Speaker
Description
In the realm of radio astronomy, the discovery and analysis of binary pulsars present a unique opportunity to test the theories of gravity, particularly General Relativity, in the strong-field limit. PulsarNet introduces an advanced machine learning pipeline specifically tailored for this purpose. This pipeline uniquely processes the Fourier amplitude spectrum to identify binary pulsar candidates with increased efficiency and accuracy. It consists of two key components: a Support Vector Machine (SVM) classifier that effectively filters out non-signal segments in the Fourier domain and an attention-based neural network (NN) regressor that precisely predicts the Keplerian parameters of potential pulsars. PulsarNet stands out by significantly accelerating the discovery process compared to traditional template match-based methods, while achieving comparable sensitivity in the white-noise regime.
