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Abstract:
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To understand galactic physics and create realistic simulations of the Milky Way, we require strong constraints on galactic evolution parameters, constraining effects such as the birth-rate of massive stars and the frequency of supernovae. In this talk, I will outline a method to precisely determine these using the chemical element abundances and ages from a large set of stars. Inference is performed via a simple chemical evolution model in a hierarchical Bayesian framework, marginalizing over a large number of parameters describing the stars' individual environments and model errors to account for inaccuracies in our model. Hamiltonian Monte Carlo methods are used to sample the posterior function, which is sped up by use of Neural Networks. I will show the parameter constraints obtained from simulations (which are competitive with those from other methods), and discuss future applications of the method.
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