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| Paper: |
Efficient Machine Learning Methods for Cosmology |
| Volume: |
532, ASTRONOMICAL DATA ANALYSIS SOFTWARE AND SYSTEMS XXX |
| Page: |
105 |
| Authors: |
Howard, E. |
| Abstract: |
Machine learning-based analysis techniques are gaining interest in cosmology due to their computational ability to generate complex models in order to analyze and interpret large scale structure data sets, such as the matter density fields comprised of nonlinear complex features, like halos, filaments, sheets and voids. We present a number of powerful machine learning algorithms (classification, regression, reinforcement learning) and data-analysis tools that can be used to predict the non-perturbative cosmological structure and non-Gaussian features hierarchically formed over all scales in the Universe, justifying the advantage of employing such methods for use in cosmology. This paper focuses on explicitly analyzing the machine learning methods that can be applied to existing cosmological problems. |
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