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arXiv 2409.07519gr-qcastro-ph.HE
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哪个是哪个?引力波双星中两个致密天体的识别

Which is which? Identification of the two compact objects in gravitational-wave binaries

Davide Gerosa, Viola De Renzis, Federica Tettoni, Matthew Mould, Alberto Vecchio, Costantino Pacilio

AI总结:

针对引力波双星中致密天体识别的标记系统误差问题,提出基于后验分布的约束聚类半监督机器学习方法,使黑洞自旋测量精度提升50%,并促进天体性质识别。

摘要:

迄今为止,在引力波天文学中观测到的致密天体总是成对出现,从未单独出现。识别双星系统的两个成分是一项精细的操作,往往被视为理所当然。标记过程(即哪个是“1”号天体,哪个是“2”号天体)在引力波数据推断中实际上起到了系统性误差的作用,或者等价地,充当了一个未明确指定的先验。常见的方法是在逐个样本的基础上,仅凭质量来标记天体。我们表明,天体识别转而可以使用整个后验分布来解决。我们将该问题表述为约束聚类——一种半监督机器学习——并发现展开标记系统误差可以显著影响,且可以说改善我们对数据的解释。具体而言,黑洞自旋测量的精度提高了50%,多模态和尾部趋于消失,后验分布更接近高斯分布,并且有助于识别天体的性质(即黑洞与中子星)。我们估计约10%的 LIGO/Virgo 后验样本受此重新标记的影响,即它们可能被归因于观测双星中的另一个致密天体。

英文摘要:

Compact objects observed in gravitational-wave astronomy so far always come in pairs and never individually. Identifying the two components of a binary system is a delicate operation that is often taken for granted. The labeling procedure (i.e., which is object "1" and which is object "2") effectively acts as systematics, or, equivalently an unspecified prior, in gravitational-wave data inference. The common approach is to label the objects solely by their masses, on a sample-by-sample basis. We show that object identification can instead be tackled using the posterior distribution as a whole. We frame the problem in terms of constrained clustering -- a flavor of semi-supervised machine learning -- and find that unfolding the labeling systematics can significantly impact, and arguably improve, our interpretation of the data. In particular, the precision of black-hole spin measurements improves by up to 50%, multimodalities and tails tend to disappear, posteriors become closer to Gaussian distributions, and the identification of the nature of the object (i.e. black hole vs. neutron star) is facilitated. We estimate that about 10% of the LIGO/Virgo posterior samples are affected by this relabeling, i.e. they might have been attributed to the other compact object in the observed binaries.

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。

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