利用拓扑数据分析和机器学习预测复发新星的爆发可预测性
Predictability of bursts of a recurrent nova using topological data analysis and machine learning
AI总结：
本文通过拓扑数据分析和机器学习方法，利用持续性图的特征化技术，预测RS Oph新星在一年内是否爆发，实验结果显示模型具有较高的召回率和准确率。
摘要：
RS Oph是一种复发新星，是一种显示约百年内爆发的 cataclismic 变量。持续同调，拓扑数据分析的一种技术，研究由数据点或其嵌入组成的 simplicial 复合体的拓扑特征演变，随着某些距离参数的变化。对于本工作，我训练了一个基于几种特征化方法的监督学习模型，包括持续性景观、Carlsson坐标、持续性图像和模板函数，这些方法用于RS Oph光变曲线各部分的持续性图。基于一种特征化方法（持续性景观）的模型在十折交叉验证中一致显示出高召回率和准确率。该方法旨在预测RS Oph在一年内是否爆发。
英文摘要：
RS Oph is a recurrent nova, a kind of cataclismic variable that shows bursts in a period approximately shorter than a century. Persistent homology, a technique from topological data analysis, studies the evolution of topological features of a simplicial complex composed of the data points or an embedding of them, as some distance parameter is varied. For this work I trained a supervised learning model based on several featurizations, namely persistence landscapes, Carlsson coordinates, persistent images, and template functions, of the persistence diagrams of sections of the lightcurve of RS Oph. A tenfold cross validation of the model based on one of the featurizations, persistence landscapes, consistently shows high recalls and accuracies. This method serves the purpose of predicting whether RS Oph is bursting within a year.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
