Degree

Doctor of Philosophy (PhD)

Department

Physics and Astronomy

Document Type

Dissertation

Abstract

In the era of gravitational wave (GW) astronomy, the detection of cosmic events such as black hole and neutron star mergers has become routine due to the Advanced LIGO (aLIGO) detectors. However, the sensitivity of these detectors also makes them susceptible to loud transient noise, which can obscure GW signals. This thesis investigates loud transient noise with a Signal-to-Noise Ratio (SNR) greater than 100, focusing particularly on the most extreme transients with SNR exceeding 1000.

We begin by analyzing the impact of loud transient noise on the binary neutron star (BNS) range of aLIGO detectors across observation runs O2, O3, and O4a. Despite improvements in interferometer sensitivity, the SNR and amplitude of loud glitches have remained constant. My statistical analysis reveals that the daily rate of these glitches follows a Poisson distribution, indicating their independent occurrence.

A comprehensive study of auxiliary data channels identifies those significantly affected by loud transient noise. An analysis of auxiliary data highlights interferometer sensing and control channels that witness these glitches, particularly noting that certain transient noise classifications, such as Blip, Koi Fish, and Extremely Loud, may share common origins.

Machine learning techniques, specifically t-SNE, are employed to cluster different Gravity Spy glitch classes and the time series of loud transient noise. This clustering supports the hypothesis of an energy scale continuum between Blip, Koi Fish, and Extremely Loud glitches. Additionally, I identify distinct morphologies of loud transient noise, laying the groundwork for determining their characteristic durations.

Potential sources of loud transient noise are examined, including residual gases, dust particles, and the differential arm length (DARM) control loop. Although these sources are ruled out based on observed behaviors and experimental noise injections, the research provides a deeper understanding of loud transient noise characteristics. This thesis establishes a foundation for future research, emphasizing the importance of detector characterization research to study transient noise in GW strain data. We aim to uncover the sources of loud transient noise, thereby improving the data quality for GW detection and enhancing our understanding of the universe.

Date

7-13-2024

Committee Chair

Gonzalez, Gabriela

DOI

https://doi.org/10.31390/gradschool_dissertations.6539

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