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General Relativity and Quantum Cosmology, gr-qc
Abstract:
Data streams of gravitational-wave detectors are polluted by transient noise
features, or "glitches", of instrumental and environmental origin. In this
work, we investigate the use of total-variation methods and learned
dictionaries to mitigate the effect of those transients in the data. We focus
on a specific type of transient, "blip" glitches, as this is the most common
type of glitch present in the LIGO detectors and their waveforms are easy to
identify. We randomly select 80 blip glitches scattered in the data from
advanced LIGO's O1 run, as provided by the citizen-science project Gravity Spy.
Our results show that dictionary-learning methods are a valid approach to model
and subtract most of the glitch contribution in all cases analyzed,
particularly at frequencies below $\sim 1$ kHz. The high-frequency component of
the glitch is best removed when a combination of dictionaries with different
atom length is employed. As a further example, we apply our approach to the
glitch visible in the LIGO-Livingston data around the time of merger of binary
neutron star signal GW170817, finding satisfactory results. This paper is the
first step in our ongoing program to automatically classify and subtract all
families of gravitational-wave glitches employing variational methods.
