论文标题
机器学习在预测核心爆发超新星爆炸结果中的应用
Applications of Machine Learning to Predicting Core-collapse Supernova Explosion Outcomes
论文作者
论文摘要
大多数现有的标准源自核心偏离超新星的祖细胞特性，在预测爆炸结果方面不是很准确。我们介绍了一种新颖的外观，以使用机器学习方法来识别核心折叠超新星的爆炸结果。由100个2D轴对称超新星模拟的样品随着Fornax演变而成的样本，我们训练并评估随机森林分类器作为爆炸预测指标。此外，我们研究了基于物理的特征集，包括紧凑性参数，ERTL条件和一个新开发的表征硅/氧界面的集合。我们还从9 $ -27 m $ _ {\ odot} $的1500多个超新星祖细胞中，我们还训练自动编码器，以直接从祖细胞密度概要文件中提取物理 - 无术特征。我们发现，仅密度曲线包含有关其爆炸性的有意义信息。硅/氧和自动编码器功能都以$ 90 \％的精度预测爆炸结果。为了预期更大的多维仿真集，我们确定了未来的方向，即机器学习应用程序将在爆炸结果预测之外有用。
Most existing criteria derived from progenitor properties of core-collapse supernovae are not very accurate in predicting explosion outcomes. We present a novel look at identifying the explosion outcome of core-collapse supernovae using a machine learning approach. Informed by a sample of 100 2D axisymmetric supernova simulations evolved with Fornax, we train and evaluate a random forest classifier as an explosion predictor. Furthermore, we examine physics-based feature sets including the compactness parameter, the Ertl condition, and a newly developed set that characterizes the silicon/oxygen interface. With over 1500 supernovae progenitors from 9$-$27 M$_{\odot}$, we additionally train an auto-encoder to extract physics-agnostic features directly from the progenitor density profiles. We find that the density profiles alone contain meaningful information regarding their explodability. Both the silicon/oxygen and auto-encoder features predict explosion outcome with $\approx$90\% accuracy. In anticipation of much larger multi-dimensional simulation sets, we identify future directions in which machine learning applications will be useful beyond explosion outcome prediction.
