相关论文: Constraining primordial non-Gaussianity using Neur…

We propose a new method for extracting the non-Gaussian signatures on the isotemperature statistics in the cosmic microwave background (CMB) sky, which is induced by the gravitational lensing due to the intervening large-scale structure of…

The new generation of galaxy surveys will provide unprecedented data allowing us to test gravity at cosmological scales. A robust cosmological analysis of the large-scale structure demands exploiting the nonlinear information encoded in the…

Constraining primordial non-Gaussianities (PNGs) in the large-scale cosmic structure (LSS) is an important step in understanding properties of the early universe, specifically in distinguishing between different inflationary models.…

One of the fundamental problems in extracting the cosmic microwave background signal (CMB) from millimeter/submillimeter observations is the pollution by emission from the Milky Way: synchrotron, free-free, and thermal dust emission. To…

With the release of the data from the Boomerang and MAXIMA-1 balloon flights, estimates of cosmological parameters based on the Cosmic Microwave Background (CMB) have reached unprecedented precision. In this paper I show that it is possible…

We present a further development of a method for accelerating the calculation of CMB power spectra, matter power spectra and likelihood functions for use in cosmological Bayesian inference. The algorithm, called {\sc CosmoNet}, is based on…

Big bang nucleosynthesis (BBN) and the cosmic microwave background (CMB) anisotropies independently predict the universal baryon density. Comparing their predictions will provide a fundamental test on cosmology. Using BBN and the CMB…

We propose an efficient Bayesian MCMC algorithm for estimating cosmological parameters from CMB data without use of likelihood approximations. It builds on a previously developed Gibbs sampling framework that allows for exploration of the…

We propose a lightweight deep convolutional neural network (lCNN) to estimate cosmological parameters from simulated three-dimensional dark matter (DM) halo distributions and associated statistics. The training dataset comprises 2000…

We describe a novel method for the application of Convolutional Neural Networks (CNNs) to fields defined on the sphere, using the HEALPix tessellation scheme. Specifically, We have developed a pixel-based approach to implement convolutional…

The statistical properties of a map of the primary fluctuations in the cosmic microwave background (CMB) may be specified to high accuracy by a few thousand power spectra measurements, provided the fluctuations are gaussian, yet the number…

Two different predictions for the primordial curvature fluctuation bispectrum are compared through their effects on the Cosmic Microwave Background temperature fluctuations. The first has a local form described by a single parameter f_{NL}.…

We explore the possibility of detecting primordial non-Gaussianity of the local type using weak lensing peak counts. We measure the peak abundance in sets of simulated weak lensing maps corresponding to three models f_NL={0, +100, -100}.…

The statistics of large-scale structure in the Universe can be used to probe non-Gaussianity of the primordial density field, complementary to existing constraints from the cosmic microwave background. In particular, the scale dependence of…

The discovery of cosmic microwave background (CMB) was a paradigm shift in the study and fundamental understanding of the early universe and also the Big Bang phenomenon. Cosmic microwave background is one of the richest and intriguing…

We present limits to the amplitude of non-Gaussian primordial fluctuations in the WMAP 1-year cosmic microwave background sky maps. A non-linear coupling parameter, f_NL, characterizes the amplitude of a quadratic term in the primordial…

We consider primordial non-Gaussianity due to quadratic corrections in the gravitational potential parametrized by a non-linear coupling parameter fnl. We study constraints on fnl from measurements of the galaxy bispectrum in redshift…

We describe a Bayesian framework for estimating the time-domain noise covariance of CMB observations, typically parametrized in terms of a 1/f frequency profile. This framework is based on the Gibbs sampling algorithm, which allows for…

We present a method to reconstruct the initial linear-regime matter density field from the late-time non-linearly evolved density field in which we channel the output of standard first-order reconstruction to a convolutional neural network…

A detection or nondetection of primordial non-Gaussianity in the CMB data is essential not only to test alternative models of the physics of the early universe but also to discriminate among classes of inflationary models. Given this far…