6 citations · 20 across the 10 of their papers we have counts for
10 papers
A Fast and Scalable Transformer Pipeline for Binary Black Hole Detection
Chayan Chatterjee, Abigail Petulante, Haowei Fu +3
With the projected increase in the detection rate of compact-binary coalescences in the coming decade, there is critical need to develop fast, robust, and scalable alternatives to…
Rapid data quality investigations of gravitational-wave events with the Data Quality Report Builder toolkit
Derek Davis, Zach Yarbrough, Joseph Areeda +30
We present the Data Quality Report Builder toolkit, DQRbuild, a suite of data quality tools that have been developed to vet gravitational-wave events in preparation for the fourth…
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…
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…
The Sound of Noise: Leveraging the Inductive Bias of Pre-trained Audio Transformers for Glitch Identification in LIGO
Suyash Deshmukh, Chayan Chatterjee, Abigail Petulante +2
Transient noise artifacts, or glitches, fundamentally limit the sensitivity of gravitational-wave (GW) interferometers and can mimic true astrophysical signals, particularly the sh…
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…
No Glitch in the Matrix: Robust Reconstruction of Gravitational Wave Signals Under Noise Artifacts
Chayan Chatterjee, Karan Jani
Gravitational wave observations by ground based detectors such as LIGO and Virgo have transformed astrophysics, enabling the study of compact binary systems and their mergers. Howe…
Pre-trained Audio Transformer as a Foundational AI Tool for Gravitational Waves
Chayan Chatterjee, Abigail Petulante, Karan Jani +7
As gravitational wave detectors become more advanced and sensitive, the number of signals recorded by Advanced LIGO and Virgo from merging compact objects is expected to rise drama…
Navigating Unknowns: Deep Learning Robustness for Gravitational Wave Signal Reconstruction
Chayan Chatterjee, Karan Jani
We present a rapid and reliable deep learning-based method for gravitational wave signal reconstruction from elusive, generic binary black hole mergers in LIGO data. We demonstrate…
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…