Expanding RIFT: Improving performance for GW parameter inference

TL;DR

This paper enhances the RIFT algorithm for gravitational wave parameter inference, improving its efficiency, flexibility, and robustness, and demonstrates its effectiveness on LIGO/Virgo O3 data and selected events.

Contribution

It introduces algorithm enhancements and software extensions that increase RIFT's performance, flexibility, and robustness for gravitational wave data analysis.

Findings

RIFT's performance is improved with new algorithm enhancements.

The software demonstrates robustness across different configurations.

Analysis of LIGO/Virgo O3 data validates the improvements.

Abstract

The Rapid Iterative FiTting (RIFT) parameter inference algorithm provides a framework for efficient, highly-parallelized parameter inference for GW sources. In this paper, we summarize essential algorithm enhancements and operating point choices for the RIFT iterative algorithm, including choices used for analysis of LIGO/Virgo O3 observations. We also describe other extensions to the RIFT algorithm and software ecosystem. Some extensions increase RIFT's flexibility to produce outputs pertinent to GW astrophysics. Other extensions increase its computational efficiency or stability. Using many randomly-selected sources, we assess code robustness with two distinct code configurations, one designed to mimic settings as of LIGO O3 and another employing several performance enhancements. We illustrate RIFT's capabilities with analysis of selected events.