TY - JOUR
T1 - Dictionary Learning: A Novel Approach to Detecting Binary Black Holes in the Presence of Galactic Noise with LISA
AU - Badger, Charles
AU - Martinovic, Katarina
AU - Torres-Forné, Alejandro
AU - Sakellariadou, Mairi
AU - Font, José A.
N1 - Funding Information:
The authors are grateful to Astrid Lamberts for providing a catalog of double white dwarf sources that allowed us to model the Galactic background. The authors also thank Nelson Christensen for providing helpful feedback. A. T.-F., M. S., and J. A. F. thank the Institute for Pure and Applied Mathematics (IPAM), University of California Los Angeles (UCLA) where this project was initiated at the occasion of the “Mathematical and Computational Challenges in the Era of Gravitational-Wave Astronomy” workshop. We acknowledge computational resources provided by the LISA Data Challenge working group in the LISA consortium. The software packages used in this study are matplotlib , n um p y , and matlab signal processing toolbox . K. M. is supported by King’s College London through a Postgraduate International Scholarship. M. S. is supported in part by the Science and Technology Facility Council (STFC), United Kingdom, under the research Grant No. ST/P000258/1. A. T.-F. and J. A. F. acknowledge support from the Spanish Agencia Estatal de Investigación (PGC2018-095984-B-I00 and PID2021-125485NB-C21) and by the Generalitat Valenciana (PROMETEO/2019/071).
Funding Information:
The authors are grateful to Astrid Lamberts for providing a catalog of double white dwarf sources that allowed us to model the Galactic background. The authors also thank Nelson Christensen for providing helpful feedback. A.T.-F., M.S., and J.A.F. thank the Institute for Pure and Applied Mathematics (IPAM), University of California Los Angeles (UCLA) where this project was initiated at the occasion of the “Mathematical and Computational Challenges in the Era of Gravitational-Wave Astronomy” workshop. We acknowledge computational resources provided by the LISA Data Challenge working group in the LISA consortium. The software packages used in this study are matplotlib [43], numpy [44], and matlab signal processing toolbox [45]. K.M. is supported by King’s College London through a Postgraduate International Scholarship. M.S. is supported in part by the Science and Technology Facility Council (STFC), United Kingdom, under the research Grant No. ST/P000258/1. A.T.-F. and J.A.F. acknowledge support from the Spanish Agencia Estatal de Investigación (PGC2018-095984-B-I00 and PID2021-125485NB-C21) and by the Generalitat Valenciana (PROMETEO/2019/071).
Publisher Copyright:
© 2023 American Physical Society.
PY - 2023/3/3
Y1 - 2023/3/3
N2 - The noise produced by the inspiral of millions of white dwarf binaries in the Milky Way may pose a threat to one of the main goals of the space-based LISA mission: the detection of massive black hole binary mergers. We present a novel study for reconstruction of merger waveforms in the presence of Galactic confusion noise using dictionary learning. We discuss the limitations of untangling signals from binaries with total mass from 102 M⊙ to 104 M⊙. Our method proves extremely successful for binaries with total mass greater than ∼3×103 M⊙ up to redshift 3 in conservative scenarios, and up to redshift 7.5 in optimistic scenarios. In addition, consistently good waveform reconstruction of merger events is found if the signal-to-noise ratio is approximately 5 or greater.
AB - The noise produced by the inspiral of millions of white dwarf binaries in the Milky Way may pose a threat to one of the main goals of the space-based LISA mission: the detection of massive black hole binary mergers. We present a novel study for reconstruction of merger waveforms in the presence of Galactic confusion noise using dictionary learning. We discuss the limitations of untangling signals from binaries with total mass from 102 M⊙ to 104 M⊙. Our method proves extremely successful for binaries with total mass greater than ∼3×103 M⊙ up to redshift 3 in conservative scenarios, and up to redshift 7.5 in optimistic scenarios. In addition, consistently good waveform reconstruction of merger events is found if the signal-to-noise ratio is approximately 5 or greater.
UR - http://www.scopus.com/inward/record.url?scp=85149619517&partnerID=8YFLogxK
U2 - 10.1103/PhysRevLett.130.091401
DO - 10.1103/PhysRevLett.130.091401
M3 - Article
SN - 0031-9007
VL - 130
JO - Physical Review Letters
JF - Physical Review Letters
IS - 9
M1 - 091401
ER -