<![CDATA[Strong gravitational lensing serves as a powerful astrophysical probe, enabling studies of dark matter, galaxy structure, and cosmological parameters. The number of strong gravitational lensing candidates at the galaxy scale is expected to reach O ~ 5 with ongoing and future wide-field galaxy surveys. Current modeling techniques largely rely on conventional fitting methods, such as least squares or maximum likelihood using Markov Chain Monte Carlo, which despite their effectiveness, are computationally expensive and require manual inspection. This motivates the development of faster yet accurate parameter estimation techniques. In this work, we construct a representative training dataset and develop an efficient Convolutional Neural Network to estimate lens parameters: the Einstein radius, axis ratio, and position angle. We utilize data from Public Data Release 3 of the Hyper Suprime-Cam Subaru Strategic Program, selecting lens galaxies in the range 0.3 ≤ z ≤ 0.9 based on the strong-lens probability distribution. We find that the choice of loss function and regularization strategy is critical. To enhance model generalization, we leverage SpatialDropout, which outperforms standard methods by addressing the spatial correlation inherent in convolutional features. Furthermore, prediction accuracy and convergence speed are strongly affected by the distribution of the training data, highlighting the importance of an appropriate loss function. Our optimized model demonstrates robust performance, achieving a Mean Absolute Error of 0.092 arcsec for the Einstein radius, providing a scalable framework for automated analysis in future wide-field surveys.]]>
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