Constraining cosmological parameters using density split lensing and the conditional stellar mass function

TL;DR

This paper introduces a simulation-based model for predicting excess surface mass density around galaxies, tailored for DESI data, and forecasts cosmological parameters using combined survey data.

Contribution

The work develops a novel emulator-based model linking local density environments to lensing signals, specifically tailored for DESI galaxy samples, enabling improved cosmological constraints.

Findings

Model accurately predicts ESD for DESI galaxies.

Emulator trained on AbacusSummit simulations achieves fast predictions.

Forecasts demonstrate potential for tight cosmological parameter constraints.

Abstract

In this work, we develop a simulation-based model to predict the excess surface mass density (ESD) depending on the local density environment. Using a conditional stellar mass function, our foreground galaxies are tailored toward the bright galaxy sample of the early data release of the Dark Energy Spectroscopic Instrument (DESI). Due to the nature of the ESD measurement, our derived model is directly applicable to all DESI data. To build this model, we use the $\texttt{AbacusSummit}$ N-body simulation suite from which we measure all necessary statistics and train an emulator based on $\texttt{CosmoPower}$. Finally, we present a cosmological parameter forecast for a possible combined analysis of DESI and the Ultraviolet Near Infrared Optical Northern Survey.