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The prediction of spin magnitudes in binary black hole and neutron starmergers is crucial for understanding the astrophysical processes andgravitational wave (GW) signals emitted during these cataclysmic events. Inthis paper, we present a novel Neuro-Symbolic Architecture (NSA) that combinesthe power of neural networks and symbolic regression to accurately predict spinmagnitudes of black hole and neutron star mergers. Our approach utilizes GWwaveform data obtained from numerical relativity simulations in the SXSWaveform catalog. By combining these two approaches, we leverage the strengthsof both paradigms, enabling a comprehensive and accurate prediction of spinmagnitudes. Our experiments demonstrate that the proposed architecture achievesan 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 regressionmodel alone. We train this model to handle higher-order multipole waveforms,with a specific focus on eccentric candidates, which are known to exhibitunique characteristics. Our results provide a robust and interpretableframework for predicting spin magnitudes in mergers. This has implications forunderstanding the astrophysical properties of black holes and deciphering thephysics underlying the GW signals.

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