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span.s1 {font-kerning: none}If a source is located behind a foreground object, the light from the source would be blended by the foreground one. Therefore, around the foreground object, we are able to find several images of the source. We call this phenomenon as gravitational lensing. In this thesis, we study the galaxy-scale strong gravitational lensing, including its searchings and applications. (1). Based on spectroscopic-selection method, we introduce Machine Learning (ML) to the searchings of new lens candidates. The foreground galaxies are early-type galaxies (ETGs) at middle redshift ($z\sim 0.5$) and the source galaxies are Ly$\alpha$ emitters (LAEs) at high redshift ($2 < z < 3$). We build a 28 layers deep Residual Networks (ResNet) model, and then train it with artificially training data. After finish the training, we apply the ResNet model to our predictive data, we find 161 of the 174 known candidates. Apart from the known candidates, we also find 5 new lens candidates. (2). Fitting the lensing systems is one of the important procedures in lensing study. As an example, we will introduce the lensing fitting procedure of a well known lensing system named Cosmic Horseshoe (SDSS J1004+4112). (3). We next study the mass properties with the known lensing samples. During the study, we need to fiiting the lensing systems to obtain the parameters of the systems (e.g., the Einstein radius, the effective radius). we also need to use the jiont analysis of gravitational lensing and dynamical data to obtain the mass distribution of the foreground galaxies. Assuming the mass density profile of ETGs is power-law $\rho\propto r^{-\gamma}$, we find the total mass distribution is very close to isothermal one and the density profile would be steeper with increasing radius, which is consistent with previous numerical simulation depending on galactic wind and active galactic nuclei (AGN) feedback. (4). The power-law mass-density profile for the inner region of ETGs is a basic assumption in the jiont analysis of gravitational lensing and dynamical data, and it seems to be reasonable, but no one has theoretically explained it. However, we find it is a natural inference of Rastall gravity. We derive a power-law mass-density profile for ETGs from Rastall gravity under the assumption of the perfect fluid matter. We find the Rastall dimensionless parameter $\beta$ determines the mass distribution of ETGs. We then we use 118 galaxy-galaxy strong gravitational lens systems to constrain the parameter $\beta(=\kappa\lambda)$ in Rastall gravity and obtain $\beta=0.163\pm0.001$(68\% confidence level), satisfying the strong energy condition and the weak energy condition. We also find that an absolutely isothermal mass distribution for ETGs is not allowed in the Rastall gravity frameworks. (5). Another basic assumption in the jiont analysis of gravitational lensing and dynamical data is the lensing mass equaling to the dynamical mass obtained from velocity dispersion. However, these two mass are obtained by different method, and the error from the method may leads different value of the mass. We investigate the discrepancy between the two-dimensional projected lensing mass and the dynamical mass for an ensemble of 97 strong gravitational lensing systems. For the singular isothermal sphere (SIS) mass model, we obtain that the Einstein mass is 20.7\% more than the dynamical mass. For more general power-law mass model, the discrepancy still exists.
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