The metals-to-dust ratio to very low metallicities using GRB and QSO absorbers; extremely rapid dust formation

Among the key parameters defining the ISM of galaxies is the fraction of the metals that are locked up in dust: the metals-to-dust ratio. This ratio bears not only on the ISM and its evolution, but particularly on the origin of cosmic dust. We combine extinction and abundance data from GRB afterglows, from QSO absorbers, as well as from galaxy-lensed QSOs, to determine the metals-to-dust ratios for lines-of-sight through a wide diversity of galaxies from blue, dwarf starbursts to massive ellipticals, across a vast range in redshift z=0.1-6.3, and nearly three orders of magnitude in column density and metal abundance. We thus determine the metals-to-dust ratio in a unique way, providing direct determinations of in situ gas and dust columns without recourse to assumptions with large uncertainties. We find that the metals-to-dust ratios in these systems are surprisingly close to the value for the local group (10^{21.3} cm-2 A_V mag-1), with a mean value of 10^{21.2} cm-2 A_V mag-1 and a standard deviation of 0.3 dex. There is no evidence of deviation from this mean ratio as a function of metallicity, even down to our lowest metallicity of 0.01 Z/Z_sun. The lack of any obvious dependence of the metals-to-dust ratio on either column density, galaxy type or age, redshift, or metallicity indicates a close correspondence between the formation of the metals and the formation of dust. Any delay between the formation of metals and dust must be shorter than the typical metal-enrichment times of these galaxies. Formation of the bulk of the dust in low mass stars is therefore ruled out by these data at any cosmic epoch. Furthermore, dust destruction must not dominate over formation/growth in virtually any galaxy environment. The correlation between metals and dust is a natural consequence of the formation of the bulk of dust in SNe [Abridged].Comment: 6 pages, 3 figures, 1 tabl

The ESO UVES Advanced Data Products Quasar Sample - I. Dataset and New N_HI Measurements of Damped Absorbers

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 spectra with emission redshifts ranging from 0.191 < z_em <6.311. The total UVES exposure time of this dataset is 1560 hours. Thanks to the high resolution of UVES spectra, it is possible to unambiguously measure the column density of absorbers with damping wings, down to N_HI > 10^{19} cm^{-2}, which constitutes the sub-damped Lya absorber (sub-DLA) threshold. Within the wavelength coverage of our UVES data, we find 150 damped Lya systems (DLAs)/sub-DLAs in the range 1.5 < z_abs < 4.7. Of these 150, 93 are DLAs and 57 are sub-DLAs. An extensive search in the literature indicates that 6 of these DLAs and 13 of these sub-DLAs have their N_HI measured for the first time. Among them, 10 are new identifications as DLAs/sub-DLAs. For each of these systems, we obtain an accurate measurement of the HI column density and the absorber's redshift in the range 1.7 < z_abs < 4.2 by implementing a Voigt profile-fitting algorithm. These absorbers are further confirmed thanks to the detection of associated metal lines and/or lines from members of the Lyman series. In our data, a few quasars' lines-of-sight are rich. An interesting example is towards QSO J0133+0400 (z_em = 4.154) with six DLAs and sub-DLAs reported.Comment: 16 pages, 24 figures, 3 table

Evolution of dust content in galaxies probed by gamma-ray burst afterglows

Because of their brightness, gamma-ray burst (GRB) afterglows are viable targets for investigating the dust content in their host galaxies. Simple intrinsic spectral shapes of GRB afterglows allow us to derive the dust extinction. Recently, the extinction data of GRB afterglows are compiled up to redshift $z=6.3$, in combination with hydrogen column densities and metallicities. This data set enables us to investigate the relation between dust-to-gas ratio and metallicity out to high redshift for a wide metallicity range. By applying our evolution models of dust content in galaxies, we find that the dust-to-gas ratio derived from GRB afterglow extinction data are excessively high such that they can be explained with a fraction of gas-phase metals condensed into dust ($f_\mathrm{in}$) $\sim 1$, while theoretical calculations on dust formation in the wind of asymptotic giant branch stars and in the ejecta of Type II supernovae suggest a much more moderate condensation efficiency ($f_\mathrm{in}\sim 0.1$). Efficient dust growth in dense clouds has difficulty in explaining the excessive dust-to-gas ratio at metallicities $Z/\mathrm{Z}_\odot <\epsilon$, where $\epsilon$ is the star formation efficiency of the dense clouds. However, if $\epsilon$ is as small as 0.01, the dust-to-gas ratio at $Z\sim 10^{-2}$ Z$_\odot$ can be explained with $n_\mathrm{H}\gtrsim 10^6$ cm$^{-3}$. Therefore, a dense environment hosting dust growth is required to explain the large fraction of metals condensed into dust, but such clouds should have low star formation efficiencies to avoid rapid metal enrichment by stars.Comment: 7 pages, 3 figures, published in MNRA

On the (in)variance of the dust-to-metals ratio in galaxies

Recent works have demonstrated a surprisingly small variation of the dust-to-metals ratio in different environments and a correlation between dust extinction and the density of stars. Naively, one would interpret these findings as strong evidence of cosmic dust being produced mainly by stars. But other observational evidence suggest there is a significant variation of the dust-to-metals ratio with metallicity. As we demonstrate in this paper, a simple star-dust scenario is problematic also in the sense that it requires that destruction of dust in the interstellar medium (e.g., due to passage of supernova shocks) must be highly inefficient. We suggest a model where stellar dust production is indeed efficient, but where interstellar dust growth is equally important and acts as a replenishment mechanism which can counteract the effects of dust destruction. This model appears to resolve the seemingly contradictive observations, given that the ratio of the effective (stellar) dust and metal yields is not universal and thus may change from one environment to another, depending on metallicity.Comment: 10 pages, 4 figures. Accepted for publication in MNRA

The ESO UVES Advanced Data Products Quasar Sample - II. Cosmological Evolution of the Neutral Gas Mass Density

Quasar foreground damped absorbers, associated with HI-rich galaxies allow to estimate the neutral gas mass over cosmic time, which is a possible indicator of gas consumption as star formation proceeds. The DLAs and sub-DLAs are believed to contain a large fraction of neutral gas mass in the Universe. In Paper I of the series, we present the results of a search for DLAs and sub-DLAs in the ESO-UVES Advanced Data Products dataset of 250 quasars. Here we use an unbiased sub-sample of sub-DLAs from this dataset. We build a subset of 122 quasars ranging from 1.5 <z_em < 5.0, suitable for statistical analysis. The statistical sample is analyzed in conjunction with other sub-DLA samples from the literature. This makes up a combined sample of 89 sub-DLAs over a redshift path of $\Delta z=193$. Redshift evolution of the number density and the line density are derived for sub-DLAs and compared with the LLSs and DLAs measurements from the literature. The results indicate that these three classes of absorbers are evolving in the redshift interval 1 < z < 5. The column density distribution, f(N,z), down to the sub-DLA limit is determined. The flattening of f_(N,z) in the sub-DLA regime is present in the observations. The redshift evolution of f_(N,z) down to sub-DLA regime is also presented, indicating the presence of more sub-DLAs at high-redshift as compared to low-redshift. f_(N,z) is further used to determine the neutral gas mass density, Omega_g, at 1.5 < z < 5.0. The complete sample shows that sub-DLAs contribute 8-20% to the total Omega_g from 1.5 < z < 5.0. In agreement with previous studies, no evolution of Omega_g is seen from low-redshift to high-redshift, suggesting that star formation solely cannot explain this non-evolution and replenishment of gas and/or recombination of ionized gas is needed. (Abridged)Comment: 20 pages, 10 figures, 7 table

Demystifying ANN with Mathematical and Graphical Insights: An Algorithmic Review for Beginners

Developments in deep learning with ANNs (Artificial Neural Networks) are paving the way for revolutionizing all application areas, especially related to non-linear regression and classification problems of predictive modelling and forecasting. Although their explainability is more complicated and challenging, deep neural networks are preferred over conventional machine learning methods for high accuracy in non-linear and complex problems. However, machine learning and data science practitioners often use ANN like a black-box. The present article concisely overviews the mathematics and computations involved in simple feed-forward neural networks (FNNs) or multilayer perceptrons (MLPs). The purpose is to spot light on what deep neural networks’ learning (or training) is and how it works. The article includes simplified derivations of the expressions for the main workhorse of neural networks (the backpropagation) and an example to explain how it works with graphical insights. An algorithm for a basic ANN application is presented in both component-form and matrix-form, together with a detailed note on the relevant data structures, to elaborate the scheme comprehensively. Python implementation of the basic algorithm is presented, and its performance results are compared with those produced using the TensorFlow library functions that implement the neural networks. The article discusses various techniques to improve the generalization capability of neural networks and how to address various training challenges. Finally, some well-established optimization approaches based on the Gradient Descent method are also discussed. The article may serve as a comprehensive premiere for a sound understanding of deep learning for undergraduate and graduate students before indulging in the relevant industry practices so that they can step into sustainable progress in the field

On the mass-metallicity relation, velocity dispersion and gravitational well depth of GRB host galaxies

We analyze a sample of 16 absorption systems intrinsic to long duration GRB host galaxies at $z \gtrsim 2$ for which the metallicities are known. We compare the relation between the metallicity and cold gas velocity width for this sample to that of the QSO-DLAs, and find complete agreement. We then compare the redshift evolution of the mass-metallicity relation of our sample to that of QSO-DLAs and find that also GRB hosts favour a late onset of this evolution, around a redshift of $\approx 2.6$. We compute predicted stellar masses for the GRB host galaxies using the prescription determined from QSO-DLA samples and compare the measured stellar masses for the four hosts where stellar masses have been determined from SED fits. We find excellent agreement and conclude that, on basis of all available data and tests, long duration GRB-DLA hosts and intervening QSO-DLAs are consistent with being drawn from the same underlying population. GRB host galaxies and QSO-DLAs are found to have different impact parameter distributions and we briefly discuss how this may affect statistical samples. The impact parameter distribution has two effects. First any metallicity gradient will shift the measured metallicity away from the metallicity in the centre of the galaxy, second the path of the sightline through different parts of the potential well of the dark matter halo will cause different velocity fields to be sampled. We report evidence suggesting that this second effect may have been detected.Comment: 11 pages, 6 figures, 6 tables. Accepted for publication in MNRAS Main Journal. For the definitive version visit http://mnras.oxfordjournals.org