Related papers: Low redshift observational constraints on dark ene…
Upcoming surveys will measure the cosmic microwave background (CMB) weak lensing power spectrum in exquisite detail, allowing for strong constraints on the sum of neutrino masses among other cosmological parameters. Standard CMB lensing…
We present a novel analytical framework employing Physics-Informed Neural Networks (PINNs) to constrain the cosmological constant $\Lambda$ through the analysis of stellar orbits around the supermassive black hole (SMBH) Sgr A* at the…
We develop an optimization algorithm, using simulated annealing for the quantification of patterns in astronomical data based on techniques developed for robotic vision applications. The methodology falls in the category of cost…
Despite the broad successes of the flat $\Lambda$CDM model and its fitness to the various cosmological observations, it confronts challenges stemming from anomalies in the measurements of the Hubble constant ($H_0$) and the amplitude of…
Forthcoming cosmic microwave background experiments (CMB) will provide precise new tests of structure-formation theories. The geometry of the Universe may be determined robustly, and the classical cosmological parameters, such as the…
We present results exploring the role that probabilistic deep learning models can play in cosmology from large-scale astronomical surveys through photometric redshift (photo-z) estimation. Photo-z uncertainty estimates are critical for the…
Cosmic microwave background polarization encodes information not only on the early universe but also dark energy, neutrino mass, and gravity in the late universe through CMB lensing. Ground based surveys such as ACTpol, PolarBear, SPTpol…
Cosmic Microwave Background (CMB) has been a cornerstone in many cosmology experiments and studies since it was discovered back in 1964. Traditional computational models like CAMB that are used for generating CMB temperature anisotropy maps…
With the use of simulated supernova catalogs, we show that the statefinder parameters turn out to be poorly and biased estimated by standard cosmography. To this end, we compute their standard deviations and several bias statistics on…
In this work, we use artificial neural networks (ANNs) to recognize the material composition, sizes of nanoparticles and their concentrations in different media with high accuracy, solely from the absorbance spectrum of a macroscopic…
We present model independent reconstructions of quintessence and the Swampland conjectures (SC) using both Machine Learning (ML) and cosmography. In particular, we demonstrate how the synergies between theoretical analyses and ML can…
Recovering credible cosmological parameter constraints in a weak lensing shear analysis requires an accurate model that can be used to marginalize over nuisance parameters describing potential sources of systematic uncertainty, such as the…
In this paper, we study the cosmological implications of the 100 square degree Weak Lensing survey (the CFHTLS-Wide, RCS, VIRMOS-DESCART and GaBoDS surveys). We combine these weak lensing data with the cosmic microwave background (CMB)…
The standard relation between the cosmological redshift and cosmic scale factor underlies cosmological inference from virtually all kinds of cosmological observations, leading to the emergence of the LambdaCDM cosmological model. This…
Decaying Dark Energy models modify the background evolution of the most common observables, such as the Hubble function, the luminosity distance and the Cosmic Microwave Background temperature-redshift scaling relation. We use the most…
We investigate a cosmological model in which a fraction of the dark matter is atomic dark matter (ADM). This ADM consists of dark versions of the electron and of the proton, interacting with each other and with dark photons just as their…
We aim to determine the most effective approach for estimating uncertainties in quasar photo-$z$ and to evaluate the ability of different models to reconstruct the true redshift distribution under varying data quality. We use photometric…
We explore linear and non-linear dimensionality reduction techniques for statistical inference of parameters in cosmology. Given the importance of compressing the increasingly complex data vectors used in cosmology, we address questions…
Astrophysical processes such as feedback from supernovae and active galactic nuclei modify the properties and spatial distribution of dark matter, gas, and galaxies in a poorly understood way. This uncertainty is one of the main theoretical…
A method based on Generative Adversaria! Networks (GANs) is developed for disentangling the physical (effective temperature and gravity) and chemical (metallicity, overabundance of a-elements with respect to iron) atmospheric properties in…