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Stellar light curves contain valuable information about oscillations andgranulation, offering insights into stars' internal structures and evolutionarystates. Traditional asteroseismic techniques, primarily focused on powerspectral analysis, often overlook the crucial phase information in these lightcurves. Addressing this gap, recent machine learning applications, particularlythose using Convolutional Neural Networks (CNNs), have made strides ininferring stellar properties from light curves. However, CNNs are limited bytheir localized feature extraction capabilities. In response, we introduce$\textit{Astroconformer}$, a Transformer-based deep learning framework,specifically designed to capture long-range dependencies in stellar lightcurves. Our empirical analysis centers on estimating surface gravity ($\logg$), using a dataset derived from single-quarter Kepler light curves with $\logg$ values ranging from 0.2 to 4.4. $\textit{Astroconformer}$ demonstratessuperior performance, achieving a root-mean-square-error (RMSE) of 0.017 dex at$\log g\approx3$ in data-rich regimes and up to 0.1 dex in sparser areas. Thisperformance surpasses both K-nearest neighbor models and advanced CNNs.Ablation studies highlight the influence of receptive field size on modeleffectiveness, with larger fields correlating to improved results.$\textit{Astroconformer}$ also excels in extracting $\nu_{\max}$ with highprecision. It achieves less than 2% relative median absolute error for 90-dayred giant light curves. Notably, the error remains under 3% for 30-day lightcurves, whose oscillations are undetectable by a conventional pipeline in 30%cases. Furthermore, the attention mechanisms in $\textit{Astroconformer}$ alignclosely with the characteristics of stellar oscillations and granulationobserved in light curves.

Astroconformer: The Prospects of Analyzing Stellar Light Curves with Transformer-Based Deep Learning Models