In:
The Astrophysical Journal, American Astronomical Society, Vol. 954, No. 2 ( 2023-09-01), p. 149-
Abstract:
We introduce the DESI LOW- Z Secondary Target Survey, which combines the wide-area capabilities of the Dark Energy Spectroscopic Instrument (DESI) with an efficient, low-redshift target selection method. Our selection consists of a set of color and surface brightness cuts, combined with modern machine-learning methods, to target low-redshift dwarf galaxies ( z 〈 0.03) between 19 〈 r 〈 21 with high completeness. We employ a convolutional neural network (CNN) to select high-priority targets. The LOW- Z survey has already obtained over 22,000 redshifts of dwarf galaxies ( M * 〈 10 9 M ⊙ ), comparable to the number of dwarf galaxies discovered in the Sloan Digital Sky Survey DR8 and GAMA. As a spare fiber survey, LOW- Z currently receives fiber allocation for just ∼50% of its targets. However, we estimate that our selection is highly complete: for galaxies at z 〈 0.03 within our magnitude limits, we achieve better than 95% completeness with ∼1% efficiency using catalog-level photometric cuts. We also demonstrate that our CNN selections z 〈 0.03 galaxies from the photometric cuts subsample at least 10 times more efficiently while maintaining high completeness. The full 5 yr DESI program will expand the LOW- Z sample, densely mapping the low-redshift Universe, providing an unprecedented sample of dwarf galaxies, and providing critical information about how to pursue effective and efficient low-redshift surveys.
Type of Medium:
Online Resource
ISSN:
0004-637X
,
1538-4357
DOI:
10.3847/1538-4357/ace902
Language:
Unknown
Publisher:
American Astronomical Society
Publication Date:
2023
detail.hit.zdb_id:
2207648-7
detail.hit.zdb_id:
1473835-1
SSG:
16,12
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