SEMINÁRIO DO DEPARTAMENTO DE ASTRONOMIA
Searching for different AGN populations in massive datasets with Machine Learning
a talk by Paula Sanchez Saez de (ESO/Germany) - In-Person
Abstract:
Metal-poor stars are key to our understanding of the early stages of chemical evolution in the Universe. New multifilter surveys, such as the Southern Photometric Local Universe Survey (S-PLUS), are greatly advancing our ability to select low-metallicity stars in our Galaxy. In a series of papers, we have selected and analyzed a sample of 522 metal-poor candidates in S-PLUS. Follow-up observations in Blanco and Gemini South telescopes, using the COSMOS and GMOS spectrographs, confirmed that 92% of the selected stars have [Fe/H] < -1, while 83% have [Fe/H] < -2, and 15% [Fe/H] < -3. Our sample also includes two stars with spectroscopic metallicity below -4. Based on this sample, we proposed further selection criteria by combining S-PLUS and Gaia DR3 data and showed that we were able to improve the purity of the selection, achieving fractions of 99.5% and 91% for [Fe/H] ≤ -1 and [Fe/H] ≤ -2, respectively. We also performed high-resolution spectroscopic follow-ups for two peculiar stars in the sample: i) SPLUS J2104−0049 was observed in the Magellan-Clay telescope and found to be the ultra metal-poor star ([Fe/H] < -4) with the lowest ever detected carbon abundance (at the time of our publication). ii) More recently, SPLUS J1424-2542 was observed during the system verification of the GHOST instrument, installed at the Gemini South telescope. Our preliminary results reveal that this star is not only a metal-poor star with low carbon abundance, but is also enriched in r-process elements.
Short-Bio:
PhD from Universidad de Chile (2019), postdoc at PUC and MAS (2019-2021), ESO Garching fellow (2021 - 2024), ESO Garching Staff astronomer (2024 - present)
Link da transmissão: https://www.youtube.com/c/AstronomiaIAGUSP/live