Euclid preparation: XIX. Impact of magnification on photometric galaxy clustering
F. Lepori, I. Tutusaus, C. Viglione, C. Bonvin, S. Camera, F.J., Castander, R. Durrer, P. Fosalba, G. Jelic-Cizmek, M. Kunz, J. Adamek, S., Casas, M. Martinelli, Z. Sakr, D. Sapone, A. Amara, N. Auricchio, C., Bodendorf, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann

TL;DR
This paper demonstrates that lensing magnification significantly influences galaxy clustering measurements and cosmological parameter estimates in Euclid, emphasizing the necessity of including this effect for accurate analysis.
Contribution
It provides a detailed assessment of lensing magnification's impact on Euclid's galaxy clustering and cross-correlation analyses, highlighting the importance of incorporating this effect in future cosmological studies.
Findings
Magnification reduces errors on key cosmological parameters by 20-35%.
Neglecting magnification causes parameter shifts up to 1.6σ.
Ignoring magnification biases the combined analysis by up to 6σ.
Abstract
We investigate the importance of lensing magnification for estimates of galaxy clustering and its cross-correlation with shear for the photometric sample of Euclid. Using updated specifications, we study the impact of lensing magnification on the constraints and the shift in the estimation of the best fitting cosmological parameters that we expect if this effect is neglected. We follow the prescriptions of the official Euclid Fisher matrix forecast for the photometric galaxy clustering analysis and the combination of photometric clustering and cosmic shear. The slope of the luminosity function (local count slope), which regulates the amplitude of the lensing magnification, and the galaxy bias have been estimated from the Euclid Flagship simulation.We find that magnification significantly affects both the best-fit estimation of cosmological parameters and the constraints in the galaxy…
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Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Astronomy and Astrophysical Research · Gaussian Processes and Bayesian Inference
