Reading the bibliography…
2023
Performance of the low-latency GstLAL inspiral search towards LIGO, Virgo, and KAGRA's fourth observing run Ewing, Becca, Huxford, Rachael, Singh, Divya et al.
Understand GstLAL is a stream-based matched-filtering search pipeline aiming at the prompt discovery of gravitational waves from compact binary coalescences such as the mergers of black holes and neutron stars.
Over the past three observation runs by the LIGO, Virgo, and KAGRA (LVK) collaboration, the GstLAL search pipeline has participated in several tens of gravitational wave discoveries. The fourth observing run (O4) is set to begin in May 2023 and is expected to see the discovery of many new and interesting gravitational wave signals which will inform our understanding of astrophysics and cosmology. We describe the current configuration of the GstLAL low-latency search and show its readiness for the upcoming observation run by presenting its performance on a mock data challenge. Performance of the low-latency GstLAL inspiral search towards LIGO, Virgo, and KAGRA's fourth observing run · Around
Built on S. Sachdev et al. , The GstLAL Search Analysis Methods for Compact Binary Mergers in Advanced LIGO’s Second and Advanced Virgo’s First Observing Runs (2019), arXiv:1901.08580 [gr-qc]
Original
1901
Earlier work this paper cites.
S. J. Kapadia et al. , A self-consistent method to estimate the rate of compact binary coalescences with a Poisson mixture model, Class. Quant. Grav. 37
Original
1903
Earlier work this paper cites.
T. Dietrich, A. Samajdar, S. Khan, N. K. Johnson-McDaniel, R. Dudi, and W. Tichy, Improving the NRTidal model for binary neutron star systems, Phys. Rev. D 100
Original
1905
Earlier work this paper cites.
R. J. E. Smith, G. Ashton, A. Vajpeyi, and C. Talbot, Massively parallel Bayesian inference for transient gravitational-wave astronomy, Mon. Not. Roy. Astron. Soc. 498
Original
1909
Earlier work this paper cites.
E. E. Salpeter, The Luminosity function and stellar evolution, Astrophys. J. 121
1955
Earlier work this paper cites.
D. W. Hogg, Distance measures in cosmology (1999), arXiv:astro-ph/9905116
1999
Earlier work this paper cites.
R. Essick, P. Godwin, C. Hanna, L. Blackburn, and E. Katsavounidis, iDQ: Statistical Inference of Non-Gaussian Noise with Auxiliary Degrees of Freedom in Gravitational-Wave Detectors, Mach. Learn.: Sci. Technol. 2
Original
2005
Earlier work this paper cites.
S. Morisaki and V. Raymond, Rapid Parameter Estimation of Gravitational Waves from Binary Neutron Star Coalescence using Focused Reduced Order Quadrature, Phys. Rev. D 102
Original
2007
Earlier work this paper cites.
R. Abbott et al. (LIGO Scientific, Virgo), GW190521: A Binary Black Hole Merger with a Total Mass of 150 M ⊙ 150M_{\odot} , Phys. Rev. Lett. 125
Original
2009
Earlier work this paper cites.
K. Cannon et al. , GstLAL: A software framework for gravitational wave discovery (2020), arXiv:2010.05082 [astro-ph.IM]
Original
2010
Earlier work this paper cites.
K. Cannon et al. , Toward Early-Warning Detection of Gravitational Waves from Compact Binary Coalescence, Astrophys. J. 748
Original
2012
Earlier work this paper cites.
K. Cannon, C. Hanna, and D. Keppel, Method to estimate the significance of coincident gravitational-wave observations from compact binary coalescence, Phys. Rev. D 88
Original
2013
Earlier work this paper cites.
B. Moe, P. Brady, B. Stephens, E. Katsavounidis, R. Williams, and F. Zhang, GraceDB: A Gravitational Wave Candidate Event Database (2014)
2014
Earlier work this paper cites.
Similar K. Cannon, C. Hanna, and J. Peoples, Likelihood-Ratio Ranking Statistic for Compact Binary Coalescence Candidates with Rate Estimation (2015), arXiv:1504.04632 [astro-ph.IM]
Original
2015
Cited alongside, same era.
P. Schmidt, F. Ohme, and M. Hannam, Towards models of gravitational waveforms from generic binaries II: Modelling precession effects with a single effective precession parameter, Phys. Rev. D 91
Original
2015
Cited alongside, same era.
W. M. Farr, J. R. Gair, I. Mandel, and C. Cutler, Counting And Confusion: Bayesian Rate Estimation With Multiple Populations, Phys. Rev. D 91
Original
2015
Cited alongside, same era.
B. P. Abbott et al. (LIGO Scientific, Virgo), Observation of Gravitational Waves from a Binary Black Hole Merger, Phys. Rev. Lett. 116
Original
2016
Cited alongside, same era.
Then S. Sakon et al. , Template bank for compact binary mergers in the fourth observing run of Advanced LIGO, Advanced Virgo, and KAGRA (2022), arXiv:2211.16674 [gr-qc]
Original
2022
Later among the works it cites.
L. Tsukada et al. , Improved ranking statistics of the GstLAL inspiral search for compact binary coalescences (2023), arXiv:2305.06286 [astro-ph.IM]
Original
2023
Closest in time.
R. Abbott et al. (KAGRA, VIRGO, LIGO Scientific), Population of Merging Compact Binaries Inferred Using Gravitational Waves through GWTC-3, Phys. Rev. X 13
Original
2023
Closest in time.
P. Joshi, L. Tsukada, and C. Hanna, Background Filter: A method for removing signal contamination during significance estimation of a GstLAL anaysis (2023), arXiv:2305.18233 [gr-qc]
Original
2023
Closest in time.
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L. P. Singer and L. R. Price, Rapid Bayesian position reconstruction for gravitational-wave transients (2016), arXiv:1508.03634 [gr-qc]
Cited alongside, same era.
LIGO Scientific Collaboration and Virgo Collaboration, GCN 21505
2017
Cited alongside, same era.
H. K. Y. Fong, From simulations to signals: Analyzing gravitational waves from compact binary coalescences , Ph.D. thesis , Toronto U. (2018)
2018
Cited alongside, same era.
A. Ray et al. , When to Point Your Telescopes: Gravitational Wave Trigger Classification for Real-Time Multi-Messenger Followup Observations (2023), arXiv:2306.07190 [gr-qc]
S. S. Chaudhary et al. , Low-latency alert products and their performance in anticipation of the fourth ligo-virgo-kagra observing run, (in prep) (2023)
2023
Closest in time.
LIGO Scientific Collaboration, Virgo Collaboration, and KAGRA Collaboration, LIGO/Virgo/KAGRA Public Alerts User Guide, https://emfollow.docs.ligo.org/userguide/ (2023)
2023
Closest in time.
GstLAL, https://git.ligo.org/lscsoft/gstlal (2023)
2023
Closest in time.
gwcelery, https://git.ligo.org/emfollow/gwcelery (2023)
2023
Closest in time.
B. Ewing, gw-lts, https://git.ligo.org/rebecca.ewing/gw-lts (2023)
2023
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A. Pace, igwn-alert, https://igwn-alert.readthedocs.io/ (2023)
2023
Closest in time.
P. Godwin, ligo-scald, https://git.ligo.org/gstlal-visualisation/ligo-scald (2023)
2023
Closest in time.