Blazars are a class of active galactic nuclei whose jets are aligned with the observer’s line of sight. They are powerful multi-frequency emitters that exhibit rapid and violent variation. Classification of blazars requires multi-frequency observation which may be able to achieve through careful planning. However, in the age of automated surveys, we might be able to complete the same task via data mining and machine learning. In this work, we explore the possibility of using the data from robotic surveys for blazar classifications, particularly the variability of their multi-frequency light curves. The 5 th edition of the Roma-BZCAT is used as our reference blazar catalog. The optical light curves of blazar studied here are taken from the Zwicky Transient Facility (ZTF) public Data Release 4 (2018-2020). The distributions of variability and fractional variability amplitudes in g and r bands are presented and compared for BL Lacs and FSRQs. The Principle Component Analysis (PCA) is then applied to various features extracted from the discrete correlation function (DCF) between the two bands as well as the variability and fractional variability amplitudes of the two bands. Although, our machine learning application for the the BL Lac-FSRQ classification shows unpromising result, the PCA has shown that around 80% of the populations can be explained with 13 features which mainly are the DCF-based ones.

Supermassive black hole (SMBH) mass determination is essential for understanding the galaxy-SMBH co-evolution. Photometric reverberation mapping (PRM) has been proposed as an alternative to the traditional method, spectroscopic reverberation mapping (SRM), which has limitation to only relatively low redshift, z, and bright objects. However, the most common and important sample of high-z active galaxies known as quasar or QSO have its populations peak at around z ≈ 2-3 thus out of reach for the SRM. We carried out a proof-of-concept campaign of quasar PRM using the 2.4-m Thai National Telescope (TNT) between 2015-2018. Such a study is important to inform a future wide-field high-cadence survey such as the Large Synoptic Survey Telescope (LSST). Our main sample contains 10 quasars at redshift z ≈ 0.7-1.2 with rSDSS = 19.7-20.7 mag, selected from the SDSS data release 10. The processed data and light curves were analysed using the discrete cross-correlation function (DCF). We used Monte Carlo (MC) simulations to model the noise characteristics and non-uniform coverage of our data as well as to verify robustness of the DCF results. Our analyses show a significant detection of lag time between continuum and broad-line emission bands of the quasar SDSS J081506.93+254124.7 (z = 1.18, rSDSS = 20.5 mag). The estimated broad line region (BLR) distance is 125±20 light-day which equate to the estimated SMBH mass of (4.3±2.0)×108 M #x2299;.

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