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We have updated and applied a convolutional neural network (CNN) machine learning model to discover and characterize damped Ly$\alpha$ systems (DLAs) based on Dark Energy Spectroscopic Instrument (DESI) mock spectra. We have optimized the…

We have employed deep neural network, or deep learning to predict the flux and the shape of the broad Ly$\alpha$ emission lines in the spectra of quasars. We use 17870 high signal-to-noise ratio (SNR > 15) quasar spectra from the Sloan…

Astrophysics of Galaxies · Physics 2020-08-05 Hassan Fathivavsari

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 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…

Astrophysics of Galaxies · Physics 2022-09-28 Ting-Yun Cheng , Ryan Cooke , Gwen Rudie

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…

Quasar damped Ly-alpha (DLA) absorption line systems with redshifts z<1.65 are used to trace neutral gas over approximately 70 per cent of the most recent history of the Universe. However, such systems fall in the UV and are rarely found in…

Cosmology and Nongalactic Astrophysics · Physics 2015-06-19 David A. Turnshek , Eric M. Monier , Sandhya M. Rao , Timothy S. Hamilton , Gendith M. Sardane , Ryan Held

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 >…

Astrophysics · Physics 2009-11-13 Sara L. Ellison , Brian A. York , Max Pettini , Nissim Kanekar

A deep neural network (DNN) model consisting of two hidden layers was proposed for predicting the immediate environments of specific atoms based on X-ray absorption near-edge spectra (XANES). The output layer of the DNN can be adjusted to…

Computational Physics · Physics 2019-05-13 Liang Li , Mindren Lu , Maria K. Y. Chan

We apply a convolutional neural network (CNN) to classify and detect quasars in the Sloan Digital Sky Survey Stripe 82 and also to predict the photometric redshifts of quasars. The network takes the variability of objects into account by…

Instrumentation and Methods for Astrophysics · Physics 2018-04-11 Johanna Pasquet-Itam , Jérôme Pasquet

As large optical quasar surveys for damped Lya become a reality and the study of star forming gas in the early Universe achieves statistical robustness, it is now vital to identify and quantify the sources of systematic error. Because the…

Quasar absorption line analysis is critical for studying gas and dust components and their physical and chemical properties as well as the evolution and formation of galaxies in the early universe. Ca II absorbers, which are one of the…

Astrophysics of Galaxies · Physics 2022-10-18 Iona Xia , Jian Ge , Kevin Willis , Yinan Zhao

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…

Astrophysics · Physics 2009-11-11 Vivienne Wild , Paul Hewett , Max Pettini

Using the Sloan Digital Sky Survey, Data Release 5, we survey proximate damped Lya systems (PDLAs): absorption line systems with HI column density N(HI)> 2x10^20 cm^{-2} at velocity separation dv < 3000 km/s from their background quasar.…

Astrophysics · Physics 2009-11-13 Jason X. Prochaska , Joseph F. Hennawi , Stephane Herbert-Fort

We report the discovery of 59 new ghostly absorbers from the Sloan Digital Sky Survey (SDSS) Data Release 14 (DR14). These absorbers, with $z_{\rm abs}$$\sim$$z_{\rm QSO}$, reveal no Ly$\alpha$ absorption, and they are mainly identified…

Astrophysics of Galaxies · Physics 2020-10-07 Hassan Fathivavsari

We present here a dataset of quasars observed with the Ultraviolet Visual Echelle Spectrograph (UVES) on the VLT and available in the ESO UVES Advanced Data Products archive. The sample is made up of a total of 250 high resolution quasar…

Cosmology and Nongalactic Astrophysics · Physics 2013-08-16 Tayyaba Zafar , Attila Popping , Celine Peroux

Quasars experiencing strong lensing offer unique viewpoints on subjects related to the cosmic expansion rate, the dark matter profile within the foreground deflectors, and the quasar host galaxies. Unfortunately, identifying them in…

Gravitationally strongly lensed quasars (SL-QSO) offer invaluable insights into cosmological and astrophysical phenomena. With the data from ongoing and next-generation surveys, thousands of SL-QSO systems can be discovered expectedly,…

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,…

Astrophysics of Galaxies · Physics 2017-01-25 Simeon Bird , Roman Garnett , Shirley Ho

We develop an automated technique for detecting damped Lyman-$\alpha$ absorbers (DLAs) along spectroscopic lines of sight to quasi-stellar objects (QSOs or quasars). The detection of DLAs in large-scale spectroscopic surveys such as…

Cosmology and Nongalactic Astrophysics · Physics 2018-05-17 Roman Garnett , Shirley Ho , Simeon Bird , Jeff Schneider

We use spectroscopy of close pairs of quasars to study diffuse gas in the circumgalactic medium (CGM) surrounding a sample of 40 Damped Lya systems (DLAs). The primary sightline in each quasar pair probes an intervening DLA in the redshift…

Astrophysics of Galaxies · Physics 2015-08-06 Kate H. R. Rubin , Joseph F. Hennawi , J. Xavier Prochaska , Robert A. Simcoe , Adam Myers , Marie Wingyee Lau
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