CLIMBER: Galaxy-Halo Connection Constraints from Next-Generation Surveys

TL;DR

This paper develops mock galaxy catalogs for upcoming spectroscopic surveys to optimize their scientific output, focusing on galaxy-halo connections and survey strategies.

Contribution

It introduces the CLIMBER procedure for assigning photometry in mock catalogs and analyzes survey parameter impacts on measurement uncertainties.

Findings

WAVES balances cosmic variance and shot noise effectively.

Increasing survey area reduces correlation function uncertainty.

Targeting completeness improvements marginally enhance constraints.

Abstract

In the coming decade, a new generation of massively multiplexed spectroscopic surveys, such as PFS, WAVES, and MOONS, will probe galaxies in the distant universe in vastly greater numbers than was previously possible. In this work, we generate mock catalogs for each of these three planned surveys to help quantify and optimize their scientific output. To assign photometry into the UniverseMachine empirical model, we develop the Calibrating Light: Illuminating Mocks By Empirical Relations (CLIMBER) procedure using UltraVISTA photometry. Using the published empirical selection functions for each aforementioned survey, we quantify the mass completeness of each survey. We compare different targeting strategies by varying the area and targeting completeness, and quantify how these survey parameters affect the uncertainty of the two-point correlation function. We demonstrate that the PFS and MOONS measurements will be primarily dominated by cosmic variance, not shot noise, motivating the need for increasingly large survey areas. On the other hand, the WAVES survey, which covers a much larger area, will strike a good balance between cosmic variance and shot noise. For a fixed number of targets, a 5% increased survey area (and $\sim$5% decreased completeness) would decrease the uncertainty of the correlation function at intermediate scales by 0.15%, 1.2%, and 1.1% for our WAVES, PFS, and MOONS samples, respectively. Meanwhile, for a fixed survey area, 5% increased targeting completeness improves the same constraints by 0.7%, 0.25%, and 0.1%. All of the utilities used to construct our mock catalogs and many of the catalogs themselves are publicly available.