CMU-CS-14-123Computer Science Department School of Computer Science, Carnegie Mellon University
CMU-CS-14-123
Andrea Klein July 2014 M.S. Thesis
Keywords:
Machine Learning, Cosmology, Dark Matter, N-body Simulations
In this thesis I present a scalable approach to distribution-to-distribution
regression on large, multi-dimensional datasets. The basic algorithm is
demonstrated on 1-dimensional toy data, then modified for efficiency and scalability. Key enhancements include parallel computation of non-parametric estimators
and the use of a ball tree to support efficient nearest-neighbor search
in high dimension. I then explore the ability of this technique to compute
the final states of cosmological N-body simulations. An existing method
uses cosmological perturbation theory to rapidly approximate the evolution
of simulations; I attempt to learn the unknown function from the approximate
to the true distributions, thereby exploiting the speed of perturbative
approximation while still approaching the accuracy of a true N-body simulation.
I investigate whether it is possible to train the algorithm on 74 pages
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