相关论文: Deep Learning of DESI Mock Spectra to Find Damped …

We have designed, developed, and applied a convolutional neural network (CNN) architecture using multi-task learning to search for and characterize strong HI Lya absorption in quasar spectra. Without any explicit modeling of the quasar…

We present the Damped Ly$\alpha$ Toolkit for automated detection and characterization of Damped Ly$\alpha$ absorbers (DLA) in quasar spectra. Our method uses quasar spectral templates with and without absorption from intervening DLAs to…

We present an automatic method based on machine-learning convolutional neural network (CNN) architecture to detect Lyman alpha emitters (LAE) hidden in the Data Release 1 spectroscopic dataset of the Dark Energy Spectroscopic Instrument…

In the Coded Aperture Snapshot Spectral Imaging (CASSI) system, deep unfolding networks (DUNs) have demonstrated excellent performance in recovering 3D hyperspectral images (HSIs) from 2D measurements. However, some noticeable gaps exist…

We develop a machine learning based algorithm using a convolutional neural network (CNN) to identify low HI column density Ly$\alpha$ absorption systems ($\log{N_{\mathrm{HI}}}/{\rm cm}^{-2}<17$) in the Ly$\alpha$ forest, and predict their…

We present the results from the optical component of a survey for damped Lyman-alpha systems (DLAs) towards radio-loud quasars. Our quasar sample is drawn from the Texas radio survey with the following primary selection criteria: z_em >…

Deep learning (DL) has been shown to outperform traditional, human-defined summary statistics of the Ly{\alpha} forest in constraining key astrophysical and cosmological parameters owing to its ability to tap into the realm of non-Gaussian…

We demonstrate the potential of Deep Learning methods for measurements of cosmological parameters from density fields, focusing on the extraction of non-Gaussian information. We consider weak lensing mass maps as our dataset. We aim for our…

We searched for evidence of reddening of background SDSS QSO spectra due to dust in intervening DLA systems. We utilise the Data Releases 5 and 7 to arrive at sample sizes of 475 (DR5) and 676 (DR7) absorbers, based on two different…

We present a deep learning framework to enhance the identification of Ly$\alpha$ emitters (LAEs) in the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX), an untargeted spectroscopic survey of LAEs at $1.9 < z < 3.5$ without imaging…

We use the average E(B-V) and ZnII column densities of a sample of z~1 CaII (3935, 3970) absorption line systems selected from the Sloan Digital Sky Survey (SDSS DR4) to show that on average, with conservative assumptions regarding…

Machine Learning (ML) algorithms are becoming popular in cosmology for extracting valuable information from cosmological data. In this paper, we evaluate the performance of a Convolutional Neural Network (CNN) trained on matter density…

We present a new catalogue of Damped Lyman-$\alpha$ absorbers from SDSS DR16Q, as well as new estimates of their statistical properties. Our estimates are computed with the Gaussian process models presented in Garnett et al. (2017); Ho et…

We present new estimates for the statistical properties of damped Lyman-$\alpha$ absorbers (DLAs). We compute the column density distribution function at $z>2$, the line density, $\mathrm{d}N/\mathrm{d}X$, and the neutral hydrogen density,…

We present the results of our automatic search for proximate damped Ly$\alpha$ absorption (PDLA) systems in the quasar spectra from the Sloan Digital Sky Survey Data Release 12. We constrain our search to those PDLAs lying within 1500 km…

Semantic labeling (or pixel-level land-cover classification) in ultra-high resolution imagery (< 10cm) requires statistical models able to learn high level concepts from spatial data, with large appearance variations. Convolutional Neural…

We have identified a metal-strong (logN(Zn+) > 13.15 or logN(Si+) > 15.95) DLA (MSDLA) population from an automated quasar (QSO) absorber search in the Sloan Digital Sky Survey Data Release 3 (SDSS-DR3) quasar sample, and find that MSDLAs…

DESI is a groundbreaking international project to observe more than 40 million quasars and galaxies over a 5-year period to create a 3D map of the sky. This map will enable us to probe multiple aspects of cosmology, from dark energy to…

This paper presents a comparison of several Convolutional Neural Network (CNN) models for extracting target signals in highly noisy measurement conditions. Four CNN architectures were investigated. The first comprises six consecutive…

We study the cross correlation of damped Ly$\alpha$ systems (DLAs) and their background quasars, using the most updated DLA catalog and the Planck 2018 CMB lensing convergence field. Our measurement suggests that the DLA bias $b_{\rm DLA}$…