Abu Bucker Siddik, Diane Oyen, Soumi De, Greg Olmschenk, Constantinos Kalapotharakos

Probing properties of neutron stars from photometric observations of these objects helps us answer crucial questions at the forefront of multi-messenger astronomy, such as, what is behavior of highest density matter in extreme environments and what is the procedure of generation and evolution of magnetic fields in these astrophysical environments? However, uncertainties and degeneracies-where different parameter sets produce similar light curves-make this task challenging. We propose a deep learning framework for inferring pulsar parameters from observed light curves. Traditional deep learning models are not designed to produce multiple degenerate solutions for a given input. To address this, we introduce a custom loss function that incorporates a light curve emulator as a forward model, along with a dissimilarity loss that encourages the model to capture diverse, degenerate parameter sets for a given light curve. We further introduce a test-time optimization scheme that refines predicted parameters by minimizing the discrepancy between the observed light curve and those reconstructed by the forward model from predicted parameters during inference. The model is trained using a suite of state-of-the-art simulated pulsar light curves. Finally, we demonstrate that the parameter sets predicted by our approach reproduce light curves that are consistent with the true observation.