Validation of Emission-Line Galaxies Target Selection Algorithms for the Dark Energy Spectroscopic Instrument Using the MMT Binospec
Tanveer Karim (1), Jae H. Lee (2, 3), Daniel J. Eisenstein (1),, Etienne Burtin (4), John Moustakas (5), Anand Raichoor (6), Christophe, Y\`eche (4) (for the DESI collaboration, (1) Department of Astronomy, Harvard, University, (2) Department of Physics, Harvard University

TL;DR
This study evaluates and compares three ELG target selection algorithms for DESI using spectroscopic data from MMT Binospec, aiming to optimize galaxy selection for dark energy research.
Contribution
It provides an empirical assessment of selection algorithms' efficiency and suggests improvements for better ELG targeting in DESI.
Findings
NDM performed the best among the algorithms
Simple modifications to FDR can improve performance
Spectroscopic validation confirms selection effectiveness
Abstract
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 D map of the Universe using galaxies up to and QSOs up to . In order to create this map, DESI will obtain spectroscopic redshifts of over 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 vs. 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…
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