Kavli Affiliate: Risa H. Wechsler
| First 5 Authors: Elise Darragh-Ford, John F. Wu, Yao-Yuan Mao, Risa H. Wechsler, Marla Geha
| Summary:
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$_odot$), comparable to the number
of dwarf galaxies discovered in SDSS-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 ten times more efficiently while maintaining high completeness. The
full five-year 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.
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