GERry: A Code to Optimise the Hunt for the Electromagnetic Counter-parts to Gravitational Wave Events

TL;DR

GERry is a new software tool designed to optimize the search strategies for electromagnetic counterparts to gravitational wave events by analyzing survey parameters and localizations.

Contribution

It introduces GERry, a code that quantifies and optimizes observing campaigns for detecting electromagnetic counterparts to gravitational waves.

Findings

GERry effectively assesses survey performance and parameter space.

It helps identify promising sources and refine search strategies.

Demonstrated with current and upcoming wide-field surveys.

Abstract

The search for the electromagnetic counterparts to gravitational wave (GW) events has been rapidly gathering pace in recent years thanks to the increasing number and capabilities of both gravitational wave detectors and wide field survey telescopes. Difficulties remain, however, in detecting these counterparts due to their inherent scarcity, faintness and rapidly evolving nature. To find these counterparts, it is important that one optimises the observing strategy for their recovery. This can be difficult due to the large number of potential variables at play. Such follow-up campaigns are also capable of detecting hundreds or potentially thousands of unrelated transients, particularly for GW events with poor localisation. Even if the observations are capable of detecting a counterpart, finding it among the numerous contaminants can prove challenging. Here we present the Gravitational wave Electromagnetic RecovRY code (GERry) to perform detailed analysis and survey-agnostic quantification of observing campaigns attempting to recover electromagnetic counterparts. GERry considers the campaign's spatial, temporal and wavelength coverage, in addition to Galactic extinction and the expected counterpart light curve evolution from the GW 3D localisation volume. It returns quantified statistics that can be used to: determine the probability of having detected the counterpart, identify the most promising sources, and assess and refine strategy. Here we demonstrate the code to look at the performance and parameter space probed by current and upcoming wide-field surveys such as GOTO & VRO.