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Cosmology from Galaxy Redshift Surveys with PointNet
Sotiris Anagnostidis, Arne Thomsen, Tomasz Kacprzak +4
In recent years, deep learning approaches have achieved state-of-the-art results in the analysis of point cloud data. In cosmology, galaxy redshift surveys resemble such a permutat…
A Full CDM Analysis of KiDS-1000 Weak Lensing Maps using Deep Learning
Janis Fluri, Tomasz Kacprzak, Aurelien Lucchi +3
We present a full forward-modeled CDM analysis of the KiDS-1000 weak lensing maps using graph-convolutional neural networks (GCNN). Utilizing the , a novel m…
Cosmological Parameter Estimation and Inference using Deep Summaries
Janis Fluri, Aurelien Lucchi, Tomasz Kacprzak +2
The ability to obtain reliable point estimates of model parameters is of crucial importance in many fields of physics. This is often a difficult task given that the observed data c…
Cosmological constraints with deep learning from KiDS-450 weak lensing maps
Janis Fluri, Tomasz Kacprzak, Aurelien Lucchi +4
Convolutional Neural Networks (CNN) have recently been demonstrated on synthetic data to improve upon the precision of cosmological inference. In particular they have the potential…
Cosmological constraints from noisy convergence maps through deep learning
Janis Fluri, Tomasz Kacprzak, Aurelien Lucchi +3
Deep learning is a powerful analysis technique that has recently been proposed as a method to constrain cosmological parameters from weak lensing mass maps. Due to its ability to l…
Fast cosmic web simulations with generative adversarial networks
Andres C. Rodriguez, Tomasz Kacprzak, Aurelien Lucchi +5
Dark matter in the universe evolves through gravity to form a complex network of halos, filaments, sheets and voids, that is known as the cosmic web. Computational models of the un…