A Neural Network Approach to Preferred Event Selection for Low-Latency Gravitational-Wave Alerts

TL;DR

This paper introduces a neural network method to select the most accurate gravitational-wave trigger for electromagnetic follow-up, outperforming traditional methods in localization accuracy and bias reduction, suitable for real-time applications.

Contribution

A novel neural network-based selector trained on simulated data that improves localization accuracy and reduces biases compared to traditional SNR and FAR-based methods.

Findings

Neural network achieves ~2% smaller searched area than SNR-based method.

The approach preserves pipeline fairness and avoids systematic biases.

Training and inference are fast, suitable for low-latency use.

Abstract

The LIGO-Virgo-KAGRA collaboration uses multiple independent search pipelines to detect gravitational waves, often resulting in multiple triggers (g-events) for a single astrophysical source. These triggers are grouped into superevents, raising a critical question for multimessenger astronomy: which g-event provides the most accurate sky localization for electromagnetic follow-up? Currently, the g-event with the highest signal-to-noise ratio (SNR) is selected, under the assumption that it should provide the best estimators of the source's parameters, including its location on the sky. Analysis of simulated signals reveals systematic deviations from this expectation. In particular, a false-alarm rate (FAR)-based selector performs slightly better than the SNR-based method, but introduces pipeline biases. We present a neural network-based selector trained on simulated signals to identify the g-event with the minimum searched area -- a metric quantifying localization accuracy. The network uses information (detector SNRs, FAR, and chirp mass) from all of the triggers associated with each astrophysical source and is designed to be pipeline-agnostic. Our results show that the neural network outperforms both traditional selectors, achieving a mean searched area ~2% smaller than the SNR-based selector. Unlike FAR-based selection, the neural network preserves the underlying distribution of pipeline contributions, avoiding systematic biases toward specific pipelines. The network can be trained in approximately one minute on a few thousand events and performs event selection instantaneously, making it suitable for low-latency applications. These results demonstrate that machine learning can enhance multimessenger astronomy capabilities while maintaining fairness across detection pipelines. We recommend implementing this approach for future observing runs.