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

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 $χ^2$ 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 $χ^2$ discriminators was formulated, along with a procedure to construct an optimal $χ^2$ 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 $χ^2$. Finally, we construct a $χ^2$ 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 $χ^2$ compared to the traditional $χ^2$. 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.