8 citations · 15 across the 6 of their papers we have counts for
6 papers
Cosmology with Persistent Homology: Parameter Inference via Machine Learning
Juan Calles, Jacky H. T. Yip, Gabriella Contardo +3
Building upon [2308.02636], we investigate the constraining power of persistent homology on cosmological parameters and primordial non-Gaussianity in a likelihood-free inference pi…
Cosmology at the Field Level with Probabilistic Machine Learning
Adam Rouhiainen
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 reconst…
Super-Resolution Emulation of Large Cosmological Fields with a 3D Conditional Diffusion Model
Adam Rouhiainen, Michael Gira, Moritz Münchmeyer +2
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…
Learning from Topology: Cosmological Parameter Estimation from the Large-scale Structure
Jacky H. T. Yip, Adam Rouhiainen, Gary Shiu
The topology of the large-scale structure of the universe contains valuable information on the underlying cosmological parameters. While persistent homology can extract this topolo…
De-noising non-Gaussian fields in cosmology with normalizing flows
Adam Rouhiainen, Moritz Münchmeyer
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…
Normalizing flows for random fields in cosmology
Adam Rouhiainen, Utkarsh Giri, Moritz Münchmeyer
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…