Degeneracy-Aware Pulsar Parameter Estimation from Light Curves via Deep Learning and Test-Time Optimization

TL;DR

This paper presents a deep learning framework with a custom loss and test-time optimization to infer degenerate pulsar parameters from light curves, improving the understanding of neutron star properties in multi-messenger astronomy.

Contribution

It introduces a novel deep learning approach that captures parameter degeneracies and refines predictions through test-time optimization for pulsar light curve analysis.

Findings

The model accurately reproduces observed light curves from predicted parameters.

The approach captures multiple degenerate solutions for the same light curve.

Predicted parameters yield light curves consistent with observations.

Abstract

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.