LensingFlow: An Automated Workflow for Gravitational Wave Lensing Analyses
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| author | Wright, Mick Janquart, Justin Cremonese, Paolo Chan, Juno C. L. Li, Alvin K. Y. Hannuksela, Otto A. Lo, Rico K. L. Ezquiaga, Jose M. Williams, Daniel Williams, Michael Ashton, Gregory Udall, Rhiannon More, Anupreeta Uronen, Laura Barsode, Ankur Seo, Eungwang Keitel, David Goyal, Srashti Heynen, Jef Liu, Anna Pankunni, Prasia |
| author_facet | Wright, Mick Janquart, Justin Cremonese, Paolo Chan, Juno C. L. Li, Alvin K. Y. Hannuksela, Otto A. Lo, Rico K. L. Ezquiaga, Jose M. Williams, Daniel Williams, Michael Ashton, Gregory Udall, Rhiannon More, Anupreeta Uronen, Laura Barsode, Ankur Seo, Eungwang Keitel, David Goyal, Srashti Heynen, Jef Liu, Anna Pankunni, Prasia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20256 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LensingFlow: An Automated Workflow for Gravitational Wave Lensing Analyses Wright, Mick Janquart, Justin Cremonese, Paolo Chan, Juno C. L. Li, Alvin K. Y. Hannuksela, Otto A. Lo, Rico K. L. Ezquiaga, Jose M. Williams, Daniel Williams, Michael Ashton, Gregory Udall, Rhiannon More, Anupreeta Uronen, Laura Barsode, Ankur Seo, Eungwang Keitel, David Goyal, Srashti Heynen, Jef Liu, Anna Pankunni, Prasia General Relativity and Quantum Cosmology Instrumentation and Methods for Astrophysics Data Analysis, Statistics and Probability 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. |
| title | LensingFlow: An Automated Workflow for Gravitational Wave Lensing Analyses |
| topic | General Relativity and Quantum Cosmology Instrumentation and Methods for Astrophysics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2507.20256 |