Kavli Affiliate: Daniel E. Holz
| First 5 Authors: Thomas A. Callister, Reed Essick, Daniel E. Holz, ,
| Summary:
Characterization of search selection effects comprises a core element of
gravitational-wave data analysis. Knowledge of selection effects is needed to
predict observational prospects for future surveys and is essential in the
statistical inference of astrophysical source populations from observed
catalogs of compact binary mergers. Although gravitational-wave selection
functions can be directly measured via injection campaigns — the insertion and
attempted recovery of simulated signals added to real instrumental data — such
efforts are computationally expensive. Moreover, the inability to interpolate
between discrete injections limits the ability to which we can study narrow or
discontinuous features in the compact binary population. For this reason, there
is a growing need for alternative representations of gravitational-wave
selection functions that are computationally cheap to evaluate and can be
computed across a continuous range of compact binary parameters. In this paper,
we describe one such representation. Using pipeline injections performed during
Advanced LIGO & Advanced Virgo’s third observing run (O3), we train a neural
network emulator for $P(mathrm{det}|theta)$, the probability that given a
compact binary with parameters is successfully detected, averaged over the
course of O3. The emulator captures the dependence of $P(mathrm{det}|theta)$
on binary masses, spins, distance, sky position, and orbital orientation, and
it is valid for compact binaries with components masses between
$1$–$100,M_odot$. We test the emulator’s ability to produce accurate
distributions of detectable events, and demonstrate its use in hierarchical
inference of the binary black hole population.
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