The collection of gravitational waves (GWs) that are either too weak or too numerous to be individually resolved is commonly referred to as the gravitational-wave background (GWB). A confident detection and model-driven characterization of such a signal will provide invaluable information about the evolution of the universe and the population of GW sources within it. We present a new, user-friendly, Python-based package for GW data analysis to search for an isotropic GWB in ground-based interferometer data. We employ cross-correlation spectra of GW detector pairs to construct an optimal estimator of the Gaussian and isotropic GWB, and Bayesian parameter estimation to constrain GWB models. The modularity and clarity of the code allow for both a shallow learning curve and flexibility in adjusting the analysis to one’s own needs. We describe the individual modules that make up pygwb, following the traditional steps of stochastic analyses carried out within the LIGO, Virgo, and KAGRA Collaboration. We then describe the built-in pipeline that combines the different modules and validate it with both mock data and real GW data from the O3 Advanced LIGO and Virgo observing run. We successfully recover all mock data injections and reproduce published results.

Renzini, A., Romero-Rodriguez, A., Talbot, C., Lalleman, M., Kandhasamy, S., Turbang, K., et al. (2023). pygwb: A Python-based Library for Gravitational-wave Background Searches. THE ASTROPHYSICAL JOURNAL, 952(1) [10.3847/1538-4357/acd775].

pygwb: A Python-based Library for Gravitational-wave Background Searches

Renzini A. I.
;
2023

Abstract

The collection of gravitational waves (GWs) that are either too weak or too numerous to be individually resolved is commonly referred to as the gravitational-wave background (GWB). A confident detection and model-driven characterization of such a signal will provide invaluable information about the evolution of the universe and the population of GW sources within it. We present a new, user-friendly, Python-based package for GW data analysis to search for an isotropic GWB in ground-based interferometer data. We employ cross-correlation spectra of GW detector pairs to construct an optimal estimator of the Gaussian and isotropic GWB, and Bayesian parameter estimation to constrain GWB models. The modularity and clarity of the code allow for both a shallow learning curve and flexibility in adjusting the analysis to one’s own needs. We describe the individual modules that make up pygwb, following the traditional steps of stochastic analyses carried out within the LIGO, Virgo, and KAGRA Collaboration. We then describe the built-in pipeline that combines the different modules and validate it with both mock data and real GW data from the O3 Advanced LIGO and Virgo observing run. We successfully recover all mock data injections and reproduce published results.
Articolo in rivista - Articolo scientifico
gravitational waves
English
2023
952
1
25
none
Renzini, A., Romero-Rodriguez, A., Talbot, C., Lalleman, M., Kandhasamy, S., Turbang, K., et al. (2023). pygwb: A Python-based Library for Gravitational-wave Background Searches. THE ASTROPHYSICAL JOURNAL, 952(1) [10.3847/1538-4357/acd775].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/459651
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