Exploring TeV Candidates of Fermi Blazars through Machine Learning

Abstract

In this work, we make use of a supervised machine-learning algorithm based on Logistic Regression (LR) to select TeV blazar candidates from the 4FGL-DR2/4LAC-DR2, 3FHL, 3HSP, and 2BIGB catalogs. LR constructs a hyperplane based on a selection of optimal parameters, named features, and hyperparameters whose values control the learning process and determine the values of features that a learning algorithm ends up learning, to discriminate TeV blazars from non-TeV blazars. In addition, it gives the probability (or logistic) that a source may be considered a TeV blazar candidate. Non-TeV blazars with logistics greater than 80% are considered high-confidence TeV candidates. Using this technique, we identify 40 high-confidence TeV candidates from the 4FGL-DR2/4LAC-DR2 blazars and we build the feature hyperplane to distinguish TeV and non-TeV blazars. We also calculate the hyperplanes for the 3FHL, 3HSP, and 2BIGB. Finally, we construct the broadband spectral energy distributions for the 40 candidates, testing for their detectability with various instruments. We find that seven of them are likely to be detected by existing or upcoming IACT observatories, while one could be observed with extensive air shower particle detector arrays.