Application of dictionary learning to denoise LIGO's blip noise transients

Understand

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.