Toward a computationally-efficient follow-up pipeline for blind continuous gravitational-wave searches

TL;DR

This paper introduces a new framework to evaluate and improve the efficiency of follow-up methods in continuous gravitational-wave searches, reducing computational costs and simplifying setup procedures for better sensitivity in future LIGO-Virgo-KAGRA data analyses.

Contribution

It presents a novel framework for assessing stochastic CW follow-ups, enabling significant cost reductions and simplified multistage follow-up setups without parameter-space metrics.

Findings

Reduced computational cost of pyfstat follow-up method

Simplified multistage follow-up setup without parameter-space metrics

Validated on Gaussian and real LIGO O3 data for various sources

Abstract

The sensitivity of continuous gravitational-wave (CW) searches for unknown neutron stars (NSs) is limited by their parameter space breadth. To fit within reasonable computing budgets, hierarchical schemes are used to identify interesting candidates using affordable methods. The resulting sensitivity depends on the number of candidates selected to follow-up. In this work, we present a novel framework to evaluate the effectiveness of stochastic CW follow-ups. Our results allow for a significant reduction of the computing cost of pyfstat, a well-established follow-up method. We also simplify the setup of multistage follow-ups by removing the need for parameter-space metrics. The study was conducted on Gaussian and real O3 Advanced LIGO data covering both isolated and binary sources. These results will have a positive impact on the sensitivity of all-sky searches in the forthcoming observing runs of the LIGO-Virgo-KAGRA collaboration.