solar interior dynamics and neutrino electromagnetic properties. We present a systematic, multi-method periodogram analysis of the 22-year Super-Kamiokande solar neutrino dataset (1996--2018), comparing nine algorithms. Through hierarchical temporal segmentation, we disentangle astrophysical signals from detector systematics. The Generalized Lomb-Scargle (GLS) method provides the most statistically robust detections by correctly handling heteroscedastic uncertainties, whereas classical Lomb-Scargle systematically underestimates significance. The Lafler--Kinman method generally fails, whereas independent algorithms like MHAOV and PDM1 recover consistent periodicities, providing vital cross-validation. In pre-2001 and SK-I data, seven algorithms provide \textit{weak evidence} ($\ln B > 0$) for a $\sim 38.8$ d periodicity. However, this signal is entirely absent in the highest-statistics SK-IV modified flux data, where the Bayes factor decisively favors the null model ($\ln B \ll -5$), indicating it is a transient feature of the early low-statistics era. Conversely, a $\sim 24.3$ d signal in post-2001 raw flux is decisively rejected by the Bayesian framework and vanishes in modified flux, confirming its seasonal systematic origin. Furthermore, no evidence is found for an $\sim 11$-year solar cycle modulation, yielding a stringent amplitude upper limit of $<0.2\%$ of the mean flux. By highlighting the stark contrast between frequentist significance and Bayesian model selection ($\ln B$) in low signal-to-noise regimes, we establish a rigorous, multi-metric best-practice framework for periodicity searches. This work provides a direct methodological blueprint for next-generation observatories like Hyper-Kamiokande and JUNO.
Comments42 pages,24 figures,3 tables,submitted to JCAP,comments welcome
