Searching for chemical classes among metal-poor stars using medium-resolution spectroscopy.

Astronomy is in the era of large spectroscopy surveys, with the spectra of hundreds of thousands of stars in the Galaxy being collected. Although most of these surveys have low or medium resolution, which makes precise abundance measurements not possible, there is still important information to be extracted from the available data. Our aim is to identify chemically distinct classes among metal-poor stars, observed by the Sloan Digital Sky Survey, using line indices. The present work focused on carbon-enhanced metal-poor (CEMP) stars and their subclasses. We applied the latent profile analysis technique to line indices for carbon, barium, iron and europium, in order to separate the sample into classes with similar chemical signatures. This technique provides not only the number of possible groups but also the probability of each object to belong to each class. The method was able to distinguish at least two classes among the observed sample, with one of them being probable CEMP stars enriched in s-process elements. However, it was not able to separate CEMP-no stars from the rest of the sample. Latent profile analysis is a powerful model-based tool to be used in the identification of patterns in astrophysics. Our tests show the potential of the technique for the attainment of additional chemical information from 'poor' data. [ABSTRACT FROM AUTHOR]