GstLAL: A software framework for gravitational wave discovery
Cannon, Kipp; Caudill, Sarah; Chan, Chiwai; Cousins, Bryce; Creighton, Jolien D.E.; Ewing, Becca; Fong, Heather; Godwin, Patrick; Hanna, Chad; Hooper, Shaun; Huxford, Rachael; Magee, Ryan; Meacher, Duncan; Messick, Cody; Morisaki, Soichiro; Mukherjee, Debnandini; Ohta, Hiroaki; Pace, Alexander; Privitera, Stephen; de Ruiter, Iris; Sachdev, Surabhi; Singer, Leo; Singh, Divya; Tapia, Ron; Tsukada, Leo; Tsuna, Daichi; Tsutsui, Takuya; Ueno, Koh; Viets, Aaron; Wade, Leslie; Wade, Madeline
(2021) SoftwareX, volume 14, pp. 1 - 9
(Article)
Abstract
The GstLAL library, derived from Gstreamer and the LIGO Algorithm Library, supports a stream-based approach to gravitational-wave data processing. Although GstLAL was primarily designed to search for gravitational-wave signatures of merging black holes and neutron stars, it has also contributed to other gravitational-wave searches, data calibration, and detector-characterization efforts. GstLAL
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has played an integral role in all of the LIGO-Virgo collaboration detections, and its low-latency configuration has enabled rapid electromagnetic follow-up for dozens of compact binary candidates.
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Keywords: Black holes, Data analysis, Gravitational waves, Multi-messenger astrophysics, Neutron stars, Software, Computer Science Applications
ISSN: 2352-7110
Publisher: Elsevier BV
Note: Funding Information: Funding for this work was provided by the National Science Foundation, USA through awards: PHY-1454389 , OAC-1642391 , PHY-1700765 , OAC-1841480 , PHY-1607178 , and PHY-1847350 . Funding for this project was provided by the Charles E. Kaufman Foundation of The Pittsburgh Foundation, USA . Computations for this research were performed on the Pennsylvania State University’s Institute for Computational and Data Sciences Advanced CyberInfrastructure (ICDS-ACI) and VM hosting. We are grateful for computational resources provided by the Leonard E Parker Center for Gravitation, Cosmology and Astrophysics at the University of Wisconsin-Milwaukee. Computing support was provided by the LIGO Laboratory through National Science Foundation, USA grant PHY-1764464 . GstLAL relies on many other open source software libraries; we gratefully acknowledge the development and support of NumPy [86] , SciPy [87] , PyGTK [88] , PyGST [89] , Bottle [42] , Kafka [90] , Fftw3F [91] , Intel MKL [92] , GLib2 [93] , GNU Scientific Library [94] , and GWpy [95] . The authors gratefully acknowledge the LIGO-Virgo-Kagra collaboration, USA for support, review, and valuable critiques throughout various stages of development of the GstLAL library. We are especially thankful for collaborations within the Compact Binary Coalescence working group. Publisher Copyright: © 2021 The Authors
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