NGC 6822 is a barred irregular galaxy located about 1.6 million light years away in the Sagittarius constellation. We are observationally identifying red supergiants (RSGs) in this galaxy to compare with stellar evolutionary models. Stellar evolutionary theory provides us with the expected quantity of RSG populations. The research conducted will allow for a comparison between observational data to theoretical expectations. Here, we propose a new sample of RSG candidates in NGC 6822 that can be utilized as an observational test of such theory. RSG stars are the coolest of the evolved massive stars and have K and M spectral types and temperatures below 4100 K. Typically, they can be up to a thousand times the radius of the Sun and are therefore highly luminous. To find them in NGC 6822, we first used parallax and proper motion values from the GAIA satellite to filter out foreground stars, before using the NIR color-magnitude diagram to eliminate lower-mass asymptotic giant branch star contaminants. Next we transformed the J and K magnitudes to effective temperatures and luminosities to create an HR diagram (HRD), and selected RSGs based on their position on the HRD. Currently, we are comparing our results to previous spectroscopically confirmed RSGs. In combination with population studies done by ourselves and others in the Local Group galaxies IC 10, M31, M33, and the Magellanic Clouds, we can test model predictions across a wide range of metallicities. Additionally, by locating a population of RSGs in NGC 6822, future possibilities for studying these massive stars with direct spectroscopic follow-up are created.

This project a follow-up study to the research conducted by my mentors, Trever Dorn-Wallenstein and Dr. Emily Levesque on the use of a Support Vector Machine (SVM) classifier to classify massive stars (Dorn-Wallenstein et al. 2021). My project is to verify that the SVM classifier sorted all the stars correctly by analyzing high-resolution spectroscopic observations of the stars visible from the Apache Point Observatory, and possibly other telescopes in the future. A support vector machine is a supervised learning model used in many fields for classification, regression, and outliers detection. In the original project, a support vector machine took a table with ‘features’ for each star (here a feature is a color or magnitude or an estimate of the star’s variability) and found the N-dimensional plane in the feature-space that best separates each class from all the other classes. Simply put, you might imagine that if you had a bunch of red and blue stars with color and brightness/magnitude measurements, that plane would be a vertical line in the Hertzsprung-Russelldiagram with all the hotter blue stars to the left and all the cooler red stars to the right. The SVM algorithm’s job was to figure out the parameters that best described each category or, in other words, a general description of what classification is. There are lots of ways to accomplish this, an SVM is just one particular way to calculate what the mathematically “best” plane in the feature space is to separate classes. My project is to go through the catalog generated by the SVM algorithm from the paper and verify whether the stars were sorted correctly. Many stars in the catalog have a pre-existing classification that can be verified, but many are not classified and the scope of my project is to identify, observe and classify them.