Astronomical Data

Astronomical data analysis is undergoing a transformation driven by the increasing volume and complexity of observations, coupled with the rise of machine learning. Current research focuses on developing and applying specialized large language models (LLMs), convolutional neural networks (CNNs), transformers, and other deep learning architectures to tasks such as object classification, redshift prediction, parameter inference, and anomaly detection in diverse astronomical datasets (e.g., images, light curves, spectra). These advancements are improving the efficiency and accuracy of astronomical research, enabling the extraction of more nuanced information from existing and future datasets, and facilitating new scientific discoveries. The development of robust evaluation frameworks and publicly available datasets is also a key focus to ensure reproducibility and facilitate broader adoption of these powerful techniques.