A complete framework for cosmological emulation and inference with CosmoPower

Understand

We present a coherent, re-usable python framework which further builds on the cosmological emulator code CosmoPower.

• In the current era of high-precision cosmology, we require high-accuracy calculations of cosmological observables with Einstein-Boltzmann codes.
• For detailed statistical analyses, such codes often incur high costs in terms of computing power, making parameter space exploration costly, especially for beyond-$\Lambda$CDM analyses.
• Machine learning-enabled emulators of Einstein-Boltzmann codes have emerged as a solution to this problem and have become a common way to perform fast cosmological analyses.