论文标题
区分双中性之星和具有重力波观测的中子星 - 黑孔二元种群
Distinguishing double neutron star from neutron star-black hole binary populations with gravitational wave observations
论文作者
论文摘要
来自两个中子星的合并的重力波不能很容易地与可比质量混合二进制产生的引力波区分开，其中一个同伴是黑洞。低质量的黑洞很有趣，因为它们可以在两个中子星的聚结中形成，这是由于巨大恒星的崩溃，来自原始宇宙中的物质过度或作为中子恒星与暗物质之间相互作用的结果。引力波通过所谓的潮汐变形参数带有中子恒星内部组成的烙印，该参数取决于状态的恒星方程，而黑洞的重量为零。我们提出了一种新的数据分析策略，该策略由贝叶斯推理和机器学习提供动力，以识别混合二进制文件，因此使用从重力波观测到的潮汐变形性参数的分布，因此低质量黑洞。
Gravitational waves from the merger of two neutron stars cannot be easily distinguished from those produced by a comparable-mass mixed binary in which one of the companions is a black hole. Low-mass black holes are interesting because they could form in the aftermath of the coalescence of two neutron stars, from the collapse of massive stars, from matter overdensities in the primordial Universe, or as the outcome of the interaction between neutron stars and dark matter. Gravitational waves carry the imprint of the internal composition of neutron stars via the so-called tidal deformability parameter, which depends on the stellar equation of state and is equal to zero for black holes. We present a new data analysis strategy powered by Bayesian inference and machine learning to identify mixed binaries, hence low-mass black holes, using the distribution of the tidal deformability parameter inferred from gravitational-wave observations.
