NeoSySPArtaN: A Neuro-Symbolic Spin Prediction Architecture for higher-order multipole waveforms from eccentric Binary Black Hole mergers using Numerical Relativity

TL;DR

This paper introduces NeoSySPArtaN, a neuro-symbolic architecture that accurately predicts spin magnitudes in eccentric binary black hole mergers using numerical relativity waveform data, enhancing interpretability and precision.

Contribution

It presents a novel neuro-symbolic framework combining neural networks and symbolic regression for spin prediction in complex gravitational waveforms from mergers.

Findings

Achieves RMSE of 0.05 for the NSA model

Demonstrates robustness on higher-order multipole waveforms

Provides interpretable predictions for eccentric mergers

Abstract

The prediction of spin magnitudes in binary black hole and neutron star mergers is crucial for understanding the astrophysical processes and gravitational wave (GW) signals emitted during these cataclysmic events. In this paper, we present a novel Neuro-Symbolic Architecture (NSA) that combines the power of neural networks and symbolic regression to accurately predict spin magnitudes of black hole and neutron star mergers. Our approach utilizes GW waveform data obtained from numerical relativity simulations in the SXS Waveform catalog. By combining these two approaches, we leverage the strengths of both paradigms, enabling a comprehensive and accurate prediction of spin magnitudes. Our experiments demonstrate that the proposed architecture achieves an impressive root-mean-squared-error (RMSE) of 0.05 and mean-squared-error (MSE) of 0.03 for the NSA model and an RMSE of 0.12 for the symbolic regression model alone. We train this model to handle higher-order multipole waveforms, with a specific focus on eccentric candidates, which are known to exhibit unique characteristics. Our results provide a robust and interpretable framework for predicting spin magnitudes in mergers. This has implications for understanding the astrophysical properties of black holes and deciphering the physics underlying the GW signals.