CONNECT: A neural network based framework for emulating cosmological observables and cosmological parameter inference

Understand

Bayesian parameter inference is an essential tool in modern cosmology, and typically requires the calculation of $10^5$--$10^6$ theoretical models for each inference of model parameters for a given dataset combination.

• Computing these models by solving the linearised Einstein-Boltzmann system usually takes tens of CPU core-seconds per model, making the entire process very computationally expensive.
• In this paper we present \textsc{connect}, a neural network framework emulating \textsc{class} computations as an easy-to-use plug-in for the popular sampler \textsc{MontePython}.
• \textsc{connect} uses an iteratively trained neural network which emulates the observables usually computed by \textsc{class}.