International Conference on Machine Learning for Astrophysics 2nd Ed. - ML4ASTRO2

Session

Past and future multiwavelength all-sky surveys

8 Jul 2024, 14:30

Aula Magna (Catania)

Conveners

Past and future multiwavelength all-sky surveys: III

• Simone Riggi (Istituto Nazionale di Astrofisica (INAF))

221. Conditional Invertible Neural Networks for enhanced analysis of young, low-mass stars in Trumpler 14: Classical vs. Deep learning methodologies

Da Eun Kang

08/07/2024, 14:30

Oral Presentation

Low-mass stars account for the majority of the stars formed in star-forming regions and about half of the total stellar mass. Living longer than massive stars, low-mass stars still remain in the early phases of stellar evolution even when massive stars are dead and provide important information for studying stellar evolution and planet formation.
The spectral classification of stars...

220. Exploring the effects of external photoevaporation in Trumpler 14, with the use of neural networks

Katia Gkimisi

08/07/2024, 14:50

Stellar clusters often host massive stars interacting with the circumstellar material around low-mass stars. The large amounts of far-ultraviolet radiation (FUV) produced by these massive stars cause the effect of external photoevaporation, where gas from the outer layers of the disk around a low-mass star gets depleted. It has been shown that this process could directly affect the evolution...

223. [INVITED] From Data to Discovery: AI in Radio Astronomy

Tao An

08/07/2024, 15:10

The field of radio astronomy is entering a new era, driven by the Square Kilometre Array (SKA) and its precursors. As we are in the Big Data era, the integration of machine learning (ML) and deep learning (DL) techniques has become crucial to address the complex challenges posed by large and complicated datasets. This talk will explore the transformative potential of AI in radio astronomy,...

224. Applying Self-Organising Maps to identify and classify complex radio morphologies in next-generation radio surveys

Afrida Alam

08/07/2024, 15:50

Radio galaxies often exhibit complex morphologies that are challenging to identify using traditional source finders, necessitating visual inspection of radio maps. The large data volumes of the current and next generation of radio continuum surveys makes such visual inspection impractical. The objective of my research is to develop a more efficient method to identify and classify complex radio...