Simulation-based priors that depend on cluster morphology allow mock X-ray/SZ cluster samples to recover the input Hubble constant with 4% (100 clusters) and 1.5% (1000 clusters) precision plus a systematic floor of 0.6–0.8 km/s/Mpc.
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A new deep-to-wide transfer function reduces mean redshift biases in Euclid tomographic bins by matching reference sample color distributions to the wide survey.
Stacking analysis of eROSITA observations detects statistically significant X-ray signal from hot gas out to 2 r_200m with a transition at the halo boundary, showing higher gas spread than in IllustrisTNG simulations.
Simulations of Euclid cluster data demonstrate that combining number counts and clustering improves the figure of merit by over 300 percent, with photo-z uncertainties broadening posteriors by 20-30 percent and smaller-scale clustering adding useful information.
citing papers explorer
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The Three Hundred Project: Validating $H_0$ inference from mock X-ray and millimetre analyses of galaxy clusters
Simulation-based priors that depend on cluster morphology allow mock X-ray/SZ cluster samples to recover the input Hubble constant with 4% (100 clusters) and 1.5% (1000 clusters) precision plus a systematic floor of 0.6–0.8 km/s/Mpc.
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Euclid: Improving redshift distribution reconstruction using a deep-to-wide transfer function
A new deep-to-wide transfer function reduces mean redshift biases in Euclid tomographic bins by matching reference sample color distributions to the wide survey.
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The SRG/eROSITA All-Sky Survey. Detection of shock-heated gas beyond the halo boundary into the accretion region
Stacking analysis of eROSITA observations detects statistically significant X-ray signal from hot gas out to 2 r_200m with a transition at the halo boundary, showing higher gas spread than in IllustrisTNG simulations.
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Euclid: Exploring observational systematics in cluster cosmology -- a comprehensive analysis of cluster counts and clustering
Simulations of Euclid cluster data demonstrate that combining number counts and clustering improves the figure of merit by over 300 percent, with photo-z uncertainties broadening posteriors by 20-30 percent and smaller-scale clustering adding useful information.