Doctor of Philosophy (PhD)



Document Type



We ushered into a new era of gravitational wave astronomy in 2015 when Advanced LIGO gravitational wave detectors in Livingston, Louisiana and Hanford, Washington observed a gravitational wave signal from the merger of binary black holes. The first detected GW150914 was a part of first Observing run (O1) and since then there have been a total of 3 Observing runs. Advanced Virgo detector in Cascina, Italy joined the efforts in the third Observing run (O3) which spanned from April 1, 2019, to March 27, 2020. It was split into O3a and O3b with a month long break between them, during October 2019, for commissioning upgrades. The first half of the run, O3a, from April 1, 2019, to October 1, 2019, resulted in detection of 39 gravitational-wave events with false alaram rate (FAR) $

The gravitational wave data quality is hurt by environmental or instrumental noise artifacts in the data. These short duration noise transients can mask or mimic a gravitational wave. Identification of transient noise coupling, which may lead to a reduced rate of noise is thus of primary concern.

This dissertation focuses on my work during O3 on identifying and reducing noise transients associated with scattered light in the detector. Light scattering adversely affects the LIGO data quality and is linked to multiple retractions of gravitational wave signals. The noise impacts the detector sensitivity in the $10 - 150$ Hz frequency band critical to the discovery of collision of compact objects, especially heavier black holes. Scattered light noise rate is correlated with an increase in ground motion near the detectors. During O3, two different populations of transients due to light scattering: \textit{Slow Scattering} and \textit{Fast Scattering} were observed. In this dissertation, I document my research that led to the identification of Slow Scattering noise couplings in the detector. This was followed by instrument hardware changes resulting in noise mitigation.

This dissertation also discusses transient noise data quality studies I performed during and after O3. These studies shed light on environmental or instrumental correlation with the transient noise in the detector. Improved noise characterization is a significant step that can lead to the recognition of noise couplings in the detector and consequent reduction, which is one of the main objectives of detector characterization.

Finally, I examine the importance of Machine Learning (ML) in gravitational-wave data analysis and discuss my work on training an ML algorithm to identify Fast Scattering noise in the data. I also discuss how this identification led to an improved understanding of the Fast Scattering noise and its dependence on ground motion in two different frequency bands.

Committee Chair

Gonzalez, Gabriela



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