Calibrating signal-to-noise ratio detection thresholds using gravitational-wave catalogs

Understand

Searching for gravitational-wave signals is a challenging and computationally intensive endeavor undertaken by multiple independent analysis pipelines.

• While detection depends only on observed noisy data, it is sometimes inconsistently defined in terms of source parameters that in reality are unknown, e.g., by placing a threshold on the optimal signal-to-noise ratio (SNR).
• We present a method to calibrate unphysical thresholds to search results by performing Bayesian inference on real observations using a model that simultaneously parametrizes the intrinsic network optimal SNR distribution and the effect of search sensitivity on it.
• We find consistency with a fourth-order power law and detection thresholds of $10.5_{-2.4}^{+2.1}$, $11.2_{-1.4}^{+1.2}$, and $9.1_{-0.5}^{+0.5}$ (medians and $90\%$ credible intervals) for events with false-alarm rates less than $1\,\mathrm{yr}^{-1}$ in the first, second, and third LIGO-Virgo-KAGRA observing runs, respectively.