Speaker
Description
The field of astronomy is experiencing a profound shift driven by machine learning, particularly deep learning, which enables efficient processing of vast datasets, surpassing human capabilities in complex data analysis. In this talk, I will showcase the diverse capabilities of AI in astronomy, focusing on tasks related to star formation and the interstellar medium. I will present our AI methodologies, including discriminative and generative models, applied to tasks such as segmenting stellar feedback structures from molecular line data cubes, inferring outflow properties through regression analysis, and determining magnetic field directions. Additionally, we tackle challenging tasks involving the inference of physical quantities like volume density, interstellar radiation field, and magnetic field strength. Through this presentation, I aim to highlight the transformative impact of AI on data analysis in astronomy and encourage exploration of these cutting-edge technologies.
| Session Selection | Astronomy and Astrophysics |
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