Constraining the Reionization History using Bayesian Normalizing Flows

@article{Hortua2020ConstrainingTR,
  title={Constraining the Reionization History using Bayesian Normalizing Flows},
  author={H'ector J. Hort'ua and Luigi Malag{\`o} and Riccardo Volpi},
  journal={Machine Learning: Science and Technology},
  year={2020},
  volume={1}
}
Upcoming experiments such as Hydrogen Epoch of Reionization Array(HERA) and the Square Kilometre Array (SKA) are intended to measure the 21 cm signal over a wide range of redshifts, representing an incredible opportunity in advancing our understanding about the nature of cosmic reionization. At the same time these kind of experiments will present new challenges in processing the extensive amount of data generated, calling for the development of automated methods capable of precisely estimating… 
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13 Citations
Background Citations
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Methods Citations
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13 Citations

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Flow-based likelihoods for non-Gaussian inference

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QuasarNet: A new research platform for the data-driven investigation of black holes

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Constraining cosmological parameters from N-body simulations with Variational Bayesian Neural Networks

This work implements multiplicative normalizing (MNFs), a family of approximate posteriors for the parameters of BNNs with the purpose of enhancing the flexibility of the variational posterior distribution, to extract Ω m, h, and σ 8 from the QUIJOTE simulations.

Amortized Bayesian Inference for Supernovae in the Era of the Vera Rubin Observatory Using Normalizing Flows

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Machine Learning and Cosmology

Current and ongoing developments relating to the application of machine learning within cosmology and a set of recommendations aimed at maximizing the scientific impact of these burgeoning tools over the coming decade through both technical development as well as the fostering of emerging communities are summarized.

Cosmology Intertwined: A Review of the Particle Physics, Astrophysics, and Cosmology Associated with the Cosmological Tensions and Anomalies

Understanding the Impact of Semi-numeric Reionization Models when Using CNNs

It is shown that neural networks trained on input data from only one model produce poor predictions on data from the other model, and this finding has important implications for future analyzes on observation data, and encourages the use of multiple models to produce images that capture the full complexity of the EoR.

Simulation-based Inference of Reionization Parameters from 3D Tomographic 21 cm Light-cone Images

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