SIMBAD references

2014MNRAS.445.4008B - Mon. Not. R. Astron. Soc., 445, 4008-4014 (2014/December-3)

Wide-field adaptive optics performance in cosmological deep fields for multi-object spectroscopy with the European Extremely Large Telescope.

BASDEN A.G., EVANS C.J. and MORRIS T.J.

Abstract (from CDS):

A multi-object spectrograph on the forthcoming European Extremely Large Telescope will be required to operate with good sky coverage. Many of the interesting deep cosmological fields were deliberately chosen to be free of bright foreground stars, and therefore are potentially challenging for adaptive optics (AO) systems. Here, we investigate multi-object AO performance using subfields chosen at random from within the Great Observatories Origins Deep Survey (GOODS)-S field, which is the worst case scenario for five deep fields used extensively in studies of high-redshift galaxies. Our AO system model is based on that of the proposed MOSAIC instrument but our findings are equally applicable to plans for multi-object spectroscopy on any of the planned Extremely Large Telescopes. Potential guide stars within these subfields are identified and used for simulations of AO correction. We achieve ensquared energies within 75 mas of between 25-35 percent depending on the subfield, which is sufficient to probe sub-kpc scales in high-redshift galaxies. We also investigate the effect of detector readout noise on AO system performance, and consider cases where natural guide stars are used for both high-order and tip-tilt-only AO correction. We also consider how performance scales with ensquared energy box size. In summary, the expected AO performance is sufficient for a MOSAIC-like instrument, even within deep fields characterized by a lack of bright foreground stars.

Abstract Copyright: © 2014 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society (2014)

Journal keyword(s): instrumentation: adaptive optics - instrumentation: high angular resolution - methods: numerical

Simbad objects: 5

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