Stellar Parameter

Stellar parameter determination, focusing on properties like effective temperature, surface gravity, and metallicity, is crucial for understanding stellar evolution and galactic structure. Current research heavily utilizes machine learning, employing diverse architectures such as artificial neural networks, convolutional neural networks, recurrent neural networks, and transformers, to analyze data from various sources including high-resolution spectra, light curves, and astrometric measurements. These advanced techniques improve the accuracy and efficiency of parameter estimation, particularly for large datasets and low signal-to-noise observations, enabling more precise characterization of stellar populations and facilitating studies of exoplanet host stars. The resulting improvements in data analysis are significantly impacting our understanding of stellar physics and galactic evolution.