This project develops interpretable analytic summaries of binary black hole population properties inferred from the GWTC-4 catalog. Instead of treating flexible population models as opaque numerical objects, symbolic regression is used to learn closed-form surrogate expressions for merger-rate evolution, spin-population trends and mass-ratio structure near the primary-mass peaks.
The resulting formulae make it possible to inspect gradients exactly, compare features across model families and pass compact population summaries into downstream calculations such as rate forecasting, formation-channel comparisons and stochastic-background estimates.
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| Fig. 1: Symbolic-regression surrogates reproduce GWTC-4 binary black hole merger-rate trends while preserving posterior uncertainty across model families. |
Related papers:
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 applying machine learning, frontier AI and interpretable inference to gravitational waves and multimessenger astronomy.
