CHARACTERIZING THE FORMATION HISTORY OF MILKY WAY LIKE STELLAR HALOS WITH MODEL EMULATORS

We use the semi-analytic model ChemTreeN, coupled to cosmological N-body simulations, to explore how different galaxy formation histories can affect observational properties of Milky Way like galaxies' stellar halos and their satellite populations. Gaussian processes are used to generate model emulators that allow one to statistically estimate a desired set of model outputs at any location of a p-dimensional input parameter space. This enables one to explore the full input parameter space orders of magnitude faster than could be done otherwise. Using mock observational data sets generated by ChemTreeN itself, we show that it is possible to successfully recover the input parameter vectors used to generate the mock observables if the merger history of the host halo is known. However, our results indicate that for a given observational data set, the determination of best-fit parameters is highly susceptible to the particular merger history of the host. Very different halo merger histories can reproduce the same observational data set, if the best-fit parameters are allowed to vary from history to history. Thus, attempts to characterize the formation history of the Milky Way using these kind of techniques must be performed statistically, analyzing large samples of high-resolution N-body simulations.