31 August 2026 to 4 September 2026
University of Malta
Europe/Malta timezone

Spectroscopy fine-tuning: the last step of machine-learning approach for UDGs

2 Sept 2026, 15:25
5m
Aula Prima (University of Malta)

Aula Prima

University of Malta

Valletta Campus, St Paul Street Valletta VLT 1216, Malta

Speaker

Antonio Vanzanella

Description

Low Surface Brightness Galaxies(LSBGs) are defined as galaxies with an average surface brightness luminosity greater than 23 magnitude arcsec-2 in the r-band, making them fainter than the night sky. Thuruthipilly et al.(2023) built a machine learning (ML) pipeline able to detect them in the Dark Energy Survey(DES) Data Release 1 using the optimal bands(r/g/z-bands), finding thousands of new candidates and creating an updated catalogue. This catalogue also contains more than 300 Ultra Diffuse Galaxies (UDGs) - a subtype of LBGs - characterised by having significant sizes but very faint luminosities. I will describe the results of the observation campaign of 22 UDGs candidates performed at the LBT using MODS instruments. Selected sources have a blue colour(g-r), indicating possible star-forming regions. Once reduced, the data showed clear emission [OII], Hⲁ and [OIII] lines. Data reduction also revealed Hᵧ and Hᵦ emission. The redshift measurement confirmed many of them as LSBGs, proving the goodness of the ML pipeline developed.

Presentation materials