Binary Black Holes

A Fast and Scalable Transformer Pipeline for Binary Black Hole Detection

Castor is a compact time-domain transformer pipeline for binary black hole detection that enables efficient time-slide background estimation and strong sensitivity on benchmark and real LIGO data.

Dr. Chayan Chatterjee, Abigail Petulante, Haowei Fu, Yang Hu, Roy Lau, Karan Jani

A Fast and Scalable Transformer Pipeline for Binary Black Hole Detection

A fast Siamese transformer search pipeline for scalable binary black hole detection and efficient empirical background estimation.

Castor

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

Symbolic regression turns GWTC-4 binary black-hole population posteriors into compact analytic formulae, making rate evolution, spin trends and mass-ratio structure easier to inspect and reuse.

Dr. Chayan Chatterjee

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

Interpretable Binary Black Hole Population Inference

Symbolic regression methods for turning GWTC-4 population posteriors into compact, differentiable analytic formulae.

Interpretable Binary Black Hole Population Inference

Attention-boosted Waveform Reconstruction neural network for binary black holes

AWaRe

Rapid Mass Parameter Estimation of Binary Black Hole Coalescences Using Deep Learning

A deep learning-based workflow for denoising and estimating black hole masses from gravitational wave data.

Rapid Mass Parameter Estimation of Binary Black Hole Coalescences Using Deep Learning

Deep Learning for Denoising Gravitational Waves

A deep learning model to extract binary black hole gravitational wave signals from LIGO noise.

Deep Learning for Denoising Gravitational Waves

Using Deep Learning to Localize Gravitational Waves

Deep learning-based classification model to localize gravitational wave sources to a sector in the sky