GstLAL: A software framework for gravitational wave discovery

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 o...

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Veröffentlicht in:SoftwareX Jg. 14; S. 100680
Hauptverfasser: 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
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Goddard Space Flight Center Elsevier B.V 01.06.2021
Elsevier
Schlagworte:
Astrophysics
Black holes
Computer Programming And Software
Data analysis
Gravitational waves
Multi-messenger astrophysics
Neutron stars
Multi-messenger astrophysics
Neutron stars
Data analysis
Black holes
Gravitational waves
Data Analysis
Gravitational Waves
Multi-Messenger Astrophysics
Black Holes
Neutron Stars
ISSN:2352-7110, 2352-7110
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Zusammenfassung: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 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.
Bibliographie:GSFC
Goddard Space Flight Center
ISSN:2352-7110
2352-7110
DOI:10.1016/j.softx.2021.100680