用粗粒似然探测极端引力波事件
Probing Extremal Gravitational-Wave Events with Coarse-Grained Likelihoods
摘要：
随着引力波瞬变目录的增长，迄今为止观测到的最极端系统都创下了新的记录。观测到的最大质量的黑洞探测了对不稳定性超新星的物理学，同时提供了关于二元黑洞系统组装环境的线索。与此同时，质量最小的黑洞使我们能够研究所谓的中子星-黑洞质量间隙，具有异常不对称质量比或大自旋的双星使我们了解双星和恒星演化。现有的离群值检验通常执行留一分析，但这些分析没有考虑到被排除的事件根据定义是总体中的极端成员。这会导致离群值评价的偏倚。我们通过引入一个粗粒度的框架来研究这些极端事件是否是真正的离群值，或者它们是否与观察到的其他人群一致，从而纠正了这种偏差。我们的方法使我们能够研究极端事件，同时测试人口模型的误设定。我们表明，这改善了偏见，目前在留一出分析中常用的引力波社区。将我们的方法应用于第二个LIGO-Virgo瞬态目录的结果，我们发现与Abbott等人，ApJL 913 L7（2021）的结论定性一致。GW 190814是一个异常值，因为它的次级质量很小。我们发现GW 190412和GW 190521都不是异常值。
英文摘要：
As catalogs of gravitational-wave transients grow, new records are set for the most extreme systems observed to date. The most massive observed black holes probe the physics of pair instability supernovae while providing clues about the environments in which binary black hole systems are assembled. The least massive black holes, meanwhile, allow us to investigate the purported neutron star-black hole mass gap, and binaries with unusually asymmetric mass ratios or large spins inform our understanding of binary and stellar evolution. Existing outlier tests generally implement leave-one-out analyses, but these do not account for the fact that the event being left out was by definition an extreme member of the population. This results in a bias in the evaluation of outliers. We correct for this bias by introducing a coarse-graining framework to investigate whether these extremal events are true outliers or whether they are consistent with the rest of the observed population. Our method enables us to study extremal events while testing for population model misspecification. We show that this ameliorates biases present in the leave-one-out analyses commonly used within the gravitational-wave community. Applying our method to results from the second LIGO--Virgo transient catalog, we find qualitative agreement with the conclusions of Abbott et al, ApJL 913 L7 (2021). GW190814 is an outlier because of its small secondary mass. We find that neither GW190412 nor GW190521 are outliers.
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