Glitch Reweighting by Glitch Parameter Simulation

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Abstract:: Gravitational waves are ripples in space-time caused by
the acceleration of high mass objects. General relativity
predicts their existence and they were first observed by
LIGO (Laser Interferometer Gravitational-wave Obser-
vatory) in 2015. LIGO utilizes highly precise instrumen-
tation to detect the differences in length of its perpen-
dicular, 4 kilometer “arms” when a gravitational wave
passes through. LIGO is able to detect the inspiral and
merger of orbiting black holes and neutron stars. The re-
sulting waveform is dependant on the parameters of the
system. After accounting for the noise in the detectors
the waveform can be matched to a model and parameters
can be found.
However, glitches in the data can occur which vary in
sources, frequency, time, duration and strength. These
glitches can interfere with how we account for noise be-
fore parameter estimation and lead to biased parameters.
To account for glitches we can cut them out or subtract
them from the data using the program BayesWave, but
BayesWave cannot construct a waveform model for CBC
(compact binary coalescence) that includes precession.
As concluded in my mentors’ paper on GW200129 [1]
”any evidence for spin-precession in GW200129 depends
sensitively on the glitch model and priors employed”.

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LIGO Document P2300173-v1