An investigation on the factors affecting machine learning classifications in gamma-ray astronomy. Issue 4 (20th January 2020) - Luo, Shengda; Leung, Alex P; Hui, C Y; Li, K L
ABSTRACT: We have investigated a number of factors that can have significant impacts on the classification performance of gamma-ray sources detected by Fermi Large Area Telescope (LAT) with machine learning techniques. We show that a framework of automatic feature selection can construct a simple model with a small set of features that yields better performance over previous results. Secondly, because of the small sample size of the training/test sets of certain classes in gamma-ray, nested re-sampling and cross-validations are suggested for quantifying the statistical fluctuations of the quoted accuracy. We have also constructed a test set by cross-matching the identified active galactic nuclei (AGNs) and the pulsars (PSRs) in the Fermi- LAT 8-yr point source catalogue (4FGL) with those unidentified sources in the previous 3rd Fermi -LAT Source Catalog (3FGL). Using this cross-matched set, we show that some features used for building classification model with the identified source can suffer from the problem of covariate shift, which can be a result of various observational effects. This can possibly hamper the actual performance when one applies such model in classifying unidentified sources. Using our framework, both AGN/PSR and young pulsar (YNG)/millisecond pulsar (MSP) classifiers are automatically updated with the new features and the enlarged training samples in 4FGL catalogue incorporated. Using a two-layer model with these updated classifiers, we have selected 20 promising MSP candidates with confidence scores 98 per cent from the unidentified sources in 4FGL catalogue that can provide inputs for a multiwavelength identification campaign.