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Durham University

Department of Physics

Staff profile

Publication details for Prof Peter Hatton

Mackenzie, Ruari, Fumagalli, Michele, Theuns, Tom, Hatton, David J, Garel, Thibault, Cantalupo, Sebastiano, Christensen, Lise, Fynbo, Johan P U, Kanekar, Nissim, Møller, Palle, O’Meara, John, Prochaska, J Xavier, Rafelski, Marc, Shanks, Tom & Trayford, James (2019). Linking gas and galaxies at high redshift: MUSE surveys the environments of six damped Lyα systems at z ≈ 3. Monthly Notices of the Royal Astronomical Society 487(4): 5070-5096.

Author(s) from Durham


We present results from a survey of galaxies in the elds of six z  3 Damped Lyman 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% of galaxies within 1000 km/s from DLAs and
with impact parameters between 25 and 280 kpc. In particular, we discovered 5 high-con dence
Ly emitters associated with three DLAs, plus up to 9 additional detections across ve of the
six elds. 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 lament. 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 signi cant fraction of DLAs trace
the neutral regions within halos with a characteristic mass of Mh  1011 􀀀 1012 M , in agreement
with the inference made from the large-scale clustering of DLAs. We nally show how larger surveys
targeting  25 absorbers have the potential of constraining the characteristic masses of halos hosting
high-redshift DLAs with sucient accuracy to discriminate between di erent models.