Research
Determining Distances with Post-AGB Stars in Globular Clusters
Globular clusters are dense, gravitationally bound groups of thousands to millions of stars, containing evolved stars such as horizontal branch (HB) and asymptotic giant branch (AGB) stars. Theory predicts that post-AGB (PAGB) stars occupy a narrow luminosity strip at the top of the Hertzsprung-Russell diagram and are promising standard candles. Using UV–IR photometry and Gaia astrometry, we identify intermediate-temperature and hot PAGB stars in metal-poor clusters and robustly determine their effective temperatures and luminosities. We find that PAGB stars occupy a narrow luminosity range centered at log(Lbol/L⊙) = 3.24, with a dispersion of 0.05 dex, supporting the hypothesis that these objects are reliable Population II distance indicators that can be used for precise extragalactic distance measurements, as demonstrated by Ciardullo et al. 2022.
Inferring Properties of Brown Dwarfs with Data-Driven Models
Brown dwarfs are substellar objects more massive than planets (\( M \geq 13 M_{\text{Jup}} \)) whose central temperatures and densities are not sufficient for hydrogen fusion to occur (\( M \leq 70 M_{\text{Jup}} \)). Spectroscopic analysis is crucial for classifying brown dwarfs and determining properties like luminosity, temperature, and surface gravity. However, accurately measuring these parameters is challenging due to their evolution and cooling, along with the difficulties in characterizing their atmospheric properties.
Machine learning has been used in astronomical research as a tool to process and analyze large amounts of data, as well as to extract novel information. The Cannon has been utilized to infer stellar parameters and abundances from spectroscopic data by employing "known stellar labels". Using The Cannon we have developed a novel method to determine physical properties of brown dwarfs using data-driven models with results that are competitive to other techniques.