Constraining the Reionization History using Bayesian Normalizing Flows. (arXiv:2005.07694v1 [astro-ph.CO])
<a href="http://arxiv.org/find/astro-ph/1/au:+Hortua_H/0/1/0/all/0/1">Héctor J. Hortúa</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Malago_L/0/1/0/all/0/1">Luigi Malago</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Volpi_R/0/1/0/all/0/1">Riccardo Volpi</a>
The next generation 21 cm surveys open a new window onto the early stages of
cosmic structure formation and provide new insights about the Epoch of
Reionization (EoR). However, the non-Gaussian nature of the 21 cm signal along
with the huge amount of data generated from these surveys will require more
advanced techniques capable to efficiently extract the necessary information to
constrain the Reionization History of the Universe. In this paper we present
the use of Bayesian Neural Networks (BNNs) to predict the posterior
distribution for four astrophysical and cosmological parameters. Besides
achieving state-of-the-art prediction performances, the proposed methods
provide accurate estimation of parameters uncertainties and infer correlations
among them. Additionally, we demonstrate the advantages of Normalizing Flows
(NF) combined with BNNs, being able to model more complex output distributions
and thus capture key information as non-Gaussianities in the parameter
conditional density distribution for astrophysical and cosmological dataset.
Finally, we propose novel calibration methods employing Normalizing Flows after
training, to produce reliable predictions, and we demonstrate the advantages of
this approach both in terms of computational cost and prediction performances.
The next generation 21 cm surveys open a new window onto the early stages of
cosmic structure formation and provide new insights about the Epoch of
Reionization (EoR). However, the non-Gaussian nature of the 21 cm signal along
with the huge amount of data generated from these surveys will require more
advanced techniques capable to efficiently extract the necessary information to
constrain the Reionization History of the Universe. In this paper we present
the use of Bayesian Neural Networks (BNNs) to predict the posterior
distribution for four astrophysical and cosmological parameters. Besides
achieving state-of-the-art prediction performances, the proposed methods
provide accurate estimation of parameters uncertainties and infer correlations
among them. Additionally, we demonstrate the advantages of Normalizing Flows
(NF) combined with BNNs, being able to model more complex output distributions
and thus capture key information as non-Gaussianities in the parameter
conditional density distribution for astrophysical and cosmological dataset.
Finally, we propose novel calibration methods employing Normalizing Flows after
training, to produce reliable predictions, and we demonstrate the advantages of
this approach both in terms of computational cost and prediction performances.
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