An optimal ${\chi }^2$ discriminator against modelled noise-transients in interferometric data in searches for binary black-hole mergers

Abstract

A vitally important requirement for detecting gravitational wave (GW) signals from compact coalescing binaries (CBC) with high significance is the reduction of the false-alarm rate of the matched-filter statistic. The data from GW detectors contain transient noise artifacts, or glitches, which adversely affect the performance of search algorithms by producing false alarms. Glitches with large amplitudes can produce triggers in the SNR time-series in spite of their small overlap with the templates. This contributes to false alarms. Historically, the traditional ${\chi }^2$ test has proved quite useful in distinguishing triggers arising from CBC signals and those caused by glitches. In a recent paper, a large class of unified ${\chi }^2$ discriminators was formulated, along with a procedure to construct an optimal ${\chi }^2$ discriminator, especially, when the glitches can be modeled. A large variety of glitches that often occur in GW detector data can be modeled as sine-Gaussians, with quality factor and central frequency, ($Q,f_0$), as parameters. We use Singular Value Decomposition to identify the most significant degrees of freedom in order to reduce the computational cost of our ${\chi }^2$. Finally, we construct a ${\chi }^2$ statistic that optimally discriminates between sine-Gaussian glitches and CBC signals. We also use Receiver-Operating-Characteristics to quantify the improvement in search sensitivity when it employs the optimal ${\chi }^2$ compared to the traditional ${\chi }^2$. The improvement in detection probability is by a few to several percentage points, near a false-alarm probability of a few times $10^{-3}$, and holds for binary black holes (BBHs) with component masses from several to a hundred solar masses. Moreover, the glitches that are best discriminated against are those that are like sine-Gaussians with $Q\in [25,50]$ and $f_0\in [40,80]$Hz.

arXiv comment17 pages, 5 figures, the abstract is shortened to comply with the arXiv's 1920 characters limitation

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Cited by in corpus (6)

• SiGMa-Net: Deep learning network to distinguish binary black hole signals from short-duration noise transients
• Hierarchical approach to matched filtering using a reduced basis
• Novel Deep Learning Approach to Detecting Binary Black Hole Mergers
• Improved binary black hole searches through better discrimination against noise transients
• Improved Binary Black Hole Search Discriminator from the Singular Value Decomposition of Non-Gaussian Noise Transients
• A ${\chi }^2$ statistic for the identification of strongly lensed gravitational waves from compact binary coalescences