Related papers: Deep Learning 21cm Lightcones in 3D

Deep networks for image classification often rely more on texture information than object shape. While efforts have been made to make deep-models shape-aware, it is often difficult to make such models simple, interpretable, or rooted in…

We present a deep learning driven computational approach to overcome the limitations of self-interference digital holography that imposed by inferior axial imaging performances. We demonstrate a 3D deep neural network model can…

Several ongoing and upcoming radio telescopes aim to detect either the global 21 cm signal or the 21 cm power spectrum. The extragalactic radio background, as detected by ARCADE-2 and LWA-1, suggests a strong radio background from cosmic…

We argue that the 21-cm global signal can be a powerful probe of isocurvature perturbations, particularly for the ones with blue-tilted spectra. Although the 21-cm global signal is much affected by astrophysical processes, which give some…

With the great promise of deep learning, discoveries of new particles at the Large Hadron Collider (LHC) may be imminent. Following the discovery of a new Beyond the Standard model particle in an all-hadronic channel, deep learning can also…

Large scale filaments, with lengths that can reach tens of Mpc, are the most prominent features in the cosmic web. These filaments have only been observed indirectly through the positions of galaxies in large galaxy surveys or through…

The EDGES collaboration reported the finding of an unexpectedly deep absorption in the radio background at 78 MHz and interpreted the dip as a first detection of redshifted 21-cm from Cosmic Dawn. We have attempted an alternate analysis,…

The 21-cm hyperfine line of neutral hydrogen is a useful tool to probe the conditions of the Universe during the Dark Ages, Cosmic Dawn, and the Epoch of Reionisation. In most of the current calculations, the 21-cm line signals at given…

Detection and analysis of the cosmic 21 cm signal of neutral hydrogen has long been considered the most promising route towards exploration of the Epoch of Reionization (EoR). 21CMMC, a Markov Chain Monte Carlo sampler of the semi-numerical…

A galaxy's morphological features encode details about its gas content, star formation history, and feedback processes, which play important roles in regulating its growth and evolution. We use deep convolutional neural networks (CNNs) to…

The 21-cm emission line from neutral hydrogen during the cosmic Dark Ages can be a powerful probe of cosmological models and early universe physics. This work provides a quantitative forecast for the design requirements of a lunar far-side…

There has been considerable recent activity applying deep convolutional neural nets (CNNs) to data from particle physics experiments. Current approaches on ATLAS/CMS have largely focussed on a subset of the calorimeter, and for identifying…

A novel method to characterize the topology of the early-universe intergalactic medium during the epoch of cosmic reionization is presented. The 21-cm radiation background from high redshift is analyzed through the calculation of the…

This paper presents the first, 15-PetaFLOP Deep Learning system for solving scientific pattern classification problems on contemporary HPC architectures. We develop supervised convolutional architectures for discriminating signals in…

Networks are ubiquitous structure that describes complex relationships between different entities in the real world. As a critical component of prediction task over nodes in networks, learning the feature representation of nodes has become…

Tomographic intensity mapping of the HI using the redshifted 21 cm observations opens up a new window towards our understanding of cosmological background evolution and structure formation. This is a key science goal of several upcoming…

We explore the impact of dark matter annihilation on the 21-cm signal during the cosmic dawn and epoch of reionization (EoR). Using modified 21cmFAST simulations and convolutional neural networks (CNNs), we investigate how energy injected…

3D object detection from a single image without LiDAR is a challenging task due to the lack of accurate depth information. Conventional 2D convolutions are unsuitable for this task because they fail to capture local object and its scale…

We study the problem of interference source identification, through the lens of recognizing one of 15 different channels that belong to 3 different wireless technologies: Bluetooth, Zigbee, and WiFi. We employ deep learning algorithms…

The Epoch of Reionization (EoR) features a rich interplay between the first luminous sources and the low-density gas of the intergalactic medium (IGM), where photons from these sources ionize the IGM. There are currently few observational…