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Cosmological parameter inference using neural networks

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With the information from the theoretically well-understood CMB almost exhausted, we need to look for new sources to learn more about the cosmological model of our universe. Whilst these new sources are plentiful, they are technically very difficult to extract information from. Neural networks with large training sets are currently providing tighter constraints on cosmological parameters than ever before. However, in their current form, these neural networks are unable to give true Bayesian inference of cosmological model parameters. I will describe why this is true and present two methods by which the information extracting power of neural networks can be built into the necessary robust statistical framework to perform trustworthy inference, whilst at the same time massively reducing the quantity of training data required.

This talk is part of the Cosmology Lunch series.

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