GstLAL: A software framework for gravitational wave discovery

Kipp Cannon; Sarah Caudill; Chiwai Chan; Bryce Cousins; Jolien D.E. Creighton; Becca Ewing; Heather Fong; Patrick Godwin; Chad Hanna; Shaun Hooper; Rachael Huxford; Ryan Magee; Duncan Meacher; Cody Messick; Soichiro Morisaki; Debnandini Mukherjee; Hiroaki Ohta; Alexander Pace; Stephen Privitera; Iris de Ruiter; Surabhi Sachdev; Leo Singer; Divya Singh; Ron Tapia; Leo Tsukada; Daichi Tsuna; Takuya Tsutsui; Koh Ueno; Aaron Viets; Leslie Wade; Madeline Wade

doi:10.1016/j.softx.2021.100680

GstLAL: A software framework for gravitational wave discovery

Kipp Cannon, Sarah Caudill, Chiwai Chan, Bryce Cousins, Jolien D.E. Creighton, Becca Ewing, Heather Fong, Patrick Godwin, Chad Hanna, Shaun Hooper, Rachael Huxford, Ryan Magee, Duncan Meacher, Cody Messick, Soichiro Morisaki, Debnandini Mukherjee, Hiroaki Ohta, Alexander Pace, Stephen Privitera, Iris de RuiterSurabhi Sachdev, Leo Singer, Divya Singh, Ron Tapia, Leo Tsukada, Daichi Tsuna, Takuya Tsutsui, Koh Ueno, Aaron Viets, Leslie Wade, Madeline Wade

Research output: Contribution to journal › Article › peer-review

51 Scopus citations

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 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.

Original language	English (US)
Article number	100680
Journal	SoftwareX
Volume	14
DOIs	https://doi.org/10.1016/j.softx.2021.100680
State	Published - Jun 2021

All Science Journal Classification (ASJC) codes

Software
Computer Science Applications

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10.1016/j.softx.2021.100680

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Cannon, K., Caudill, S., Chan, C., Cousins, B., Creighton, J. D. E., Ewing, B., Fong, H., Godwin, P., Hanna, C., Hooper, S., Huxford, R., Magee, R., Meacher, D., Messick, C., Morisaki, S., Mukherjee, D., Ohta, H., Pace, A., Privitera, S., ... Wade, M. (2021). GstLAL: A software framework for gravitational wave discovery. SoftwareX, 14, Article 100680. https://doi.org/10.1016/j.softx.2021.100680

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title = "GstLAL: A software framework for gravitational wave discovery",

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 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.",

author = "Kipp Cannon and Sarah Caudill and Chiwai Chan and Bryce Cousins and Creighton, {Jolien D.E.} and Becca Ewing and Heather Fong and Patrick Godwin and Chad Hanna and Shaun Hooper and Rachael Huxford and Ryan Magee and Duncan Meacher and Cody Messick and Soichiro Morisaki and Debnandini Mukherjee and Hiroaki Ohta and Alexander Pace and Stephen Privitera and {de Ruiter}, Iris and Surabhi Sachdev and Leo Singer and Divya Singh and Ron Tapia and Leo Tsukada and Daichi Tsuna and Takuya Tsutsui and Koh Ueno and Aaron Viets and Leslie Wade and Madeline Wade",

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year = "2021",

month = jun,

doi = "10.1016/j.softx.2021.100680",

language = "English (US)",

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Cannon, K, Caudill, S, Chan, C, Cousins, B, Creighton, JDE, Ewing, B, Fong, H, Godwin, P, Hanna, C, Hooper, S, Huxford, R, Magee, R, Meacher, D, Messick, C, Morisaki, S, Mukherjee, D, Ohta, H, Pace, A, Privitera, S, de Ruiter, I, Sachdev, S, Singer, L, Singh, D, Tapia, R, Tsukada, L, Tsuna, D, Tsutsui, T, Ueno, K, Viets, A, Wade, L & Wade, M 2021, 'GstLAL: A software framework for gravitational wave discovery', SoftwareX, vol. 14, 100680. https://doi.org/10.1016/j.softx.2021.100680

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T2 - A software framework for gravitational wave discovery

AU - Cannon, Kipp

AU - Caudill, Sarah

AU - Chan, Chiwai

AU - Cousins, Bryce

AU - Creighton, Jolien D.E.

AU - Ewing, Becca

AU - Fong, Heather

AU - Godwin, Patrick

AU - Hanna, Chad

AU - Hooper, Shaun

AU - Huxford, Rachael

AU - Magee, Ryan

AU - Meacher, Duncan

AU - Messick, Cody

AU - Morisaki, Soichiro

AU - Mukherjee, Debnandini

AU - Ohta, Hiroaki

AU - Pace, Alexander

AU - Privitera, Stephen

AU - de Ruiter, Iris

AU - Sachdev, Surabhi

AU - Singer, Leo

AU - Singh, Divya

AU - Tapia, Ron

AU - Tsukada, Leo

AU - Tsuna, Daichi

AU - Tsutsui, Takuya

AU - Ueno, Koh

AU - Viets, Aaron

AU - Wade, Leslie

AU - Wade, Madeline

PY - 2021/6

Y1 - 2021/6

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AB - 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.

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GstLAL: A software framework for gravitational wave discovery

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