Incorporating information from LIGO data quality streams into the PyCBC search for gravitational waves
Incorporating information from LIGO data quality streams into the PyCBC search for gravitational waves
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DOI：
10.1103/physrevd.106.102006
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发表时间：
2022-04
影响因子：
5
通讯作者：
D. Davis;M. Trevor;S. Mozzon;L. Nuttall
中科院分区：
文献类型：
--
作者：
D. Davis;M. Trevor;S. Mozzon;L. Nuttall
我们提出了一种新的方法，该方法可以解释PyCBC搜索紧致双联长引力波时引力波探测器噪声特性随时间的变化。我们使用来自LIGO数据质量流的信息来监测每个探测器及其环境的状态，以模拟每个探测器中噪声率的变化。这些数据质量流允许在检测器故障期间在数据中识别的候选数据更有效地作为噪声被拒绝。这种方法允许机器学习预测探测器状态的数据作为PyCBC搜索的一部分，将可探测到的引力波信号总数增加了5%。当使用机器学习分类和手动生成的标记来搜索LIGO-Virgo第三次观测运行的数据时，与不使用任何数据质量流相比，可探测的引力波信号总数增加了20%。我们还展示了这种方法如何足够灵活，可以包含来自大量其他任意数据流的信息，这些数据流可能会进一步提高搜索的灵敏度。
We present a new method which accounts for changes in the properties of gravitational-wave detector noise over time in the PyCBC search for gravitational waves from compact binary coa-lescences. We use information from LIGO data quality streams that monitor the status of each detector and its environment to model changes in the rate of noise in each detector. These data quality streams allow candidates identified in the data during periods of detector malfunctions to be more efficiently rejected as noise. This method allows data from machine learning predictions of the detector state to be included as part of the PyCBC search, increasing the the total number of detectable gravitational-wave signals by up to 5%. When both machine learning classifications and manually-generated flags are used to search data from LIGO-Virgo’s third observing run, the total number of detectable gravitational-wave signals is increased by up to 20% compared to not using any data quality streams. We also show how this method is flexible enough to include information from large numbers of additional arbitrary data streams that may be able to further increase the sensitivity of the search.