Pulsars are rapidly rotating, highly magnetized neutron stars that emit beams of electromagnetic radiation, observed as periodic pulses when those beams reach Earth. In modern radio surveys, the candidate selection problem arises because millions of pulsar-like signals are detected, making it challenging to reliably identify true pulsars. My project focuses on solving the candidate selection problem of pulsars through the use of a Bayesian neural networks (BNN).

With the help of the Department of Physics & Astronomy at San Francisco State University, spectral energy distribution data will be gathered from the most recent Fermi Large-Area Telescope and pulsar catalogs. These will be used as the BNN's input data to classify millisecond and young pulsars with reliable probability distribution estimates. Based on these uncertainty quantifications, other researchers will be able to determine which high-confidence candidates to pursue in future studies. This will be significant in discovering new objects in our universe and understanding the Galactic structure.