arXiv 2201.111262022-01-26astro-ph.IMgr-qc

基于深度学习的二元黑洞合并质量参数快速估计

Rapid Mass Parameter Estimation of Binary Black Hole Coalescences Using Deep Learning

Alistair McLeod, Daniel Jacobs, Chayan Chatterjee, Linqing Wen, Fiona Panther

备注：: 17 pages, 16 figures

摘要：

深度学习可用于大幅减少在引力波（GW）中检测到的致密物体（包括黑洞和中子星）的合并双星的参数估计的处理时间。作为第一步，我们提出了两个神经网络模型训练，以快速估计后验分布的啁啾质量和质量比的检测到的二元黑洞系统从GW应变数据的LIGO汉福德和利文斯顿天文台。利用这些参数可以预测组分质量，这对预测合并中包含中子星的可能性有影响。结果进行比较的“黄金标准”的参数估计的引力波使用的LIGO-Virgo合作（LVC），LALInference。我们的模型预测的后验分布与来自LALInference的一致，同时一旦模型被训练，则使用数量级更少的处理时间。中值预测是在90%的可信区间的LALInference的所有预测参数进行测试时，真正的二元黑洞事件检测LVC的第一次和第二次观测运行。我们认为，深度学习在低延迟高精度参数估计方面具有很强的潜力，适用于实时GW搜索管道。

英文摘要：

Deep learning can be used to drastically decrease the processing time of parameter estimation for coalescing binaries of compact objects including black holes and neutron stars detected in gravitational waves (GWs). As a first step, we present two neural network models trained to rapidly estimate the posterior distributions of the chirp mass and mass ratio of a detected binary black hole system from the GW strain data of LIGO Hanford and Livingston Observatories. Using these parameters the component masses can be predicted, which has implications for the prediction of the likelihood that a merger contains a neutron star. The results are compared to the 'gold standard' of parameter estimation of gravitational waves used by the LIGO-Virgo Collaboration (LVC), LALInference. Our models predict posterior distributions consistent with that from LALInference while using orders of magnitude less processing time once the models are trained. The median predictions are within the 90% credible intervals of LALInference for all predicted parameters when tested on real binary black hole events detected during the LVC's first and second observing runs. We argue that deep learning has strong potential for low-latency high-accuracy parameter estimation suitable for real-time GW search pipelines.

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