Classifying blazar candidates from the 3FGL unassociated catalog into BL Lacs and FSRQs using Swift and WISE data
Amanpreet Kaur, Abraham D. Falcone, Michael C. Stroh

TL;DR
This study employs machine learning with multi-wavelength data to classify unassociated gamma-ray sources as BL Lacs or FSRQs, improving identification accuracy of blazar subclasses.
Contribution
It introduces a Random Forest classifier integrating Swift-XRT, Fermi, and WISE data for blazar subclassification, achieving robust categorization of 84 candidates.
Findings
84 blazar candidates classified with high confidence
50 likely BL Lacs identified, 34 ambiguous
classification results agree with recent Fermi catalog
Abstract
We utilize machine learning methods to distinguish BL Lacertae objects (BL Lac) from Flat Spectrum Radio Quasars (FSRQ) within a sample of likely X-ray blazar counterparts to Fermi 3FGL unassociated gamma-ray sources. From our previous work, we have extracted 84 sources that were classified as 99% likley to be blazars. We then utilize SwiftXRT, Fermi, and WISE (The Wide-field Infrared Survey Explorer) data together to distinguish the specific type of blazar, FSRQs or BL Lacs. Various X-ray and Gamma-ray parameters can be used to differentiate between these subclasses. These are also known to occupy different parameter space on the WISE color-color diagram. Using all these data together would provide more robust results for the classified sources. We utilized a Random Forest Classifier to calculate the probability for each blazar to be associated with a BL Lac or an FSRQ. Based…
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