Asimov: A framework for coordinating parameterestimation workflows

Abstract

Since the first detection in 2015 of gravitational waves from compact binary coalescence (B. P.
\nAbbott & others, 2016a), improvements to the Advanced LIGO and Advanced Virgo detectors
\nhave expanded our view into the universe for these signals. Searches of the latest observing
\nrun (O3) have increased the number of detected signals to 90, at a rate of approximately 1 per
\nweek (The LIGO Scientific Collaboration, the Virgo Collaboration, Abbott, et al., 2021; The
\nLIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, et al., 2021).
\nFuture observing runs are expected to increase this even further(B. P. Abbott & others, 2020).
\nBayesian analysis of the signals can reveal the properties of the coalescing black holes and
\nneutron stars by comparing predicted waveforms to the observed data (B. P. Abbott & others,
\n2016b). The proliferating number of detected signals, the increasing number of methods that
\nhave been deployed (Ashton & others, 2019; Lange et al., 2018; Veitch & others, 2015), and
\nthe variety of waveform models (Khan et al., 2020; Ossokine & others, 2020; Pratten & others,
\n2021) create an ever-expanding number of analyses that can be considered.
\nAsimov is a python package which is designed to simplify and standardise the process of
\nconfiguring these analyses for a large number of events. It has already been used in developing
\nanalyses in three major gravitational wave catalog publications (R. Abbott & others, 2021;
\nThe LIGO Scientific Collaboration, the Virgo Collaboration, Abbott, et al., 2021; The LIGO
\nScientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, et al., 2021). The
\nsource code of Asimov is archived to Zenodo (Williams et al., 2021).

Topics & Concepts