J. T. Zhu | Searcharxiv

arXiv2023

The jet apparent motion and central engine study of Fermi blazars

The study of blazar jet has been performed for several decades via the VLBI technique, while its generation and propagation stay unclear. In the present work, we compiled a sample of 407 VLBI detected \textit{Fermi} blazars (VFBs) and studied the correlations between apparent velocity (${\rm log}\,β_{\rm app}$) and jet/accretion disk properties. We found a positive correlation between $γ$-ray luminosity (${\rm log}\,L_{\rm γ}$) and ${\rm log}\,β_{\rm app}$, the correlation suggests that the apparent motion of jet knot is related to the jet power.

astro-ph.HE↗

arXiv2023

Exploring TeV candidates of Fermi blazars through machine learning

In this work, we make use of a supervised machine learning algorithm based on Logistic Regression (LR) to select TeV blazar candidates from the 4FGL-DR2 / 4LAC-DR2, 3FHL, 3HSP, and 2BIGB catalogs. LR constructs a hyperplane based on a selection of optimal parameters, named features, and hyper-parameters whose values control the learning process and determine the values of features that a learning algorithm ends up learning, to discriminate TeV blazars from non-TeV blazars. In addition, it gives the probability (or logistic) that a source may be considered as a TeV blazar candidate. Non-TeV blazars with logistics greater than 80% are considered high-confidence TeV candidates. Using this technique, we identify 40 high-confidence TeV candidates from the 4FGL-DR2 / 4LAC-DR2 blazars and we build the feature hyper-plane to distinguish TeV and non-TeV blazars. We also calculate the hyper-planes for the 3FHL, 3HSP, and 2BIGB. Finally, we construct the broadband spectral energy distributions (SED) for the 40 candidates, testing for their detectability with various instruments. We find that 7 of them are likely to be detected by existing or upcoming IACT observatories, while 1 could be observed with EAS particle detector arrays.

astro-ph.HE↗