Top: Reconstruction of GW231123 waveform using AWaRe. Bottom: Residuals obtained by subtracting the AWaRe mean reconstructions from the whitened strains.Abstract
The LIGO–Virgo–KAGRA Collaboration recently reported GW231123, a black-hole merger with a total mass of around 190–265 solar masses. This event adds to the growing evidence of “lite” intermediate-mass black-hole (IMBH) discoveries, where the post-merger black hole exceeds 100 solar masses. GW231123 posed several data-analysis challenges owing to waveform-model systematics and the 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 combines specialized tools for complementary 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 this 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 masses, 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.
Dr. Chayan Chatterjee
AI for New Messengers Postdoctoral Fellow
Dr. Chayan Chatterjee is the A.I. for New Messengers Postdoctoral Fellow at Vanderbilt University. His research focuses on application of machine learning to study gravitational waves - tiny ripples in space-time created by colliding black holes and neutron stars.