Seminario: Marco Tarantino (UniPa)

When:

17 December 2025 @ 11:00 – 12:00

2025-12-17T11:00:00+01:00

2025-12-17T12:00:00+01:00

Contact:

Giulia Piccinini

Titolo: Using a Neural Network approach and Starspots dependent models to predict Effective Temperatures and Ages of young stars

Abstract:

This study presents a statistical approach to accurately predict the effective temper-

atures of pre-main sequence stars, which are necessary for determining stellar ages

using the isochrone methodology and cutting-age starspots-dependent models. By

training a Neural Network model on high-quality spectroscopic temperatures from

the Gaia-ESO Survey as the response variable, and using photometric data from

Gaia DR3 and 2MASS catalogs as explanatory variables, we implemented a method-

ology to accurately derive the effective temperatures of much larger populations of

stars for which only photometric data are available. The model demonstrated robust

performance for low-mass stars with temperatures below 7 000 K, including young

stars, the primary focus of this work. Predicted temperatures were employed to con-

struct Hertzsprung-Russell diagrams and to predict stellar ages of different young

clusters and star forming regions through isochrone interpolation, achieving excellent

agreement with spectroscopic-based ages and literature values derived from model-

independent methods like lithium equivalent widths. The inclusion of starspot evolu-

tionary models improved the age predictions, providing a more accurate description

of stellar properties. Additionally, by applying this temperature–age framework to

a large, spatially complete sample of young stars in the solar neighbourhood, we in-

vestigate the recent star formation and how it relates to the structure of the Local

Bubble, thereby describing its role in influencing the local star-formation history.