Classifying blazar candidates from the 3FGL unassociated catalog into BL Lacs and FSRQs using Swift and WISE data

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 $\ge$ 99% likley to be blazars. We then utilize Swift$-$XRT, 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 on P${}_{bll}$, which is the probability for each source to be a BL Lac, we placed our sources into five different categories based on this value as follows; P${}_{bll}$ $\ge$ 99%: highly likely BL Lac, P${}_{bll}$ $\ge$ 90%: likely BL Lac, P${}_{bll}$ $\le$ 1%: highly likely FSRQ, P${}_{bll}$ $\le$ 10%: likely FSRQ, and 90% $<$ P${}_{bll}$ $<$ 10%: ambiguous. Our results categorize the 84 blazar candidates as 50 likely BL Lacs and the rest 34 being ambiguous. A small subset of these sources have been listed as associated sources in the most recent Fermi catalog, 4FGL, and in these cases our results are in agreement on the classification.

13 pages, 4 figures, 2 tables, accepted for publication in AJ

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Cited by in corpus (2)

• Classifications of Fermi-LAT unassociated sources in multiple machine learning methods
• Classification of blazar candidates of unknown type in Fermi 4LAC by unanimous voting from multiple Machine Learning Algorithms