Doctoral Dissertations

Keywords and Phrases

Glitch; Gravitational Waves; Machine Learning; Statistics


“The existence of gravitational waves (GWs), small perturbations in spacetime produced by accelerating massive objects was first predicted in 1916 as solutions of Einstein’s Theory of General Relativity (Einstein, 1916). Detecting and analyzing GWs produced by sources allows us to probe astrophysical phenomena.

The era of GW astronomy began from the first direct detection of the coalescence of a binary black hole in 2015 by the collaboration of the advanced Laser Interferometer Gravitational-wave Observatory (LIGO) (Aasi et al., 2015) and advanced Virgo (Abbott et al., 2016a). Since 2015, LIGO-Virgo detected about 50 confident transient events of GW signals (Abbott et al., 2019d, 2021b).

To detect GW signals, the detectors must be extremely sensitive, causing them to be susceptible to instrumental and environmental noise. Particularly, excess transient noise artifacts, or glitches significantly impair the quality of detector data. Identification of the source of these glitches is a crucial point for the improvement of GW signal detectability and a better estimate of source parameters. However, glitches are the product of short-lived linear and non-linear couplings among the interrelated detector-control systems that include optic alignment systems and mitigation systems of ground motions, generally making it difficult to find their origin. We present a new software called PyChChoo (Mogushi, 2021a) which uses time series recorded in the instrumental control systems and environmental sensors around times when glitches are present in the detector’s output read-out to reveal essential clues about their origin. Using these time series, we subtract glitches using a machine learning algorithm. We find that our method reduces 20-70% of excess power due to the presence of glitches. For low-latency operations, we present another machine-learning based algorithm called NNETFIX (Mogushi et al., 2021) to estimate the data containing a GW signal that is partially removed due to the presence of an overlapping glitch”--Abstract, page iv.


Cavaglia, Marco

Committee Member(s)

Vojta, Thomas
Saito, Shun
Yamilov, Alexey
Kopeikin, Sergei



Degree Name

Ph. D. in Physics


K.M. is supported by the U.S. National Science Foundation grant PHY-1921006.


Missouri University of Science and Technology

Publication Date

Summer 2021

Journal article titles appearing in thesis/dissertation

  • Application PF a New Transient-Noise Analysis Tool for an Unmodeled Gravitational-Wave Search Pipeline
  • Reduction of Transient Noise Artifacts in Gravitational-Wave Data using Deep Learning


xxii, 202 pages

Note about bibliography

Includes bibliographic references.


© 2021 Kentaro Mogushi, All rights reserved.

Document Type

Dissertation - Open Access

File Type




Thesis Number

T 11914

Electronic OCLC #


Included in

Physics Commons