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 $\geq$ 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${bll}$ $\geq$ 99%: highly likely BL Lac, P${bll}$ $\geq$ 90%: likely BL Lac, P${bll}$ $\leq$ 1%: highly likely FSRQ, P${bll}$ $\leq$ 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.