arXiv subjects

Kaylah McGowan

Publications and source records attributed to Kaylah McGowan.

2 recordsLinked to original sources

arXiv2026

ArchGEM: an Advanced Data Analysis Tool for Analyzing Scattered Light Noise in LIGO

Scattered light is one of the most common sources of non-stationary noise at low frequencies in Advanced LIGO detectors. It appears as arch-like features in time-frequency spectrograms, produced when stray light reflects from moving surfaces and recombines with the main interferometer beam. In this study, we present ArchGEM, an automated framework for identifying and characterizing these arches and recovering the physical properties of the scattering surfaces. ArchGEM combines a prominence-based peak-finding method with a Gaussian Mixture Model clustering approach to capture a range of scattered-light morphologies across different detector conditions. We apply ArchGEM to scattered light glitches across Advanced LIGO observing runs O3 (2019--2020) and O4 (2023--2024). We find that the average frequency distributions of this noise span 15--25 Hz in O3a and O4, but increase to 20--40 Hz during O3b. Typical inferred surface velocities are 0.2--0.5 $μ$m/s, and inferred surface displacements are 0.1--0.3 $μ$m. The Gaussian Mixture Model performs most consistently for complex or overlapping features, with mean frequency offsets within 5 Hz of the Gravity Spy baseline. Our results show that ArchGEM provides a practical tool for detector characterization by linking observed spectrogram features to the motion of scattering surfaces and helping guide future mitigation of scattered light noise in current and next-generation interferometers. By quantifying the temporal and spectral behavior of scattered light, ArchGEM provides a robust framework for diagnosing noise sources and guiding targeted mitigation strategies in future detector upgrades.

astro-ph.IM↗

arXiv2025

Machine Learning Confirms GW231123 is a "Lite" Intermediate Mass Black Hole Merger

The LIGO-Virgo-KAGRA Collaboration recently reported GW231123, a black hole merger with total mass of around 190-265 solar mass. This event adds to the growing evidence of "lite" intermediate mass black hole (IMBH) discoveries of post-merger black holes >100 solar mass. GW231123 posed several data analysis challenges owing to waveform-model systematics and presence of noise artifacts called glitches. We present the first comprehensive machine learning analysis to further validate this event, strengthen its astrophysical inference, and characterize instrumental noise in its vicinity. Our approach uses a combination of tools tailored for specific analyses: GW-Whisper, an adaptation of OpenAI's audio transformer, ArchGEM, a Gaussian mixture model-based soft clustering and density approximation software and AWaRe, a convolutional autoencoder. We identify the data segment containing the merger with >70% confidence in both detectors and verify its astrophysical origin. We then characterize the scattered light glitch around the event, providing the first physically interpretable parameters for the glitch. We also reconstruct the real waveforms from the data with slightly better agreement to model-agnostic reconstructions than to quasi-circular models, hinting at possible astrophysics beyond current waveform families (such as non-circular orbits or environmental imprints). Finally, by demonstrating high-fidelity waveform reconstructions for simulated mergers with total masses between 100-1000 solar mass, we show that our method can confidently probe the IMBH regime. Our integrated framework offers a powerful complementary tool to traditional pipelines for rapid, robust analysis of massive, glitch-contaminated events.

astro-ph.HE↗