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Deep Extragalactic VIsible Legacy Survey (DEVILS): Motivation, Design and Target Catalogue

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arxiv 1806.05808 v1 pith:PNBRCB4Q submitted 2018-06-15 astro-ph.GA

Deep Extragalactic VIsible Legacy Survey (DEVILS): Motivation, Design and Target Catalogue

classification astro-ph.GA
keywords deepsurveyextragalacticdevilsobservationstargettelescopevisible
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The Deep Extragalactic VIsible Legacy Survey (DEVILS) is a large spectroscopic campaign at the Anglo-Australian Telescope (AAT) aimed at bridging the near and distant Universe by producing the highest completeness survey of galaxies and groups at intermediate redshifts ($0.3<z<1.0$). Our sample consists of $\sim$60,000 galaxies to Y$<$21.2mag, over $\sim$6deg$^{2}$ in three well-studied deep extragalactic fields (Cosmic Origins Survey field, COSMOS, Extended Chandra Deep Field South, ECDFS and the X-ray Multi-Mirror Mission Large-Scale Structure region, XMM-LSS - all Large Synoptic Survey Telescope deep-drill fields). This paper presents the broad experimental design of DEVILS. Our target sample has been selected from deep Visible and Infrared Survey Telescope for Astronomy (VISTA) Y-band imaging (VISTA Deep Extragalactic Observations, VIDEO and UltraVISTA), with photometry measured by ProFound. Photometric star/galaxy separation is done on the basis of NIR colours, and has been validated by visual inspection. To maximise our observing efficiency for faint targets we employ a redshift feedback strategy, which continually updates our target lists, feeding back the results from the previous night's observations. We also present an overview of the initial spectroscopic observations undertaken in late 2017 and early 2018.

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  1. Machine Learning Techniques for Astrophysics and Cosmology: Photometric Redshifts

    astro-ph.IM 2026-05 unverdicted novelty 3.0

    AI techniques for photometric redshift estimation have converged and are now limited by the size, systematics, and selection effects in spectroscopic training samples rather than by methodology.