New Neural Network Emulator Enhances Gravitational-Wave Detection Analysis

A recent paper titled "A neural network emulator of the Advanced LIGO and Advanced Virgo selection function" by Thomas A. Callister, Reed Essick, and Daniel E. Holz presents a new method for understanding gravitational-wave detection. The authors highlight the importance of accurately characterizing search selection effects in gravitational-wave data analysis, which is crucial for predicting future observational prospects and for statistical inference of astrophysical source populations from observed catalogs of compact binary mergers.

The traditional method of measuring gravitational-wave selection functions involves computationally intensive injection campaigns, where simulated signals are added to real data. However, this approach has limitations, particularly in its inability to interpolate between discrete injections, which restricts the study of narrow or discontinuous features in compact binary populations.

To address these challenges, the authors developed a neural network emulator that estimates the probability of detecting a compact binary based on its parameters, averaged over the third observing run (O3) of Advanced LIGO and Advanced Virgo. This emulator captures the dependence of detection probability on various factors, including binary masses, spins, distances, sky positions, and orbital orientations, and is applicable to compact binaries with component masses ranging from 1 to 100 solar masses.

The paper demonstrates the emulator's capability to produce accurate distributions of detectable events and its utility in hierarchical inference of binary black hole populations. The findings suggest that this new approach could significantly enhance the efficiency of gravitational-wave data analysis by providing a computationally cheaper and more versatile tool for evaluating selection functions across a continuous range of binary parameters.

This research is expected to have a substantial impact on future gravitational-wave surveys and the understanding of compact binary mergers. The full paper can be cited as follows: Callister, T. A., Essick, R., & Holz, D. E. (2024). A neural network emulator of the Advanced LIGO and Advanced Virgo selection function. arXiv:2408.16828.