Rapid determination of LISA sensitivity to extreme mass ratio inspirals with machine learning
Rapid determination of LISA sensitivity to extreme mass ratio inspirals with machine learning
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DOI：
10.1093/mnras/stad1397
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发表时间：
2022-12
影响因子：
4.8
通讯作者：
C. Chapman-Bird;C. Berry;G. Woan
中科院分区：
文献类型：
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作者：
C. Chapman-Bird;C. Berry;G. Woan
对恒星质量致密天体进入大质量黑洞（MBHs）的吸进的引力波观测，即极端质量比吸进（EMRIs），使精确测量MBH质量和自旋等参数成为可能。激光干涉仪空间天线预计将探测到足够的emri，以探测潜在的源群，测试MBHs及其环境的形成和演化理论。人口研究受到选择效应的影响，选择效应在整个EMRI参数空间中变化，如果未解释，则会导致偏差推断。这种偏差可以纠正，但是评估许多EMRI信号的可探测性在计算上是昂贵的。我们通过(i)构建一个快速准确的神经网络插值器，能够从其参数中预测EMRI的信噪比，以及（ii）使用学习选择函数的神经网络进一步加速可检测性估计，利用我们的第一个神经网络进行数据生成，从而降低了这一成本。由此产生的框架可以快速估计选择函数，从而在总体推断分析中充分处理EMRI可检测性。我们将我们的方法应用于一个天体物理学驱动的EMRI人口模型，展示了潜在的选择偏差，并随后对它们进行了纠正。考虑到选择效应，我们预测在116个EMRI检测中，LISA测量MBH质量函数斜率的精度为8.8%，CO质量函数斜率的精度为4.6%，MBH自旋星等分布宽度的精度为10%，事件率的精度为12%，EMRI在z = 6以下的红移。
Gravitational wave observations of the inspiral of stellar-mass compact objects into massive black holes (MBHs), extreme mass ratio inspirals (EMRIs), enable precision measurements of parameters such as the MBH mass and spin. The Laser Interferometer Space Antenna is expected to detect sufficient EMRIs to probe the underlying source population, testing theories of the formation and evolution of MBHs and their environments. Population studies are subject to selection effects that vary across the EMRI parameter space, which bias inference results if unaccounted for. This bias can be corrected, but evaluating the detectability of many EMRI signals is computationally expensive. We mitigate this cost by (i) constructing a rapid and accurate neural network interpolator capable of predicting the signal-to-noise ratio of an EMRI from its parameters, and (ii) further accelerating detectability estimation with a neural network that learns the selection function, leveraging our first neural network for data generation. The resulting framework rapidly estimates the selection function, enabling a full treatment of EMRI detectability in population inference analyses. We apply our method to an astrophysically-motivated EMRI population model, demonstrating the potential selection biases and subsequently correcting for them. Accounting for selection effects, we predict that with 116 EMRI detections LISA will measure the MBH mass function slope to a precision of 8.8%, the CO mass function slope to a precision of 4.6%, the width of the MBH spin magnitude distribution to a precision of 10% and the event rate to a precision of 12% with EMRIs at redshifts below z = 6.