TY - JOUR
T1 - pygwb
T2 - A Python-based Library for Gravitational-wave Background Searches
AU - Renzini, Arianna I.
AU - Romero-Rodríguez, Alba
AU - Talbot, Colm
AU - Lalleman, Max
AU - Kandhasamy, Shivaraj
AU - Turbang, Kevin
AU - Biscoveanu, Sylvia
AU - Martinovic, Katarina
AU - Meyers, Patrick
AU - Tsukada, Leo
AU - Janssens, Kamiel
AU - Davis, Derek
AU - Matas, Andrew
AU - Charlton, Philip
AU - Liu, Guo Chin
AU - Dvorkin, Irina
AU - Banagiri, Sharan
AU - Bose, Sukanta
AU - Callister, Thomas
AU - De Lillo, Federico
AU - D’Onofrio, Luca
AU - Garufi, Fabio
AU - Harry, Gregg
AU - Lawrence, Jessica
AU - Mandic, Vuk
AU - Macquet, Adrian
AU - Michaloliakos, Ioannis
AU - Mitra, Sanjit
AU - Pham, Kiet
AU - Poggiani, Rosa
AU - Regimbau, Tania
AU - Romano, Joseph D.
AU - van Remortel, Nick
AU - Zhong, Haowen
N1 - Publisher Copyright:
© 2023. The Author(s). Published by the American Astronomical Society.
PY - 2023/7/1
Y1 - 2023/7/1
N2 - 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.
AB - 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.
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U2 - 10.3847/1538-4357/acd775
DO - 10.3847/1538-4357/acd775
M3 - Article
AN - SCOPUS:85165279254
SN - 0004-637X
VL - 952
JO - Astrophysical Journal
JF - Astrophysical Journal
IS - 1
M1 - 25
ER -