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In today's era, a tremendous amount of data is generated by differentobservatories and manual classification of data is something which ispractically impossible. Hence, to classify and categorize the objects there aremultiple machine and deep learning techniques used. However, these predictionsare overconfident and won't be able to identify if the data actually belongs tothe trained class. To solve this major problem of overconfidence, in this studywe propose a novel Bayesian Neural Network which randomly samples weights froma distribution as opposed to the fixed weight vector considered in thefrequentist approach. The study involves the classification of Stars and AGNsobserved by XMM Newton. However, for testing purposes, we consider CV, Pulsars,ULX, and LMX along with Stars and AGNs which the algorithm refuses to predictwith higher accuracy as opposed to the frequentist approaches wherein theseobjects are predicted as either Stars or AGNs. The proposed algorithm is one ofthe first instances wherein the use of Bayesian Neural Networks is done inobservational astronomy. Additionally, we also make our algorithm to identifystars and AGNs in the whole XMM-Newton DR11 catalogue. The algorithm almostidentifies 62807 data points as AGNs and 88107 data points as Stars with enoughconfidence. In all other cases, the algorithm refuses to make predictions dueto high uncertainty and hence reduces the error rate.

A Bayesian Neural Network Approach to identify Stars and AGNs observed by XMM Newton