Related papers: Fast cosmological parameter estimation using neura…
$ $Future surveys could obtain tighter constraints on the cosmological parameters with the galaxy power spectrum than with the Cosmic Microwave Background. However, the inclusion of multiple overlapping tracers, redshift bins, and more…
The possibility of determining cosmological parameters on the basis of a wide set of observational data including the Abell-ACO cluster power spectrum and mass function, peculiar velocities of galaxies, the distribution of Ly-$\alpha$…
We present a joint cosmological analysis combining data from the Planck satellite, the Atacama Cosmology Telescope, and the South Pole Telescope. We construct a unified likelihood that reproduces the measured temperature and polarisation…
We present a method for fast optimal estimation of the temperature angular power spectrum from observations of the cosmic microwave background. We employ a Hamiltonian Monte Carlo (HMC) sampler to obtain samples from the posterior…
We present a comprehensive neural architecture, the PUREPath, which leverages a nested Probabilistic multi-modal U- Net framework, augmented by the inclusion of probabilistic ResNet blocks in the Expanding Pathway of the decoders, to…
We present a novel application of partial convolutional neural networks (PCNN) that can inpaint masked images of the cosmic microwave background. The network can reconstruct both the maps and the power spectra to a few percent for circular…
We present the first $\Lambda$CDM cosmological analysis performed on a galaxy survey using marked power spectra. The marked power spectrum is the two-point function of a marked field, where galaxies are weighted by a function that depends…
Determination of cosmological parameters is a major goal in cosmology at present. The availability of improved data sets necessitates the development of novel statistical tools to interpret the inference from a cosmological model. In this…
We propose a technique for determination of the spectral parameters of the cosmological signal and pixel noise using observational data on CMB polarization without any additional assumptions. We introduce the notion of so called crossing…
We present cosmological constraints for the flat $\Lambda$CDM model, including the sum of neutrino masses, by performing a multi-probe analysis of a total of 13 tomographic auto- and cross-angular power spectra. This is achieved by…
We present Xspect, a method to obtain estimates of the angular power spectrum of the Cosmic Microwave Background (CMB) temperature anisotropies including analytical error bars developed for the Archeops experiment. Cross-power spectra are…
Disparity estimation is a difficult problem in stereo vision because the correspondence technique fails in images with textureless and repetitive regions. Recent body of work using deep convolutional neural networks (CNN) overcomes this…
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 learn relevant features from the data, it is able to extract…
Convolutional Neural Networks (CNNs) have recently been applied to cosmological fields -- weak lensing mass maps and galaxy maps. However, cosmological maps differ in several ways from the vast majority of images that CNNs have been tested…
Using a nonparametric function estimation methodology, we present a comparative analysis of the WMAP 1-, 3-, 5-, and 7-year data releases for the CMB angular power spectrum with respect to the following key questions: (a) How well is the…
The power spectrum of density fluctuations is a foundational source of cosmological information. Precision cosmological probes targeted primarily at investigations of dark energy require accurate theoretical determinations of the power…
The recent WMAP data represents a milestone in cosmology and helps constrain cosmological parameters with unprecedented accuracy. In this work we combine the WMAP data with previous CMB anisotropy measurements at smaller angular scales to…
Convolutional Neural Networks (CNNs) achieve remarkable accuracy in vision tasks, yet their computational complexity challenges low-power edge deployment. In this work, we present COMET, a framework of CNN models that employ efficient…
The possibility to constrain cosmological parameters from galaxy surveys using field-level machine learning methods that bypass traditional summary statistics analyses, depends crucially on our ability to generate simulated training sets.…
One of the main obstacles for extracting the Cosmic Microwave Background (CMB) from mm/submm observations is the pollution from the main Galactic components: synchrotron, free-free and thermal dust emission. The feasibility of using simple…