机器学习检测致密天体中的非轴对称快速味不稳定性
Machine Learning Detection of Non-Axisymmetric Fast Flavor Instabilities in Compact Objects
AI总结：
研究致密天体中中微子非轴对称快速味不稳定性，基于中微子电子轻子数角矩的输入特征，用机器学习方法检测。模型对多数测试数据集泛化性较好，人工非轴对称可提升性能，味平衡角分布下需额外特征改进模型，是相关模拟的关键一步。
AI中文摘要：
在诸如核心坍缩超新星（CCSNe）和中子星合并（NSMs）等致密天体物理环境中，中微子会经历FFCs，其可能在极小尺度上发展。FFCs发生的必要条件是中微子电子轻子数（ELN）角分布存在零交叉。本文基于$\nu_e$和$\bar\nu_e$的零阶和一阶角矩的输入特征，探索机器学习（ML）方法来检测这些环境中的非轴对称ELN交叉。ML模型对多种方法生成的大多数未见测试数据集具有较好的泛化性。对于一维CCSNe背景下通过求解离散玻尔兹曼输运方程得到的轴对称分布数据集，模型性能一般，施加人工非轴对称可显著提高性能。对于味平衡角分布，仅基于ELN输入训练的ML模型在真实交叉依赖于重轻子中微子和反中微子的平衡后角分布时性能不佳，但去除重轻子中微子和反中微子分布后，在检测ELN交叉时表现出色。这凸显了需要额外输入特征来进一步改进模型，这是将FFCs成功整合到大规模CCSNe和NSM模拟中的关键一步。
英文摘要：
Neutrinos in dense astrophysical environments such as core-collapse supernovae (CCSNe) and neutron star mergers (NSMs) can undergo FFCs, which could develop on extremely small scales. A necessary condition for the occurrence of FFCs is the presence of a zero crossing in the electron lepton number (ELN) angular distribution of neutrinos. In this work, we explore machine learning (ML) approaches to detect non-axisymmetric ELN crossings in these environments, based on input features of the $ν_e$ and $\barν_e$ zeroth and first angular moments. Overall, the ML models demonstrate relatively good generalizability for most of the unseen test datasets generated by various methods that do not assume the same underlying angular distributions as used in the training set. Interestingly, while the model's performance is mediocre for an axisymmetric distribution dataset derived by solving the discretized Boltzmann transport equation under 1D CCSN background, imposing an artificial non-axisymmetry substantially improves the performance. We also find that for the flavor-equilibrated angular distributions, although our ML model trained based solely on ELN inputs performs poorly when the true crossings depend on the post-equilibrated angular distributions of heavy lepton neutrinos and antineutrinos, which become different, it delivers strong performance in detecting ELN crossings when the heavy-lepton neutrino and antineutrino distributions are artificially removed. This highlights the need for additional input features to further improve the model. This is a crucial step toward successfully integrating FFCs into large-scale CCSN and NSM simulations.
