My research to this point has consisted of two project themes: modeling interstellar chemistry with rate kinetics models and observations of young stellar objects and old stars with an infrared spectrograph. Both are data-intensive, though in different styles. The chemical models output abundances for ~1000 chemical species at ~500 timesteps covering ~107 years. This generates a fairly large dataset, with which I plot the growth and destruction of various species in an attempt to either match observations, or make predictions for future observations, of the abundance of various gas-phase and solid ice chemical species. The second theme of my research is motivated by my advisor leaving Cornell; I switched research topics to observational astronomy to broaden my areas of expertise. I have spent this year characterizing spectra from the Infrared Spectrograph aboard the Spitzer Space Telescope. My introductory project was to identify molecular absorption features in a spectrum of a very old star in the Small Magellanic Cloud, a nearby galaxy. I am now looking at a sample of 275 spectra of young stellar objects in the Large Magellanic Cloud. The spectra exhibit emission bands from solid-state carbonaceous species, absorption from silicates and ices, and emission lines from ionized atoms. The goal is to learn more about star formation and star-forming environments through the features seen in the spectra.
My motivation for taking this course is primarily to gain skills that are applicable to jobs outside academia, but I am interested in any possible application Bayesian data analysis has towards my current research. I have nearly no statistical background, but it could be interesting to try a framework other than the established data reduction method for the extraction of spectral features from my YSO sample. I'm not exactly sure how it would be done, or what would be gained, but determination of silicate absorption strength is notoriously difficult due to its overlap with hydrocarbon emission features. If there was a method of statistical analysis which could help separate the two, that would be extremely useful.
On the topic of astrochemical modeling, I know of one paper (Makrymallis & Viti 2014) that used Bayesian analysis to determine interstellar environment parameters (density, cosmic ray ionization rate, ultraviolet radiation field strength, etc.). It appears to be a new method to combine astronomical observations with chemical models, and I hope to be able to understand the paper's methodology by the end of the course. Shown below is a results figure from the paper:
