Asimov: A framework for coordinating parameter estimation workflows

TL;DR

Asimov is a Python framework that streamlines and standardizes the configuration of Bayesian gravitational wave analyses across many events, facilitating large-scale data processing and reproducibility.

Contribution

It introduces a new Python package, Asimov, that simplifies and standardizes the setup of parameter estimation workflows for multiple gravitational wave events.

Findings

Used in three major gravitational wave catalog publications

Reduces complexity in configuring analyses for numerous signals

Enhances reproducibility and efficiency in gravitational wave data analysis

Abstract

Since the first detection in 2015 of gravitational waves from compact binary coalescence, improvements to the Advanced LIGO and Advanced Virgo detectors have expanded our view into the universe for these signals. Searches of the of the latest observing run (O3) have increased the number of detected signals to 90, at a rate of approximately 1 per week. Future observing runs are expected to increase this even further. Bayesian analysis of the signals can reveal the properties of the coalescing black holes and neutron stars by comparing predicted waveforms to the observed data. The proliferating number of detected signals, the increasing number of methods that have been deployed, and the variety of waveform models create an ever-expanding number of analyses that can be considered. Asimov is a python package which is designed to simplify and standardise the process of configuring these analyses for a large number of events. It has already been used in developing analyses in three major gravitational wave catalog publications.