Speaker
Dr
Lorenzo Monti
(Istituto Nazionale di Astrofisica (INAF))
Description
Metallicity is a key parameter in stellar evolution, yet its determination often relies on high-resolution spectra, limiting observational coverage. In this work, we explore the use of Transformer-based models to infer metallicity directly from photometric light curves, leveraging their sequential learning capabilities and attention to temporal patterns. Our results show performance comparable to the best RNN model from our previous work. However, with the upcoming Gaia DR4 data release—providing longer time-series observations—we anticipate significant improvements in accuracy and robustness. This approach paves the way for large-scale metallicity analysis in photometric surveys.
