Using machine learning to autotune chi-squared tests for gravitational wave searches

@article{McIsaac2022UsingML,
  title={Using machine learning to autotune chi-squared tests for gravitational wave searches},
  author={Connor McIsaac and Ian Harry},
  journal={Physical Review D},
  year={2022}
}
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… 

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