arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新，休息一下吧，周末愉快~~
arXiv 2007.08391astro-ph.SR
收藏

基于可逆神经网络的光度学恒星参数确定

Stellar Parameter Determination from Photometry using Invertible Neural Networks

Victor F. Ksoll, Lynton Ardizzone, Ralf Klessen, Ullrich Koethe, Elena Sabbi, Massimo Robberto, Dimitrios Gouliermis, Carsten Rother, Peter Zeidler, Mario Gennaro

备注：
Accepted for Publication by MNRAS on 19. September, 41 pages, 48 figures, 2 tables
阅读 PDF
arXiv 页面 ↗HTML 原文 ↗

摘要：

利用哈勃太空望远镜进行光度测量使我们能够以高分辨率和深覆盖范围研究恒星星族，通常通过将测量数据与适当的恒星演化模型进行比较来获得组成恒星的物理参数估计值。这是一个非常不平凡的任务，由于影响，如微分消光，测光误差，低过滤器覆盖范围，或不确定性的恒星演化计算。这些引入了难以检测和破坏的简并。为了改善这种情况，我们引入了一种新的深度学习方法，称为条件可逆神经网络（cINN），以解决基于单个恒星的测光预测物理参数的逆问题，并获得完整的后验分布。我们建立了一个精心策划的合成训练数据集来自PARSEC的恒星演化模型来预测恒星的年龄，初始/当前质量，光度，有效温度和表面重力。我们从MIST和达特茅斯模型的合成数据进行测试，并基准我们的方法对两个研究良好的星团，Westerlund 2和NGC 6397的HST数据。对于合成数据，我们发现整体表现出色，并注意到年龄是最难约束的参数。对于基准集群，我们检索合理的结果，并确认以前的研究结果Westerlund 2的集群年龄（$1.04_{-0.90}^{+8.48}\,\mathrm{Myr} $），质量分离，恒星初始质量函数。对于NGC 6397，我们恢复了质量，光度和温度的合理估计，然而，恒星演化模型和观测之间的差异阻止了老恒星年龄的可接受的恢复。

英文摘要：

Photometric surveys with the Hubble Space Telescope (HST) allow us to study stellar populations with high resolution and deep coverage, with estimates of the physical parameters of the constituent stars being typically obtained by comparing the survey data with adequate stellar evolutionary models. This is a highly non-trivial task due to effects such as differential extinction, photometric errors, low filter coverage, or uncertainties in the stellar evolution calculations. These introduce degeneracies that are difficult to detect and break. To improve this situation, we introduce a novel deep learning approach, called conditional invertible neural network (cINN), to solve the inverse problem of predicting physical parameters from photometry on an individual star basis and to obtain the full posterior distributions. We build a carefully curated synthetic training data set derived from the PARSEC stellar evolution models to predict stellar age, initial/current mass, luminosity, effective temperature and surface gravity. We perform tests on synthetic data from the MIST and Dartmouth models, and benchmark our approach on HST data of two well-studied stellar clusters, Westerlund 2 and NGC 6397. For the synthetic data we find overall excellent performance, and note that age is the most difficult parameter to constrain. For the benchmark clusters we retrieve reasonable results and confirm previous findings for Westerlund 2 on cluster age ($1.04_{-0.90}^{+8.48}\,\mathrm{Myr} $), mass segregation, and the stellar initial mass function. For NGC 6397 we recover plausible estimates for masses, luminosities and temperatures, however, discrepancies between stellar evolution models and observations prevent an acceptable recovery of age for old stars.

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。

↑