LensingFlow: An Automated Workflow for Gravitational Wave Lensing Analyses

TL;DR

LensingFlow is an automated, scalable workflow designed to detect gravitational lensing in large gravitational wave datasets, integrating existing pipelines with metadata management for reproducibility and efficiency.

Contribution

It introduces an automated, extensible workflow framework for gravitational wave lensing analysis, combining multiple pipelines and prioritization for large-scale studies.

Findings

Successfully identified lensing candidates in mock data

Automated workflow enhances reproducibility and scalability

Integrates multiple analysis pipelines and prioritization system

Abstract

In this work, we present LensingFlow. This is an implementation of an automated workflow to search for evidence of gravitational lensing in a large series of gravitational wave events. This workflow conducts searches for evidence in all generally considered lensing regimes. The implementation of this workflow is built atop the Asimov automation framework and CBCFlow metadata management software and the resulting product therefore encompasses both the automated running and status checking of jobs in the workflow as well as the automated production and storage of relevant metadata from these jobs to allow for later reproduction. This workflow encompasses a number of existing lensing pipelines and has been designed to accommodate any additional future pipelines to provide both a current and future basis on which to conduct large scale lensing analyses of gravitational wave signal catalogues. The workflow also implements a prioritisation management system for jobs submitted to the schedulers in common usage in computing clusters ensuring both the completion of the workflow across the entire catalogue of events as well as the priority completion of the most significant candidates. As a first proof-of-concept demonstration, we deploy LensingFlow on a mock data challenge comprising 10 signals in which signatures of each lensing regime are represented. LensingFlow successfully ran and identified the candidates from this data through its automated checks of results from consituent analyses.