papers

Publications (17)

astro-ph.CO2026

Interpretable Analytic Formulae for GWTC-4 Binary Black Hole Population Properties via Symbolic Regression

Chayan Chatterjee

Recent LIGO-Virgo-KAGRA (LVK) analyses have revealed complex structure in the binary black hole (BBH) population, including distinct features in the primary mass spectrum and nontr…

astro-ph.IM2026

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

Kaylah McGowan, Shania Nichols, Siddharth Soni +4

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 spectrog…

astro-ph.HE2020

Enhancing Gravitational-Wave Science with Machine Learning

Elena Cuoco, Jade Powell, Marco Cavaglià +23

Machine learning has emerged as a popular and powerful approach for solving problems in astrophysics. We review applications of machine learning techniques for the analysis of grou…

astro-ph.HE2025

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

Chayan Chatterjee, Kaylah McGowan, Suyash Deshmukh +1

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" i…

hep-ex2023

Applications of Deep Learning to physics workflows

Manan Agarwal, Jay Alameda, Jeroen Audenaert +65

Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing…

gr-qc2024

Reconstruction of binary black hole harmonics in LIGO using deep learning

Chayan Chatterjee, Karan Jani

Gravitational wave signals from coalescing compact binaries in the LIGO and Virgo interferometers are primarily detected by the template based matched filtering method. While this…