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Inferring halo masses with graph neural networks
(Institute of Physics, 2022)
Understanding the halo–galaxy connection is fundamental in order to improve our knowledge on the nature and
properties of dark matter. In this work, we build a model that infers the mass of a halo given the positions,
...
Hiflow: Generating diverse hi maps and inferring cosmology while marginalizing over astrophysics using normalizing flows
(IOP Publishing, 2022)
A wealth of cosmological and astrophysical information is expected from many ongoing and upcoming large-scale
surveys. It is crucial to prepare for these surveys now and develop tools that can efficiently extract ...
Quijote-png: Simulations of primordial non-gaussianity and the information content of the matter field power spectrum and bispectrum
(IOP Publishing, 2023)
Primordial non-Gaussianity (PNG) is one of the most powerful probes of the early universe, and measurements of
the large-scale structure of the universe have the potential to transform our understanding of this area. ...
Quijote-png: The information content of the halo power spectrum and bispectrum
(IOP Publishing, 2023)
We investigate how much can be learnt about four types of primordial non-Gaussianity (PNG) from small-scale
measurements of the halo field. Using the QUIJOTE-PNG simulations, we quantify the information content
accessible ...