Abstract: Comparing physical models with observations is one of the main challenges in supernova research. One particularly complex task is the analysis of the evolving spectral sequences. These complex sequences also contain a wealth of information about the object and are thus invaluable to the understanding of these objects. With the profusion of data in the “big data” era, it is essential to have tools that allow automated extraction of physical quantities from the abundance of spectra. We have created a code (TARDIS - Kerzendorf & Sim 2014) that can quickly synthesize supernova spectra with some physical accuracy (using well tested methods). The code is designed to accommodate new physics in a modular form that will allow us to test the systematic uncertainty of several approximations. In addition to the spectral synthesis code, we have created a framework (nicknamed Dalek) that uses machine learning algorithms to find the maximum likelihood of parameters for a given observed spectrum as well as exploring the uncertainties In this talk, I will introduce the code, then will give an overview of some of the preliminary results and will close with an overview of our future research.
