# Power spectrum modelling of galaxy and radio intensity maps including observational effects

## TL;DR

This paper develops a comprehensive model for how observational effects alter the power spectra of galaxy and radio intensity maps, aiding accurate cosmological analysis of survey data.

## Contribution

It introduces a general framework for modeling the impact of observational effects on power spectra, including noise, weighting, smoothing, and pixelization, validated with simulations.

## Key findings

- Derived models for auto- and cross-power spectra under observational effects.
- Validated models using N-body simulation datasets.
- Provided open-source Python code for power spectrum analysis.

## Abstract

Fluctuations in the large-scale structure of the Universe contain significant information about cosmological physics, but are modulated in survey datasets by various observational effects. Building on existing literature, we provide a general treatment of how fluctuation power spectra are modified by a position-dependent selection function, noise, weighting, smoothing, pixelization and discretization. Our work has relevance for the spatial power spectrum analysis of galaxy surveys with spectroscopic or accurate photometric redshifts, and radio intensity-mapping surveys of the sky brightness temperature including generic noise, telescope beams and pixelization. We consider the auto-power spectrum of a field, the cross-power spectrum between two fields and the multipoles of these power spectra with respect to a curved sky, deriving the corresponding power spectrum models, estimators, errors and optimal weights. We note that "FKP weights" for individual tracers do not in general provide the optimal weights when measuring the cross-power spectrum. We validate our models using mock datasets drawn from N-body simulations, and provide the python code we use for these tests at https://github.com/cblakeastro/intensitypower. Our treatment should be useful for modelling and studying cosmological fluctuation fields in observed and simulated datasets.