Speaker
Description
The Fermi Large Area Telescope (LAT) has significantly advanced our understanding of the high-energy gamma-ray sky, yet nearly one third of the sources in the Fourth Fermi-LAT Source Catalog (4FGL) remain unassociated with known astrophysical objects. Traditional machine learning approaches used to classify these sources typically treat spectral features as independent tabular variables and often rely on spatial coordinates, which can introduce biases related to source location.In this work, we present a 1D convolutional neural network (1D-CNN)–based hierarchical deep learning framework designed to classify unassociated FermiLAT sources by exploiting the intrinsic structure of their spectral and variability properties while avoiding the use of galactic coordinates. Our primary goal is to identify high-confidence pulsar candidates and further distinguish betweenYoung Pulsars and Millisecond Pulsars (MSPs), providing valuable targets for future radio observations.We developed a 1D-CNN architecture, named TabularResCNN, that interprets the spectral data from the 4FGL-DR4 catalog as sequential signals, enabling the model to capture local correlations and spectral curvature within the SpectralEnergy Distribution (SED). The classification is performed hierarchically: first separating Active Galactic Nuclei (AGNs) from Pulsars and, then distinguishing Young Pulsars from MSPs. To address class imbalance, we employ a cost sensitive learning strategy, and we use Grad-CAM techniques to assess the interpretability of the model.Our model achieves an accuracy of ~98 % in separating AGNs from Pulsars and~81 % in distinguishing Young Pulsars from MSPs. Applying the method to ~2500 unassociated sources, we identify ~200 pulsar candidates and ~1100 AGN candidates. The predicted populations show strong astrophysical consistency in their spatial and physical distributions, demonstrating the potential of this approach to guide future pulsar searches with facilities such as FAST and SKAO.