An image processing approach to identify solar plages observed at 393.37 nm by the Kodaikanal solar observatory

Solar plages, which are bright regions on the Sun’s surface, are an important indicator of solar activity. In this study, we propose an automated algorithm for segmenting solar plages in Ca-K wavelength solar data obtained from the Kodaikanal Solar Observatory.

Gamma-ray Blazar Classification using Machine Learning with Advanced Weight Initialization and Self-Supervised Learning Techniques

Machine learning has emerged as a powerful tool in the field of gamma-ray astrophysics. The algorithms can distinguish between different source types, such as blazars and pulsars, and help uncover new insights into the high-energy universe. In this work, we employ ML algorithms to achieve the same.

Estimation of redshift and associated uncertainty of Fermi/LAT extra-galactic sources with Deep Learning

Predicting the redshifts of sources in the 4LAC-DR3 catalog and estimating the associated uncertainty with the help of variational inference.

A Bayesian CNN approach to SDSS image data classification

In recent years, numerous algorithms have been proposed that demonstrate remarkable performance in classifying galaxies into multiple classes. However, none of these methods account for uncertainty or solve the problem of overconfident predictions. In this study, we propose a Bayesian CNN.