Exploring Evolved Stars: Bolometric Luminosities, SiO Masers, and Machine Learning Classification.

  • CART Astrophysics Seminar Series

April 3, 2025 2:00 PM
PAIS 3205

Host:
Greg Taylor
Presenter:
Rajorshi Bhattacharya (UNM)
The Bulge Asymmetries and Dynamical Evolution (BAaDE) survey aims to enhance our understanding of Galactic structure by studying 28,062 infrared-selected red giant stars, predominantly Mira-type Asymptotic Giant Branch (AGB) stars. A key challenge in characterizing these stars is obtaining accurate bolometric luminosities, which require precise extinction corrections and distance estimates. Once the absolute magnitude is derived, bolometric corrections (BC) must be applied, which vary significantly based on factors such as metallicity, stellar type, position, and available photometric data. Furthermore, deriving bolometric magnitudes using stellar periods is complicated by the absence of a universal Period-Luminosity (P-L) relation, particularly for Galactic Miras, where metallicity differences compared to the Large Magellanic Cloud (LMC) introduce additional challenges. In this talk, I will present results for 15,000 AGB stars from the BAaDE survey, where distances were estimated using spectral energy distribution (SED) fitting, assuming similar luminosities for sources with matching colors. Extinction-corrected photometric data in the near- and mid-infrared were used to derive absolute magnitudes, which were then converted to bolometric magnitudes using various bolometric corrections (BCs). I will discuss the impact of different BC methods, compare our results with previous luminosity estimations based on radiative transfer modeling, Period-Luminosity (P-L) relations, and fixed distance assumptions, and highlight the complexities of deriving bolometric luminosities for a diverse AGB population. This work aims to establish a consistent approach for large-scale luminosity estimation, providing insights into the inner Galaxy’s stellar population.  Also discussed will be results on mass-loss rates for a subset of these AGB stars using the luminosities, providing insights into their late-stage stellar evolution. Additionally, I will also present preliminary results on SiO maser variability of 20 AGB sources from BAaDE with known periods and optical light curves. Beyond AGB stars, I will also discuss how machine learning techniques can be used to classify evolved stars based on their photometric features.

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