Letter to the Editor

Implications of SPT and eROSITA cosmologies for Planck cluster number counts and t-SZ power spectrum

¹ Université Paris-Saclay, CNRS, Institut d’Astrophysique Spatiale, 91405 Orsay, France
² Université Paris-Saclay, Université Paris Cité, CEA, CNRS, AIM, 91191 Gif-sur-Yvette, France
³ Department of Astronomy, Cornell University, Ithaca, NY 14853, USA

^⋆ Corresponding author: gaspard.aymerich@universite-paris-saclay.fr

Received: 3 September 2025
Accepted: 17 October 2025

Abstract

Comparison between cosmological studies is usually performed in a statistical manner at the level of the posteriors of cosmological parameters. In this Letter, we show how this approach poorly reflects the differences between cosmological analyses, when applied to cosmological studies using galaxy cluster abundances. We illustrate this by deriving the implications of the best-fit cosmologies from the recent SPT and eROSITA cluster number counts analyses on the Planck thermal Sunyaev-Zeldovich (t-SZ) probes. We first fix the mass calibration, and find that the Planck cluster sample would theoretically contain 498 clusters with the SPT cosmology, and 1098 clusters with the eROSITA cosmology, instead of the 439 clusters observed. We then fit the Planck number counts with cosmological parameters fixed to the SPT and eROSITA best-fit values, varying only the hydrostatic mass bias, and find required biases of 0.790 ± 0.070 and 0.630 ± 0.034 for SPT and eROSITA respectively, instead of the 0.844^+0.055_−0.062 derived in Aymerich et al. (arXiv eprints [arXiv:2509.02068]). Lastly, we compute the expected t-SZ power spectrum obtained from the SPT and eROSITA cosmologies, and compare these to the Planck measurement. While the predicted SPT angular power spectrum is in good agreement with the Planck measurements, the normalisation of the predicted eROSITA angular power spectrum is two times higher at all scales. These two tests highlight the power of comparing predicted cluster abundances and t-SZ power spectra to measured data in a physically interpretable way.