Presenter(s): Jordyn Mascarenas-Wells
Faculty Mentor(s): Aaron Rizutto
Poster 36
Session: Sciences
Studying populations of young stars is essential to humanity’s understanding of planet formation and thereby the existence and development of life in our universe. Such pre-main-sequence stars have previously been very difficult to identify due to the lack of precision on distance measurements available with current technology. The Gaia Space Telescope has revolutionized our ability to assign position and distance values to targets, offering sub-milliarcsecond parallax uncertainties. This allows for unprecedented exactness in absolute magnitude calculations and accurate placement of objects onto Hertzsprung-Russell Diagrams. Based on plot location and isochrone modeling, age and mass predictions for each object can be determined. This research aimed to use Bayesian Statistics to assign probability values to these predictions. Targets were confined to the Taurus-Auriga Star Forming Region, the nearest and most well-studied region of low mass star formation, to ensure the model worked as expected. A synthetic population was constructed, incorporating an initial mass function, binary probability and model photometry from PARSEC isochrones. The theoretical data set was then compared to each Taurus star, producing subsequent age and mass probability distributions. The model produced results as expected up until the range of one solar magnitude. Moving forward, a third dimension of metallicity will be built into the model. The mass range for the synthetic population will be increased and infrared photometry will be included. The model will be applied to other important data sets and a blind search for new groups of young stars in the full Gaia dataset will be performed.