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pro vyhledávání: '"Rouhiainen, Adam"'
Autor:
Calles, Juan, Yip, Jacky H. T., Contardo, Gabriella, Noreña, Jorge, Rouhiainen, Adam, Shiu, Gary
Building upon [2308.02636], this article investigates the potential constraining power of persistent homology for cosmological parameters and primordial non-Gaussianity amplitudes in a likelihood-free inference pipeline. We evaluate the ability of pe
Externí odkaz:
http://arxiv.org/abs/2412.15405
Autor:
Rouhiainen, Adam
The large-scale structure in cosmology is highly non-Gaussian at late times and small length scales, making it difficult to describe analytically. Parameter inference, data reconstruction, and data generation tasks in cosmology are greatly aided by v
Externí odkaz:
http://arxiv.org/abs/2402.07694
High-resolution (HR) simulations in cosmology, in particular when including baryons, can take millions of CPU hours. On the other hand, low-resolution (LR) dark matter simulations of the same cosmological volume use minimal computing resources. We de
Externí odkaz:
http://arxiv.org/abs/2311.05217
The topology of the large-scale structure of the universe contains valuable information on the underlying cosmological parameters. While persistent homology can extract this topological information, the optimal method for parameter estimation from th
Externí odkaz:
http://arxiv.org/abs/2308.02636
Autor:
Rouhiainen, Adam, Münchmeyer, Moritz
Fields in cosmology, such as the matter distribution, are observed by experiments up to experimental noise. The first step in cosmological data analysis is usually to de-noise the observed field using an analytic or simulation driven prior. On large
Externí odkaz:
http://arxiv.org/abs/2211.15161
Normalizing flows are a powerful tool to create flexible probability distributions with a wide range of potential applications in cosmology. Here we are studying normalizing flows which represent cosmological observables at field level, rather than a
Externí odkaz:
http://arxiv.org/abs/2105.12024
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