Developing a unified pipeline for large-scale structure data analysis with angular power spectra -- II. A case study for magnification bias and radio continuum surveys
Konstantinos Tanidis, Stefano Camera, and David Parkinson

TL;DR
This paper develops a modular pipeline for analyzing large-scale structure data using angular power spectra, emphasizing the importance of modeling magnification bias in galaxy surveys like EMU to avoid biased cosmological parameter estimates.
Contribution
It introduces a publicly available code that incorporates magnification bias into angular power spectrum analysis, demonstrating its significance in deep radio continuum surveys.
Findings
Correct modeling of magnification bias prevents parameter estimation bias.
Neglecting magnification leads to biased results with wide redshift bins.
Including magnification enhances cosmological information and reduces degeneracies.
Abstract
Following on our purpose of developing a unified pipeline for large-scale structure data analysis with angular (i.e. harmonic-space) power spectra, we now include the weak lensing effect of magnification bias on galaxy clustering in a publicly available, modular parameter estimation code. We thus forecast constraints on the parameters of the concordance cosmological model, dark energy, and modified gravity theories from galaxy clustering tomographic angular power spectra. We find that a correct modelling of magnification is crucial in order not to bias the estimation of cosmological parameters, especially in the case of deep galaxy surveys. Our case study adopts specifications of the Evolutionary Map of the Universe (EMU), which is a full-sky, deep radio-continuum survey, and is expected to probe the Universe up to redshift . We assume the Limber approximation, and include…
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