Metallicities of red giants in S-PLUS

We combined the five broadband and the seven narrowband filters of S-PLUS 'especially centred on prominent stellar spectral features' to train Machine Learning algorithms. The training catalogue consists of the cross-matching between the S-PLUS and Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) survey catalogues.

The classification neural network uses the colours (J0378-u), (J0395-g), (J0410-g), (J0515-g), (J0660-r), (g-z), (r-i) as input features, whereas the network for metallicities uses the (J0378-u), (J0395-g), (J0410-g), (J0515-g), (J0660-r), (u-g) and (r-z) colours as input features. The resulting network is capable of identify ~99% of the giants in the test set. The network for determining the photometric metallicities of giant stars estimates metallicities in the test set a with a standard deviation of {sigma}_giants ~0.07dex with respect to the spectroscopic values.