Predicting the T-Web using the beta-skeleton
May 2020 »
Abstract:
Machine learning has found its way into observational cosmology by tackling on of the most relevant problems in the field: inferring the large scale distribution of Dark Matter (DM) in the local Universe. The DM spatial distribution is not directly observable and must be inferred from the observational data. In this project we will show how machine learning methods can help us to solve this task. A first approximation consist in matching the beta-skeleton of the galaxies distribution with the cosmic web of the DM density field.
We will present preliminary results for our reconstruction efforts based on Illustris-TNG (TNG) simulation and comment on its implications and strategies for improvement.
Authors:
- John Suárez
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