Characterizing the formation history of Milky Way-like stellar haloes
  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 haloes 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 dataset, 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.
