Continuous gravitational waves (CW) from sources such as a non-axisymmetric spinning neutron star have not yet been detected. If CW from neutron stars exist, the weak signals could be hidden within noise. The search for CW signals is impeded by the presence of narrow spectral artifacts (lines) caused by instrumentation or the environment at the Laser Interferometer Gravitational Wave Observatory (LIGO). Better identification of line noise would make a detection more likely, and a detection has the potential to expand our current knowledge of neutron stars. A non-machine learning (non-ML) line finding algorithm is currently used on a daily basis at the LIGO Hanford and Livingston Observatories. A different approach to line finding utilizes a machine learning (ML) algorithm. My research uses both the ML and non-ML line finding methods, comparing the accuracy and efficiency of these two methods applied to various data sets. Expected results are that the non-ML method is currently more accurate and efficient from a computing resources perspective, but that the ML approach has the potential for high accuracy and adaptability, and will eventually be more efficient in human hours. The impact of my research is to implement more accurate and efficient line finding algorithms. This work is important because automated line finders at LIGO Hanford and LIGO Livingston save researchers hundreds of hours of work and could be set up to alert researchers on changes in noise behaviors.

Gravitational waves (GWs) are ripples in spacetime propagating outward from a source at the speed of light. Compact astrophysical objects such as rapidly spinning neutron stars that have miniscule “mountains” on their surfaces are expected to produce continuous gravitational waves (CWs) that are persistent in time. Detecting CWs would be revolutionary, providing new information about the properties of neutron stars. The expected CW signal is mimicked by background noise from some environmental and instrumental sources, so detecting them using the Laser Interferometer Gravitational Wave Observatory (LIGO) is a challenge. We can improve the detector sensitivity by mitigating the instrumental noise. In this project, my goal is to identify how individual frequencies of instrumental noise change in response to changes in components of the detector. Given a known change in a detector component, I analyze how the noise artifacts change around the time of the component change. Averaging over time and taking the ratio of the spectrum after the change to the spectrum before the change reveals changes in individual frequencies, which I can identify and monitor for changes day-by-day. Results of this study highlight correlations between the reduction in particular frequencies of noise and particular changes in detector components. In this ongoing investigation, I find that several combs of equally spaced frequencies are significantly and persistently reduced in magnitude on the same day as an electric bias flip on the test mass at the end of a detector arm, so the electric components of test masses seem to contribute some noise. By identifying another source of instrumental noise, this study helps scientists more clearly distinguish between noise and astrophysical signals, increasing the sensitivity of LIGO to CWs.