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We introduce the DESI LOW-Z Secondary Target Survey, which combines thewide-area capabilities of the Dark Energy Spectroscopic Instrument (DESI) withan efficient, low-redshift target selection method. Our selection consists of aset of color and surface brightness cuts, combined with modern machine learningmethods, to target low-redshift dwarf galaxies ($z$ < 0.03) between $19 < r <21$ with high completeness. We employ a convolutional neural network (CNN) toselect high-priority targets. The LOW-Z survey has already obtained over 22,000redshifts of dwarf galaxies (M$_* < 10^9$ M$_\odot$), comparable to the numberof 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% completenesswith ~1% efficiency using catalog-level photometric cuts. We also demonstratethat our CNN selections $z<0.03$ galaxies from the photometric cuts subsampleat least ten times more efficiently while maintaining high completeness. Thefull five-year DESI program will expand the LOW-Z sample, densely mapping thelow-redshift Universe, providing an unprecedented sample of dwarf galaxies, andproviding critical information about how to pursue effective and efficientlow-redshift surveys.

Target Selection and Sample Characterization for the DESI LOW-Z Secondary Target Program