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Oxford University Press (OUP), Monthly Notices of the Royal Astronomical Society, 4(487), p. 5070-5096

DOI: 10.1093/mnras/stz1501

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Linking gas and galaxies at high redshift: MUSE surveys the environments of six damped Lyα systems at z ≈ 3

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Data provided by SHERPA/RoMEO

Abstract

ABSTRACT We present results from a survey of galaxies in the fields of six z ≥ 3 damped Lyman α (Lyα) systems (DLAs) using the Multi Unit Spectroscopic Explorer (MUSE) at the Very Large Telescope (VLT). We report a high detection rate of up to ${≈ } 80{{\ \rm per\ cent}}$ of galaxies within 1000 km s−1 from DLAs and with impact parameters between 25 and 280 kpc. In particular, we discovered five high-confidence Lyα emitters associated with three DLAs, plus up to nine additional detections across five of the six fields. The majority of the detections are at relatively large impact parameters (>50 kpc) with two detections being plausible host galaxies. Among our detections, we report four galaxies associated with the most metal-poor DLA in our sample (Z/Z⊙ = −2.33 ± 0.22), which trace an overdense structure resembling a filament. By comparing our detections with predictions from the Evolution and Assembly of GaLaxies and their Environments (EAGLE) cosmological simulations and a semi-analytic model designed to reproduce the observed bias of DLAs at z > 2, we conclude that our observations are consistent with a scenario in which a significant fraction of DLAs trace the neutral regions within haloes with a characteristic mass of $M_{\rm h} ≈ 10^{11}-10^{12}~\rm M_⊙$, in agreement with the inference made from the large-scale clustering of DLAs. We finally show how larger surveys targeting ≈25 absorbers have the potential of constraining the characteristic masses of haloes hosting high-redshift DLAs with sufficient accuracy to discriminate between different models.

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