Ponente
Descripción
In this talk we explore the consequences of inferring cosmological parameters with uncertain theory predictions in which we include potential biases and noise sources. We will discuss that noise sources in the theory prediction translate into scatter noise in the likelihood and will provide an estimation of this quantity. We will show issues that might appear if trying to estimate parameters from such likelihood and suggest the use of Gaussian processes to reconstruct it. Furthermore, we introduce an iterative process based on Bayesian optimization to explore the cosmological parameter space in an efficient and effective manner. Finally, we will discuss and quantify the potential biases appearing when using a N-body simulation as the theory prediction for the galaxy clustering parameter estimation.
