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We use the semi-analytic model ChemTreeN, coupled to cosmological N-bodysimulations, to explore how different galaxy formation histories can affectobservational properties of Milky Way-like galaxies' stellar haloes and theirsatellite populations. Gaussian processes are used to generate model emulatorsthat allow one to statistically estimate a desired set of model outputs at anylocation of a p-dimensional input parameter space. This enables one to explorethe full input parameter space orders of magnitude faster than could be doneotherwise. Using mock observational data sets generated by ChemTreeN itself, weshow that it is possible to successfully recover the input parameter vectorsused to generate the mock observables if the merger history of the host halo isknown. However, our results indicate that for a given observational data setthe determination of "best fit" parameters is highly susceptible to theparticular merger history of the host. Very different halo merger histories canreproduce the same observational dataset, if the "best fit" parameters areallowed to vary from history to history. Thus, attempts to characterize theformation history of the Milky Way using these kind of techniques must beperformed statistically, analyzing large samples of high resolution N-bodysimulations.

Characterizing the formation history of Milky Way-like stellar haloes with model emulators