A Machine Learning-based Source Property Inference for Compact Binary Mergers
A Machine Learning-based Source Property Inference for Compact Binary Mergers
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DOI：
10.3847/1538-4357/ab8dbe
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发表时间：
2020-06-01
影响因子：
4.9
通讯作者：
Pannarale, Francesco
中科院分区：
文献类型：
--
作者：
Chatterjee, Deep;Ghosh, Shaon;Pannarale, Francesco
双星中子星星合并的发现，GW 170817，是第一个成功的多信使观测紧凑的双星合并的故事。推断的合并率，沿着增加的灵敏度的地面引力波（GW）网络在目前的LIGO/处女座，和未来的LIGO/处女座/KAGRA观测运行，强烈暗示在检测的二进制文件，可能有一个电磁（EM）对应。快速评估可能导致对应物的性质对于帮助时间敏感的后续行动至关重要，特别是机器人望远镜。至少，对应物的可能性需要一颗中子星星（NS）。此外，潮汐破裂物理是重要的，以确定合并后的剩余物质，其动力学可能会导致对应物。然而，主要的挑战是，二元系统参数，如质量和自旋估计的实时，GW模板为基础的搜索，往往是由统计和系统误差为主。在这里，我们提出了一种方法，使用监督机器学习来减轻这种选择效应，以报告基于NS组件的存在的对应物的可能性，以及合并后残留物质的存在在真实的时间。
The detection of the binary neutron star merger, GW170817, was the first success story of multi-messenger observations of compact binary mergers. The inferred merger rate, along with the increased sensitivity of the ground-based gravitational-wave (GW) network in the present LIGO/Virgo, and future LIGO/Virgo/KAGRA observing runs, strongly hints at detections of binaries that could potentially have an electromagnetic (EM) counterpart. A rapid assessment of properties that could lead to a counterpart is essential to aid time-sensitive follow-up operations, especially robotic telescopes. At minimum, the possibility of counterparts requires a neutron star (NS). Also, the tidal disruption physics is important to determine the remnant matter post-merger, the dynamics of which could result in the counterparts. The main challenge, however, is that the binary system parameters, such as masses and spins estimated from the real-time, GW template-based searches, are often dominated by statistical and systematic errors. Here, we present an approach that uses supervised machine learning to mitigate such selection effects to report the possibility of counterparts based on the presence of an NS component, and the presence of remnant matter post-merger in real time.