Preliminary Target Selection for the DESI Quasar (QSO) Sample

TL;DR

This paper presents two methods, traditional color cuts and machine learning, for selecting quasar candidates for the DESI survey to map large-scale structure across different redshift ranges.

Contribution

It introduces and compares two novel approaches for quasar target selection using optical and infrared imaging data for DESI.

Findings

The machine-learning method improves quasar candidate selection efficiency.

Color cut method provides a simple baseline for candidate selection.

Both methods enable better targeting for DESI's large-scale structure measurements.

Abstract

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 Ly-$\alpha$ forests at $z>2.1$. 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.