Abstract We present a survey undertaken with the Atacama Large Millimeter/submillimeter Array (ALMA) to study the galaxies associated with a representative sample of 16 damped Lyαabsorbers (DLAs) atz ≈ 4.1–4.5, using the [Cii] 158μm ([Cii]) line. We detect seven [Cii]-emitting galaxies in the fields of five DLAs, all of which have absorption metallicity [M/H] > −1.5. We find that the detectability of these Hi-selected galaxies with ALMA is a strong function of DLA metallicity, with a detection rate of % for DLAs with [M/H] > −1.5 and 0+18% for DLAs with [M/H] < −1.5. The identified DLA galaxies have far-IR properties similar to those of typical star-forming galaxies atz ∼ 4, with estimated obscured star formation rates ranging from ≲6M⊙yr−1to 110M⊙yr−1. High-metallicity DLAs therefore provide an efficient way to identify and study samples of high-redshift, star-forming galaxies, without preselecting the galaxies by their emission properties. The agreement between the velocities of the metal absorption lines of the DLA and the [Cii] emission line of the DLA galaxy indicates that the metals within the DLA originated in the galaxy. With observed impact parameters between 14 and 59 kpc, this indicates that star-forming galaxies atz ∼ 4 have a substantial reservoir of dense, cold, neutral gas within their circumgalactic medium that has been enriched with metals from the galaxy.
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slick: Modeling a Universe of Molecular Line Luminosities in Hydrodynamical Simulations
Abstract We presentslick(the Scalable Line Intensity Computation Kit), a software package that calculates realistic CO, [Ci], and [Cii] luminosities for clouds and galaxies formed in hydrodynamic simulations. Built on the radiative transfer codedespotic,slickcomputes the thermal, radiative, and statistical equilibrium in concentric zones of model clouds, based on their physical properties and individual environments. We validate our results by applyingslickto the high-resolution run of theSimbasimulations, testing the derived luminosities against empirical and theoretical/analytic relations. To simulate the line emission from a universe of emitting clouds, we have incorporated random forest machine learning (ML) methods into our approach, allowing us to predict cosmologically evolving properties of CO, [Ci], and [Cii] emission from galaxies such as luminosity functions. We tested this model in 100,000 gas particles, and 2500 galaxies, reaching an average accuracy of ∼99.8% for all lines. Finally, we present the first model light cones created with realistic and ML-predicted CO, [Ci], and [Cii] luminosities in cosmological hydrodynamical simulations, fromz= 0 toz= 10.
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- Award ID(s):
- 1909153
- PAR ID:
- 10548568
- Publisher / Repository:
- DOI PREFIX: 10.3847
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 974
- Issue:
- 2
- ISSN:
- 0004-637X
- Format(s):
- Medium: X Size: Article No. 197
- Size(s):
- Article No. 197
- Sponsoring Org:
- National Science Foundation
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