New Mass Estimates for Massive Binary Systems: A Probabilistic Approach Using Polarimetric Radiative Transfer

Author:

Fullard Andrew G.ORCID,O’Brien John T.ORCID,Kerzendorf Wolfgang E.ORCID,Shrestha ManishaORCID,Hoffman Jennifer L.ORCID,Ignace RichardORCID,van der Smagt PatrickORCID

Abstract

Abstract Understanding the evolution of massive binary stars requires accurate estimates of their masses. This understanding is critically important because massive star evolution can potentially lead to gravitational-wave sources such as binary black holes or neutron stars. For Wolf–Rayet (WR) stars with optically thick stellar winds, their masses can only be determined with accurate inclination angle estimates from binary systems which have spectroscopic M sin i measurements. Orbitally phased polarization signals can encode the inclination angle of binary systems, where the WR winds act as scattering regions. We investigated four Wolf–Rayet + O star binary systems, WR 42, WR 79, WR 127, and WR 153, with publicly available phased polarization data to estimate their masses. To avoid the biases present in analytic models of polarization while retaining computational expediency, we used a Monte Carlo radiative-transfer model accurately emulated by a neural network. We used the emulated model to investigate the posterior distribution of the parameters of our four systems. Our mass estimates calculated from the estimated inclination angles put strong constraints on existing mass estimates for three of the systems, and disagree with the existing mass estimates for WR 153. We recommend a concerted effort to obtain polarization observations that can be used to estimate the masses of WR binary systems and increase our understanding of their evolutionary paths.

Publisher

American Astronomical Society

Subject

Space and Planetary Science,Astronomy and Astrophysics

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. 1991T-Like Type Ia Supernovae as an Extension of the Normal Population;The Astrophysical Journal;2024-03-26

2. Colliding winds in WR21 and WR31 – I. The X-ray view;Monthly Notices of the Royal Astronomical Society;2023-09-06

3. Exploring the polarization of axially symmetric supernovae with unsupervised deep learning;Monthly Notices of the Royal Astronomical Society;2023-08-31

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3