Validation of Emission-Line Galaxies Target Selection Algorithms for the
  Dark Energy Spectroscopic Instrument Using the MMT Binospec
  The forthcoming Dark Energy Spectroscopic Instrument (DESI) experiment plans
to measure the effects of dark energy on the expansion of the Universe and
create a $3$D map of the Universe using galaxies up to $z \sim 1.6$ and QSOs up
to $z \sim 3.5$. In order to create this map, DESI will obtain spectroscopic
redshifts of over $30$ million objects; among them, a majority are \oii
emitting star-forming galaxies known as emission-line galaxies (ELGs). These
ELG targets will be pre-selected by drawing a selection region on the $g - r$
vs. $r - z$ colour-colour plot, where high redshift ELGs form a separate locus
from the lower redshift ELGs and interlopers. In this paper, we study the
efficiency of three ELG target selection algorithms -- the final design report
(FDR) cut based on the DEEP2 photometry, Number Density Modelling and Random
Forest -- to determine how the combination of these three algorithms can be
best used to yield a simple selection boundary that will be best suited to meet
DESI's science goals. To do this, we selected $17$ small patches in the DESI
footprint where we run the three target selection algorithms to pre-select ELGs
based on their photometry. We observed the pre-selected ELGs using the MMT
Binospec, which is similar in functionality to the DESI instrument, to obtain
their spectroscopic redshifts and fluxes of $1054$ ELGs. By analysing the
redshift and fluxing distribution of these galaxies, we find that although NDM
performed the best, simple changes in the FDR definition would also yield
sufficient performance.
