Inferring the astrophysical population of gravitational wave sources in the presence of noise transients
Inferring the astrophysical population of gravitational wave sources in the presence of noise transients
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DOI：
10.1093/mnras/stad1823
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发表时间：
2023-04
影响因子：
4.8
通讯作者：
J. Heinzel;C. Talbot;G. Ashton;S. Vitale
中科院分区：
文献类型：
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作者：
J. Heinzel;C. Talbot;G. Ashton;S. Vitale
干涉引力波（GW）观测站全球网络（LIGO, Virgo， KAGRA）已经探测并描述了近100个二元致密天体的并合。然而，更多真实的GWs潜伏在阈值以下，需要从地面源噪声触发（称为小故障）中筛选出来。因为小故障不是由天体物理现象引起的，假设小故障有天体物理来源（例如双黑洞合并），对小故障的推断会导致源参数与已知的天体物理种群不一致。在这项工作中，我们展示了如何通过同时对其源种群进行贝叶斯推断，从真实GW事件和故障污染物的目录中提取无偏种群约束。在本文中，我们假设小故障来自具有良好特征的有效种群（小故障）的特定类。我们还计算了目录中属于天体物理或故障类的每个事件的概率的后验，并获得了目录中天体物理事件数量的后验，发现它与实际包含的事件数量一致。
The global network of interferometric gravitational wave (GW) observatories (LIGO, Virgo, KAGRA) has detected and characterized nearly 100 mergers of binary compact objects. However, many more real GWs are lurking sub-threshold, which need to be sifted from terrestrial-origin noise triggers (known as glitches). Because glitches are not due to astrophysical phenomena, inference on the glitch under the assumption it has an astrophysical source (e.g. binary black hole coalescence) results in source parameters that are inconsistent with what is known about the astrophysical population. In this work, we show how one can extract unbiased population constraints from a catalogue of both real GW events and glitch contaminants by performing Bayesian inference on their source populations simultaneously. In this paper, we assume glitches come from a specific class with a well-characterized effective population (blip glitches). We also calculate posteriors on the probability of each event in the catalogue belonging to the astrophysical or glitch class, and obtain posteriors on the number of astrophysical events in the catalogue, finding it to be consistent with the actual number of events included.