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Abstract:

The Euclid mission of the European Space Agency seeks to understand the Universe’s expansion history and the nature of dark energy, through measurements of cosmic shear. This requires a very accurate estimate of the true redshift distribution of the galaxies, with the systematic error in the mean redshift satisfying σ⟨z⟩ < 0.002(1 + z) per tomographic bin. Achieving this accuracy relies on reference samples with spectroscopic redshifts, together with a procedure to match them to survey sources for which only photometric redshifts are available. One important source of systematic uncertainty is the mismatch in photometric properties between galaxies in the Euclid survey and the reference objects. We develop a method to degrade the photometry of objects with deep photometry to match the properties of any shallower survey in the multi-band photometric space, preserving all the correlations between the fluxes and their uncertainties. We compare our transfer method with more demanding image-based methods, such as Balrog from the Dark Energy Survey Collaboration. According to our metrics, our method outperforms Balrog. We implement our method in the redshift distribution reconstruction, based on the self-organising map approach, and test it using a realistic sample from the Euclid Flagship Mock Galaxy Simulation. We find that the key ingredient is to ensure that the reference objects are distributed in the colour space the same way as the wide-survey objects, which can be efficiently achieved with our transfer method. In our best implementation, the mean redshift biases are consistently reduced across the tomographic bins, bringing a significant fraction of them within the Euclid accuracy requirements in all tomographic bins. Equally importantly, the tests allow us to pinpoint which step in the calibration pipeline has the strongest impact on achieving the required accuracy. Our approach also reproduces the overall redshift distributions, which are crucial for applications such as angular clustering. The agreement between the reconstructed and true distributions demonstrates both the feasibility and robustness of the approach. This implementation is sufficient for Euclid Data Release 1 and provides a solid foundation for subsequent data releases.

Abstract:

Secondary anisotropies in the cosmic microwave background (CMB) contain information that can be used to test both cosmological models and models of galaxy formation. Starting from light-cone-based healpix maps and catalogues, we present a new set of mock CMB maps constructed in a self-consistent manner from the flamingo suite of cosmological hydrodynamical simulations, including CMB lensing, thermal and kinetic Sunyaev–Zel’dovich effects, cosmic infrared background, radio point source and anisotropic screening maps. We show that these simulations reproduce a wide range of observational constraints. We also compare our simulations with previous predictions based on dark matter-only simulations which generally model the secondary anisotropies independently from one another, concluding that our hydrodynamical simulation mocks perform at least as well as previous mocks in matching the observations whilst retaining self-consistency in the predictions of the different components. Using the model variations in flamingo, we further explore how the signals depend on cosmology and feedback modelling, and we predict cross-correlations between some of the signals that differ significantly from those in previous mocks. The mock CMB maps should provide a valuable resource for exploring correlations between different secondary anisotropies and other large-scale structure tracers, and can be applied to forecasts for upcoming surveys.

Abstract:

This study explores the impact of observational and modelling systematic effects on cluster number counts and cluster clustering and provides model prescriptions for their joint analysis in the context of the Euclid survey. Using 1000 Euclid -like cluster catalogues, we investigated the effect of systematic uncertainties on cluster summary statistics and their auto- and cross-covariance, and performed a likelihood analysis to evaluate their impact on cosmological constraints, with a focus on the matter density parameter (Ω m ) and on the power spectrum amplitude ( σ 8 ). Combining cluster clustering with number counts significantly improves cosmological constraints, with the figure of merit increasing by over 300% compared to number counts alone. We confirm that the two probes are uncorrelated, and the cosmological constraints derived from their combination are almost insensitive to the cosmology dependence of the covariance. We find that photometric redshift uncertainties broaden cosmological posteriors by 20–30%, while secondary effects such as redshift-space distortions (RSDs) have a smaller impact on the posteriors – 5% for clustering alone, 10% when combining probes – but can significantly bias the constraints if neglected. We show that clustering data below 60 h −1 Mpc provides additional constraining power, while scales larger than the acoustic oscillation scale add almost no information on Ω m and σ 8 parameters. The RSDs and photo- z uncertainties also influence the number count covariance, with a significant impact of about 15–20% on the parameter constraints.