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In the forthcoming era of big astronomical data, it is a burden to find out target sources from ground-based and space-based telescopes. Although Machine Learning (ML) methods have been extensively utilized to address this issue, the incorporation of in-depth data analysis can significantly enhance the efficiency of identifying target sources when dealing with massive volumes of astronomical data. In this work, we focused on the task of finding AGN candidates and identifying BL Lac/FSRQ candidates from the 4FGL DR3 uncertain sources. We studied the correlations among the attributes of the 4FGL DR3 catalogue and proposed a novel method, named FDIDWT, to transform the original data. The transformed dataset is characterized as low-dimensional and feature-highlighted, with the estimation of correlation features by Fractal Dimension (FD) theory and the multi-resolution analysis by Inverse Discrete Wavelet Transform (IDWT). Combining the FDIDWT method with an improved lightweight MatchboxConv1D model, we accomplished two missions: (1) to distinguish the Active Galactic Nuclei (AGNs) from others (Non-AGNs) in the 4FGL DR3 uncertain sources with an accuracy of 96.65%, namely, Mission A; (2) to classify blazar candidates of uncertain type (BCUs) into BL Lacertae objects (BL Lacs) or Flat Spectrum Radio Quasars (FSRQs) with an accuracy of 92.03%, namely, Mission B. There are 1354 AGN candidates in Mission A, 482 BL Lacs candidates and 128 FSRQ candidates in Mission B were found. The results show a high consistency of greater than 98% with the results in previous works. In addition, our method has the advantage of finding less variable and relatively faint sources than ordinary methods.

The `blazar sequence' has been proposed for more than 20 years, yet its nature is still unclear. In this work, for the first time, we expand this topic to the TeV band by using a sample of 58 TeV blazars including 48 blazars in the quiescent state and 21 blazars in the flaring state. We investigate the correlation between the TeV luminosity, which has been compensated for attenuation from extragalactic background light, and the synchrotron peak frequency. We note that there is no correlation between TeV luminosity and peak frequency in the quiescent state and a strong anti-correlation in the flaring state for the observed value. However, there is a strong positive correlation in both the quiescent state and the flaring state for the intrinsic value. This indicates that the blazar sequence is shown in the flaring state rather than in the quiescent state for the observed value and the blazar sequence is not present in both two states after removing the beaming effect. In addition, to confirm whether the beaming effect results in the blazar sequence, we compare the \textit{Fermi} $\gamma$-ray luminosity between the quiescent state and the flaring state. We find the \textit{Fermi} $\gamma$-ray luminosity in the flaring state is greater than that in the quiescent state and the Doppler factor in the flaring state is greater. We suggest the blazar sequence in the flaring state may be due to the stronger beaming effect., Comment: 19 pages, 4 figures and 4 tables. Accepted to APJ

Jet origination is one of the most important questions of AGN, yet it stays obscure. In this work, we made use of information of emission lines, spectral energy distributions (SEDs), \textit{Fermi}-LAT $\gamma$-ray emission, construct a blazar sample that contains 667 sources. We notice that jet power originations are different for BL Lacs and for FSRQs. The correlation between jet power $P_{\rm jet}$ and the normalized disk luminosity $L_{\rm Disk}/L_{\rm Edd}$ shows a slope of -1.77 for BL Lacs and a slope of 1.16 for FSRQs. The results seem to suggest that BL Lac jets are powered by extracting blackhole rotation energy, while FSRQ jets are mostly powered by accretion disks. Meanwhile, we find the accretion ratio $\dot{M} / \dot{M}_{\rm Edd}$ increase with the normalized $\gamma$-ray luminosity. Base on this, we propose a dividing line, ${\rm log} (L_{\rm BLR}/L_{\rm Edd}) = 0.25 \ {\rm log} (L_{\rm \gamma}/L_{\rm Edd}) - 2.23$, to separate FSRQs and BL Lacs in the diagram of $L_{\rm BLR}/L_{\rm Edd}$ against $L_{\rm \gamma}/L_{\rm Edd}$ through using the machine learning method, the method gives an accuracy of 84.5\%. In addition, we propose an empirical formula, $M_{\rm BH}/M_{\rm \odot} \simeq L_{\rm \gamma}^{0.65}/21.46$, to estimate blackhole mass based on a strong correlation between $\gamma$-ray luminosity and blackhole mass. Strong $\gamma$-ray emission is typical in blazars, and the emission is always boosted by a Doppler beaming effect. In this work, we generate a new method to estimate a lower-limit of Doppler factor $\delta$ and give $\delta_{\rm BL Lac} = 7.94$ and $\delta_{\rm FSRQ} = 11.55$., Comment: Accepted to ApJ