# Evaluating the optical classification of Fermi BCUs using machine learning

## TL;DR

This study applies four supervised machine learning algorithms to classify uncertain blazar sources in the Fermi-LAT catalog using observational data, achieving high accuracy and robustness in distinguishing FSRQs from BL Lacs.

## Contribution

It introduces a comparative analysis of machine learning methods for classifying Fermi blazar candidates of uncertain type based on multi-parameter observational data.

## Key findings

- All four machine learning methods perform well with high accuracy.
- The methods classify approximately 25% FSRQs and 75% BL Lacs among BCUs.
- Mclust Gaussian Mixture Model achieves the highest accuracy of 80%.

## Abstract

In the third catalog of active galactic nuclei detected by the Fermi-LAT (3LAC) Clean Sample, there are 402 blazars candidates of uncertain type (BCU). Due to the limitations of astronomical observation or intrinsic properties, it is difficult to classify blazars using optical spectroscopy. The potential classification of BCUs using machine learning algorithms is essential. Based on the 3LAC Clean Sample, we collect 1420 Fermi blazars with 8 parameters of {\gamma}-ray photon spectral index, radio flux, flux density, curve significance, the integral photon flux in 100 to 300 MeV, 0.3 to 1 GeV, 10 to 100 GeV and variability index. Here, we apply 4 different supervised machine learning (SML) algorithms (\emph{Decision trees, Random forests, support vector machines and Mclust Gaussian finite mixture models}) to evaluate the classification of BCUs based on the direct observational properties. All the 4 methods can perform exceedingly well with a more accuracy and can effective forecast the classification of Fermi BCUs. The evaluating results show the results of these methods (SML) are valid and robust, where, about 1/4 sources are FSRQs and 3/4 are BL Lacs in 400 BCUs, which are consistent with some other recent results. Although a number of factors influence the accuracy of SML, the results are stable at a fixed ratio 1:3 between FSRQs and BL Lacs, which suggests that the SML can provides an effective method to evaluate the potential classification of BCUs. Among the 4 methods, Mclust Gaussian Mixture Modelling has the highest accuracy for our training sample (4/5, seed=123).