The Digital Press

Galaxy cluster counts can reveal key cosmological parameters, but systematic uncertainties limit their precision. This study introduces a simulation-based inference method using neural networks to better model cluster observations and improve parameter estimates.

Why it matters: Reducing systematics in cluster surveys sharpens constraints on fundamental cosmological parameters like Ωm and σ8.

The big picture: Forward modeling with simulations and neural networks integrates multiple observables, enhancing cosmological analyses from future surveys.

Stunning stat: Mass calibration must be accurate to better than 10% to avoid significant biases in Ωm and σ8 estimates.

Quick takeaway: The parameter S_8 is more robust against mass calibration errors, offering a reliable cosmological probe.