Working towards an optimal sampling of the 21 cm signal parameter space

Abstract

With a statistical detection of the 21 cm signal fluctuations from the Epoch of Reionization (EoR) expected in the next few years, there is an interest in developing robust and precise techniques to constrain the underlying astrophysical parameters. Bayesian inference with Markov Chain Monte Carlo, or different types of supervised learning for backward modelling (from signal to parameters) are examples of such techniques. They usually require many instances of forward modelling (from parameters to signal) in sampling the parameters space, either when performing the steps of the Markov Chain or when building a training sample for supervised learning. As forward modelling can be costly (if performed with numerical simulations for example), we should attempt to perform an optimal sampling according to some principle. With this goal in mind, we present an approach based on defining a metric on the space of observables, induced by the manner through which the modelling creates a mapping from the parameter space onto the space of observables. This metric bears a close connection to Jeffreys' prior from information theory. It is used to generate a homogeneous and isotropic sampling of the signal space with two different methods. We show that when the resulting optimized samplings, created with 21cmFAST, are used to train a neural network we obtain a modest reduction of the error on parameter reconstruction of ~10% (compared to a naïve sampling of the same size). Excluding the borders of the parameter space region, the improvement is more substantial, on the order of 30-40%.

arXiv commentSubmitted to MNRAS

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Cited by in corpus (2)

• Improved supervised learning methods for EoR parameters reconstruction
• A unified framework for 21cm tomography sample generation and parameter inference with Progressively Growing GANs