相关论文: Can we learn from matter creation to solve the $H_…
We study a cosmological model based on the holographic principle that allows an interaction between dark energy and dark matter with a Hubble infrared cutoff. We adopt an agnostic point of view with respect to the form of the interaction…
The discrepancy between the value of the Hubble constant $H_0$ in the late, local universe and the one obtained from the Planck collaboration representing an all-sky value for the early universe reached the 5-$\sigma$ level. Approaches to…
We showed how to use trained neural networks to perform Bayesian reasoning in order to solve tasks outside their initial scope. Deep generative models provide prior knowledge, and classification/regression networks impose constraints. The…
Many experiments in the near future will test dark energy through its effects on the linear growth of matter perturbations. In this paper we discuss the constraints that future large-scale redshift surveys can put on three different…
Transfer learning is a machine learning paradigm where knowledge from one problem is utilized to solve a new but related problem. While conceivable that knowledge from one task could be useful for solving a related task, if not executed…
How to generate instances with relevant properties and without bias remains an open problem of critical importance for a fair comparison of heuristics. In the context of scheduling with precedence constraints, the instance consists of a…
This paper presents a systematic literature review focusing on the application of machine learning techniques for deriving observational constraints in cosmology. The goal is to evaluate and synthesize existing research to identify…
Feedforward neural networks with random hidden nodes suffer from a problem with the generation of random weights and biases as these are difficult to set optimally to obtain a good projection space. Typically, random parameters are drawn…
Some functions entering cosmological analysis, such as the dark energy equation of state or systematic uncertainties, are unknown functions of redshift. To include them without assuming a particular form we derive an efficient method for…
The values of the Hubble constant ($\rm{H_0}$) inferred from the cosmic microwave background (CMB) and local measurements via the distance ladder exhibit a $\sim5\sigma$ tension. In this work we propose that the tension might be partially…
The Hubble tension is one of the most exciting problems that Cosmology faces today. A lot of possible solutions for it have already been proposed in the last few years, with a lot of them using a lot of new and exotic physics ideas to deal…
The standard model of modern cosmology might be cracked by the recent persistent hot debate on the Hubble-constant ($H_0$) tension, which manifests itself as the sound-horizon ($r_s$) tension or absolute-magnitude ($M_B$) tension if deeming…
The Hubble constant, $H_0$, tension is the tension among the local probes, Supernovae Ia, and the Cosmic Microwave Background Radiation. It has been almost a decade, and this tension still puzzles the community. Here, we add intermediate…
Unsupervised estimation of latent variable models is a fundamental problem central to numerous applications of machine learning and statistics. This work presents a principled approach for estimating broad classes of such models, including…
We present a proof-of-principle determination of the Hubble parameter $H(z)$ from photometric data, obtaining a determination at an effective redshift of $z=0.75$ ($0.65<z<0.85$) of $H(0.75) =105.0\pm 7.9(stat)\pm 7.3(sys)$ km s$^{-1}$…
Efficiently learning mixture of Gaussians is a fundamental problem in statistics and learning theory. Given samples coming from a random one out of k Gaussian distributions in Rn, the learning problem asks to estimate the means and the…
Local measurements of the Hubble constant ($H_0$) based on Cepheids e Type Ia supernova differ by $\approx 5 \sigma$ from the estimated value of $H_0$ from Planck CMB observations under $\Lambda$CDM assumptions. In order to better…
Given a finite and noisy dataset generated with a closed-form mathematical model, when is it possible to learn the true generating model from the data alone? This is the question we investigate here. We show that this model-learning problem…
Dark matter dominates the matter budget of the universe but its nature is unknown. Deviations from the standard model, where dark matter clusters with the same gravitational strength as baryons, and has the same pressureless equation of…
Structured sparsity has recently emerged in statistics, machine learning and signal processing as a promising paradigm for learning in high-dimensional settings. All existing methods for learning under the assumption of structured sparsity…