Speaker
Balázs Pál
(Wigner Research Centre for Physics)
Description
Examining the properties of stellar populations in and around the Milky Way is a crucial step towards the understanding of galactic evolution. Gaining insight into this process can provide us valuable information about the large-scale and long-term characteristics of both ordinary and dark matter. In this study we focused on the spectroscopic aspect of this investigation, by looking into how well autoencoder-based neural networks (AEs) perform in the processing and analysis of stellar spectra. We show that AEs are capable of learning the physical characteristics of stellar spectra to a considerable extent, even in noisy and low S/N conditions.
Authors
Balázs Pál
(Wigner Research Centre for Physics)
Laszlo Dobos
(E)
