# Estimating the confusion noise for the Millimetron space telescope

## TL;DR

This paper develops a model to estimate the confusion noise for the Millimetron space telescope by simulating the Cosmic Infrared Background and comparing it with observational data and other models.

## Contribution

The paper introduces a new CIB model based on the eGALICS simulation and spectral energy distributions, improving confusion noise estimation for future space telescopes.

## Key findings

- The model reasonably reproduces observational data.
- Significant differences exist among models at high redshifts.
- Resolution effects impact number counts and noise estimates.

## Abstract

Sensitivity of future far infrared 10m class space telescopes will be limited by a confusion noise created by distant galaxies. Our primary goal is to create a model that will allow us to estimate the confusion noise parameters of the Millimetron mission. We construct a model of the Cosmic Infrared Background (CIB) aimed at exploring the methods of prediction and reduction of the confusion noise. The model is based on the publicly available eGALICS simulation. For each simulated galaxy we construct a spectral energy distribution with the help of the GRASIL code. To put our model in the context of the current CIB investigations, we compare the outputs of the model with the available observational data and with three other models. One is a well known "backwards evolution" model of Bethermin et al. 2011 and two others are based on a simple mass-luminosity (M-L) relation applied to simulated dark matter halo catalogs. We conclude that our model reproduces the observational data reasonably well. All four models show significant differences in the predictions of the distribution of sources on the flux-redshift plane, especially at high redshifts. We give estimations of the confusion noise based on number counts (source density criterion, probability of deflection criterion etc.) and based on the analysis of the simulated maps. We show that resolution effects influence the number counts curves and noise estimations.