相关论文: Reconstructing Large-scale Temperature Profiles ar…

Aims: We introduce a new deep-learning approach for the reconstruction of 3D dust density and temperature distributions from multi-wavelength dust emission observations on the scale of individual star-forming cloud cores (<0.2pc). Methods:…

The diffusion coefficient of heavy quarks in the deconfined medium is examined in this research using a deep convolutional neural network (CNN) trained with data from relativistic heavy ion collisions involving heavy flavor hadrons. The CNN…

We study chromospheric emission to understand the temperature stratification in the solar chromosphere. We observed the intensity profile of the CaIIH line in a quiet Sun region close to the disk center at the German Vacuum Tower Telescope.…

We describe a novel end-to-end approach using Machine Learning to reconstruct the power spectrum of cosmological density perturbations at high redshift from observed quasar spectra. State-of-the-art cosmological simulations of structure…

Temperature field reconstruction is essential for analyzing satellite heat reliability. As a representative machine learning model, the deep convolutional neural network (DCNN) is a powerful tool for reconstructing the satellite temperature…

Learning powerful feature representations for image retrieval has always been a challenging task in the field of remote sensing. Traditional methods focus on extracting low-level hand-crafted features which are not only time-consuming but…

Convolutional neural networks (CNNs) can potentially provide powerful tools for classifying and identifying patterns in climate and environmental data. However, because of the inherent complexities of such data, which are often…

A self-consistent formalism to jointly study cosmic reionization and thermal history of the IGM is presented. The model implements most of the relevant physics governing these processes, such as the inhomogeneous IGM density distribution,…

The aim of this project is to recover the CMB anisotropies maps in temperature and polarized intensity by means of a deep convolutional neural network (CNN) which, after appropiate training, can remove the foregrounds from Planck and…

Ionization balance in the intergalactic medium (IGM) is central to the interpretation of quasar absorption spectra, linking observed ionic columns to the underlying gas density, temperature, metallicity, and ionizing radiation field.…

We examine the temperature structure of the intergalactic medium IGM) surounding a hard radiation source, such as a Quasi-Stellar Object (QSO), as it responds to the onset of helium reionization by the source. We model the reionization…

We investigate the hyperfine transition of $^{3}\mathrm{He}^{+}$ as a promising probe of the IGM during the final stages of helium reionization. Utilising the most recent helium reionization simulation, we generate three-dimensional maps of…

The cosmic microwave background (CMB), carrying the inhomogeneous information of the very early universe, is of great significance for understanding the origin and evolution of our universe. However, observational CMB maps contain serious…

At redshifts z >~ 2, most of the baryons reside in the smooth intergalactic medium which is responsible for the low column density Lyman-alpha forest. This photoheated gas follows a tight temperature-density relation which introduces a…

Recent years have brought more precise temperature measurements of the low-density intergalactic medium (IGM). These new measurements constrain the processes that heated the IGM, such as the reionization of H I and of He II. We present a…

The 21-cm brightness-temperature field of neutral hydrogen during the Epoch of Reionization and Cosmic Dawn is a rich source of cosmological and astrophysical information, primarily due to its significant non-Gaussian features. However, the…

We use state-of-the-art hydrodynamical simulations combined with a 1D radiative transfer code to assess the extent to which the highly ionized regions observed close to z~6 quasars, which we refer to as near-zones, can constrain the…

Machine learning models have been employed to perform either physics-free data-driven or hybrid dynamical downscaling of climate data. Most of these implementations operate over relatively small downscaling factors because of the challenge…

This study aims to test a potential application of lognormal seminumerical simulations to recover the thermal parameters and Jeans length. This could be suitable for generating large number of synthetic spectra with various input data and…

The incorporation of radiative transfer effects into cosmological hydrodynamical simulations is essential for understanding how the intergalactic medium (IGM) makes the transition from a neutral medium to one that is almost fully ionized.…