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Automating the Inclusion of Subthreshold Signal-to-Noise Ratios for Rapid Gravitational-Wave Localization
Cody Messick, Surabhi Sachdev, Kipp Cannon +29
The accurate localization of gravitational-wave (GW) events in low-latency is a crucial element in the search for further multimessenger signals from these cataclysmic events. The…
Improving the background estimation technique in the GstLAL inspiral pipeline with the time-reversed template bank
Chiwai Chan, Kipp Cannon, Sarah Caudill +20
Background estimation is important for determining the statistical significance of a gravitational-wave event. Currently, the background model is constructed numerically from the s…
A Machine Learning Based Source Property Inference for Compact Binary Mergers
Deep Chatterjee, Shaon Ghosh, Patrick R. Brady +4
The detection of the binary neutron star (BNS) merger, GW170817, was the first success story of multi-messenger observations of compact binary mergers. The inferred merger rate alo…
Template bank for spinning compact binary mergers in the second observation run of Advanced LIGO and the first observation run of Advanced Virgo
Debnandini Mukherjee, Sarah Caudill, Ryan Magee +25
We describe the methods used to construct the aligned-spin template bank of gravitational waveforms used by the GstLAL-based inspiral pipeline to analyze data from the second obser…
Optimizing searches for electromagnetic counterparts of gravitational wave triggers
Michael W. Coughlin, Duo Tao, Man Leong Chan +9
With the detection of a binary neutron star system and its corresponding electromagnetic counterparts, a new window of transient astronomy has opened. Due to the size of the error…
A classifier for gravitational-wave inspiral signals in non-ideal single-detector data
Shasvath J. Kapadia, Thomas Dent, Tito Dal Canton
We describe a multivariate classifier for candidate events in a templated search for gravitational-wave (GW) inspiral signals from neutron-star--black-hole (NS-BH) binaries, in dat…