Given the X-ray observations of galaxy clusters by eROSITA and the multi-cosmology simulations, one can compare their outcomes and constrain the cosmological parameters inversely via machine learning. The key point lies in understanding the simulations and observations from a probabilistic perspective. We are first to match the individual observed eROSITA galaxy cluster to the...
We derive constraints on the neutrino mass using a frequentist approach based on likelihood profiles. Our analysis leverages the latest cosmological datasets, including DESI DR2 BAO and DR1 full-shape likelihoods, CMB and CMB lensing from Planck and ACT, recent Lyman-alpha 1D power spectrum emulation applied to eBOSS, and supernovae data.
Profile likelihoods offer several advantages when...
