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Register a new userDetecting Multiple DLAs per Spectrum in SDSS DR12 with Gaussian Processes
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We present a revised version of our automated technique using Gaussian processes (GPs) to detect Damped Lyman-$alpha$ absorbers (DLAs) along quasar (QSO) sightlines. The main improvement is to allow our Gaussian process pipeline to detect multiple DLAs along a single sightline. Our DLA detections are regularised by an improved model for the absorption from the Lyman-$alpha$ forest which improves performance at high redshift. We also introduce a model for unresolved sub-DLAs which reduces mis-classifications of absorbers without detectable damping wings. We compare our results to those of two different large-scale DLA catalogues and provide a catalogue of the processed results of our Gaussian process pipeline using 158 825 Lyman-$alpha$ spectra from SDSS data release 12. We present updated estimates for the statistical properties of DLAs, including the column density distribution function (CDDF), line density ($dN/dX$), and neutral hydrogen density ($Omega_{textrm{DLA}}$).
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We define Baryon Acoustic Oscillation (BAO) distances $hat{d}_alpha(z, z_c)$, $hat{d}_z(z, z_c)$, and $hat{d}_/(z, z_c)$ that do not depend on cosmological parameters. These BAO distances are measured as a function of redshift $z$ with the Sloan Digital Sky Survey (SDSS) data release DR12. From these BAO distances alone, or together with the correlation angle $theta_textrm{MC}$ of the Cosmic Microwave Background (CMB), we constrain the cosmological parameters in several scenarios. We find $4.3 sigma$ tension between the BAO plus $theta_textrm{MC}$ data and a cosmology with flat space and constant dark energy density $Omega_textrm{DE}(a)$. Releasing one and/or the other of these constraints obtains agreement with the data. We measure $Omega_textrm{DE}(a)$ as a function of $a$.
The interstellar medium (ISM) is a complex non-linear system governed by gravity and magneto-hydrodynamics, as well as radiative, thermodynamical, and chemical processes. Our understanding of it mostly progresses through observations and numerical simulations, and a quantitative comparison between these two approaches requires a generic and comprehensive statistical description. The goal of this paper is to build such a description, with the purpose to permit an efficient comparison independent of any specific prior or model. We start from the Wavelet Scattering Transform (WST), a low-variance statistical description of non-Gaussian processes, developed in data science, that encodes long-range interactions through a hierarchical multiscale approach based on the Wavelet transform. We perform a reduction of the WST through a fit of its angular dependencies, allowing to gather most of the information it contains into a few components whose physical meanings are identified, and that describe, e.g., isotropic and anisotropic behaviours. The result of this paper is the Reduced Wavelet Scattering Transform (RWST), a statistical description with a small number of coefficients that characterizes complex structures arising from non-linear phenomena, free from any specific prior. The RWST coefficients encode moments of order up to four, have reduced variances, and quantify the couplings between scales. To show the efficiency and generality of this description, we apply it successfully to three kinds of processes: fractional Brownian motions, MHD simulations, and Herschel observations in a molecular cloud. With fewer than 100 coefficients when probing 6 scales and 8 angles on 256*256 maps, we were able with the RWST to perform quantitative comparisons, to infer relevant physical properties, and to produce realistic synthetic fields.
We present a new determination of the large-scale clustering of the CIV forest (i.e., the absorption due to all CIV absorbers) using its cross-correlation with quasars in the Sloan Digital Sky Survey (SDSS) Data Release 12 (DR12). We fit a linear bias model to the measured cross-correlation. We find that the transmission bias of the CIV forest, $b_{Fc}$, at a mean redshift of $z=2.3$, obeys the relation $(1+beta_c)b_{F c} = -0.024 pm 0.003$. Here, $beta_{c}$ is the linear redshift space distortion parameter of the CIV absorption, which can only be poorly determined at $beta_c=1.1pm 0.6$ from our data. This transmission bias is related to the bias of CIV absorbers and their host halos through the effective mean optical depth of the CIV forest, $bartau_c$. Estimating a value $bar tau_c(z) simeq 0.01$ from previous studies of the CIV equivalent width distribution, our measurement implies a CIV absorber bias near unity, with a large error due to uncertainties in both $beta_c$ and $bartau_c$. This makes it compatible with the higher DLA bias $b_{rm DLA}simeq 2$ measured previously from the cross-correlation of DLAs and the Lyman-$alpha$ forest. We discuss the implications of the CIV absorber bias for the mass distribution of their host halos. More accurate determinations of $bar tau_c(z)$ and $beta_c$ are necessary to obtain a more robust measurement of this CIV absorber bias.
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 al. (2020) with an improved model for marginalising uncertainty in the mean optical depth of each quasar. We compute the column density distribution function (CDDF) at $2 < z < 5$, the line density ($textrm{d} N/ textrm{d} X$), and the neutral hydrogen density ($Omega_{textrm{DLA}}$). Our Gaussian process model provides a posterior probability distribution of the number of DLAs per spectrum, thus allowing unbiased probabilistic predictions of the statistics of DLA populations even with the noisiest data. We measure a non-zero column density distribution function for $N_{textrm{HI}} < 3 times 10^{22} ,textrm{cm}^{-2}$ with $95%$ confidence limits, and $N_{textrm{HI}} lesssim 10^{22} ,textrm{cm}^{-2}$ for spectra with signal-to-noise ratios $> 4$. Our results for DLA line density and total hydrogen density are consistent with previous measurements. Despite a small bias due to the poorly measured blue edges of the spectra, we demonstrate that our new model can measure the DLA population statistics when the DLA is in the Lyman-$beta$ forest region. We verify our results are not sensitive to the signal-to-noise ratios and redshifts of the background quasars although a residual correlation remains for detections from $z_{textrm{QSO}} < 2.5$, indicating some residual systematics when applying our models on very short spectra, where the SDSS spectral observing window only covers part of the Lyman-$alpha$ forest.
We present the characteristics of the Damped Lyman-$alpha$ (DLA) systems found in the data release DR16 of the extended Baryon Oscillation Spectroscopic Survey (eBOSS) of the Sloan Digital Sky Survey (SDSS). DLAs were identified using the convolutional neural network (CNN) of~cite{Parks2018}. A total of 117,458 absorber candidates were found with $2 leq zdla leq 5.5$ and $19.7 leq lognhi leq 22$, including 57,136 DLA candidates with $lognhi geq 20.3$. Mock quasar spectra were used to estimate DLA detection efficiency and the purity of the resulting catalog. Restricting the quasar sample to bright forests, i.e. those with mean forest fluxes $meanflux>2timesfluxunit$, the completeness and purity are greater than 90% for DLAs with column densities in the range $20.1leq lognhi leq 22$.
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