Engineering topics

Staten, Ryan

Publications and source records attributed to Staten, Ryan.

3 records

DOE OSTI2020

Preliminary Target Selection for the DESI Emission Line Galaxy (ELG) Sample

DESI will precisely constrain cosmic expansion and the growth of structure by collecting ~35 million redshifts across ~80% of cosmic history and one third of the sky to study Baryon Acoustic Oscillations (BAO) and Redshift Space Distortions (RSD). In this work, we present a preliminary target selection for an Emission Line Galaxy (ELG) sample, which will comprise about half of all DESI tracers. The selection consists of a g-band magnitude cut and a (g – r) versus (r – z) color box, which we validate using HSC/PDR2 photometric redshifts and DEEP2 spectroscopy. The ELG target density should be ~2400 deg –2 , with ~65% of ELG redshifts reliably within a redshift range of 0.6 < z < 1.6. ELG targeting for DESI will be finalized during a "Survey Validation" phase.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Preliminary Target Selection for the DESI Luminous Red Galaxy (LRG) Sample

The DESI survey will observe more than 8 million candidate luminous red galaxies (LRGs) in the redshift range 0.3 < z < 1.0. In this work, we present a preliminary version of the DESI LRG target section developed using Legacy Surveys Data Release 8 g, r, z and W1 photometry. This selection yields a sample with a uniform surface density of ~600 deg –2 and very low predicted stellar contamination and redshift failure rates. During DESI Survey Validation, updated versions of this selection will be tested and optimized.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Preliminary Target Selection for the DESI Quasar (QSO) Sample

The DESI survey will measure large-scale structure using quasars as direct tracers of dark matter in the redshift range 0.9 < z < 2.1 and using quasar Lyα forests at z > 2.1. In this work, we present two methods to select candidate quasars for DESI based on imaging in three optical (g, r, z) and two infrared (W1, W2) bands. The first method uses traditional color cuts and the second utilizes a machine-learning algorithm.

79 ASTRONOMY AND ASTROPHYSICS↗