Description |
Accurately estimating masses of galaxy clusters is needed to extract cosmological information from them. It is, therefore, crucial to find combinations of observable properties of clusters which have a low-scatter relationship with their masses. Machine learning (ML) tools provide a quick and efficient way of looking for low-scatter relations in abstract high- dimensional parameter spaces. I will present a new and a more accurate method for estimating cluster masses which combines observables from CMB and X-ray surveys. More generally, I will show how ML tools can be useful for estimating distances and masses of astrophysical objects, making mock catalogs, and extracting cosmological information from non-linear scales.
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