论文标题
使用神经网络分类器的重力波选择效果
Gravitational-wave selection effects using neural-network classifiers
论文作者
论文摘要
我们提出了一种新型的机器学习方法，以估计重力波观测中的选择效果。使用类似于图像分类和模式识别的技术，我们训练一系列神经网络分类器，以预测紧凑型二进制合并的引力波信号的LIGO/处女座可检测性。我们包括自旋进动的效果，高阶模式和多个检测器，并表明它们的遗漏在大型人群研究中很常见，往往高估了参数空间选定区域中推断的合并率。尽管在这里我们使用简单的信噪比阈值训练分类器，但我们的方法准备与全管道注射结合使用，从而铺平了将天体物理和噪声触发器的实际分布铺平到重力波群体分析中的方式。
We present a novel machine-learning approach to estimate selection effects in gravitational-wave observations. Using techniques similar to those commonly employed in image classification and pattern recognition, we train a series of neural-network classifiers to predict the LIGO/Virgo detectability of gravitational-wave signals from compact-binary mergers. We include the effect of spin precession, higher-order modes, and multiple detectors and show that their omission, as it is common in large population studies, tends to overestimate the inferred merger rate in selected regions of the parameter space. Although here we train our classifiers using a simple signal-to-noise ratio threshold, our approach is ready to be used in conjunction with full pipeline injections, thus paving the way toward including actual distributions of astrophysical and noise triggers into gravitational-wave population analyses.
