论文标题
使用机器学习来自动计算卡方测试进行引力波搜索
Using machine learning to auto-tune chi-squared tests for gravitational wave searches
论文作者
论文摘要
引力波搜索的敏感性通过检测器数据中的非高斯噪声而降低。这些非高斯通常与匹配过滤器搜索中使用的模板波形很好地匹配，并且需要信号一致性测试以将其与天体物理信号区分开。但是，从经验上调整这些测试以提高功效是耗时的，并限制了这些测试的复杂性。在这项工作中，我们展示了一个使用机器学习技术自动调整信号矛盾测试的框架。我们实施了一个新的$χ^2 $信号抗性测试，以针对搜索中间质量黑洞二进制的大量噪声，并使用本文规定的框架训练新的测试。我们发现，这种方法有效地训练了一个复杂的模型，以减轻噪声的重量，同时使信号种群相对不受影响。这将搜索的敏感性提高了$ \ sim 11 \％$对于具有质量$> 300 m_ \ odot $的信号。将来，该框架可用于在任何常用的匹配过滤器搜索算法中实现新测试，从而进一步提高我们的搜索敏感性。
The sensitivity of gravitational wave searches is reduced by the presence of non-Gaussian noise in the detector data. These non-Gaussianities often match well with the template waveforms used in matched filter searches, and require signal-consistency tests to distinguish them from astrophysical signals. However, empirically tuning these tests for maximum efficacy is time consuming and limits the complexity of these tests. In this work we demonstrate a framework to use machine-learning techniques to automatically tune signal-consistency tests. We implement a new $χ^2$ signal-consistency test targeting the large population of noise found in searches for intermediate mass black hole binaries, training the new test using the framework set out in this paper. We find that this method effectively trains a complex model to down-weight the noise, while leaving the signal population relatively unaffected. This improves the sensitivity of the search by $\sim 11\%$ for signals with masses $> 300 M_\odot$. In the future this framework could be used to implement new tests in any of the commonly used matched-filter search algorithms, further improving the sensitivity of our searches.
