Analysing the 21cm signal from the Epoch of Reionization with artificial neural networks

Abstract

The 21 cm signal from the Epoch of Reionization should be observed within the next decade. While a simple statistical detection is expected with SKA pathfinders, the SKA will hopefully produce a full 3D mapping of the signal. To extract from the observed data constraints on the parameters describing the underlying astrophysical processes, inversion methods must be developed. For example, the Markov Chain Monte Carlo method has been successfully applied. Here we test another possible inversion method: artificial neural networks (ANN). We produce a training set which consists of 70 individual sample. Each sample is made of the 21 cm power spectrum at different redshifts produced with the 21cmFast code plus the value of three parameters used in the semi-numerical simulations that describe astrophysical processes. Using this set we train the network to minimize the error between the parameter values it produces as an output and the true values. We explore the impact of the architecture of the network on the quality of the training. Then we test the trained network on the new set of 54 test samples with different values of the parameters. We find that the quality of the parameter reconstruction depends on the sensitivity of the power spectrum to the different parameters at a given redshift, that including thermal noise and sample variance decreases the quality of the reconstruction and that using the power spectrum at several redshifts as an input to the ANN improves the quality of the reconstruction. We conclude that ANNs are a viable inversion method whose main strength is that they require a sparse exploration of the parameter space and thus should be usable with full numerical simulations.

8 figures, 2 tables, accepted in MNRAS

References in corpus (15)

• Deep Learning in Neural Networks: An Overview
• Cosmology at Low Frequencies: The 21 cm Transition and the High-Redshift Universe
• Observational constraints on Cosmic Reionization
• Efficient Simulations of Early Structure Formation and Reionization
• The morphology of HII regions during reionization
• 21CMMC: an MCMC analysis tool enabling astrophysical parameter studies of the cosmic 21 cm signal
• Radiative transfer simulations of cosmic reionization I: methodology and initial results
• Accelerated Evolution of Ly$\alpha$ Luminosity Function at $\text{z}\gtrsim 7$ Revealed by the Subaru Ultra-Deep Survey for Ly$\alpha$ Emitters at $\text{z}=7.3$
• First Season MWA EoR Power Spectrum Results at Redshift 7
• Multi-redshift limits on the 21cm power spectrum from PAPER
• Detection and extraction of signals from the epoch of reionization using higher order one-point statistics
• PkANN - I. Non-linear matter power spectrum interpolation through artificial neural networks
• The Difference PDF of 21-cm Fluctuations: A Powerful Statistical Tool for Probing Cosmic Reionization
• Machine-learning Selection of Optical Transients in Subaru/Hyper Suprime-Cam Survey
• Photo-$z$ with CuBAN$z$: An improved photometric redshift estimator using Clustering aided Back Propagation Neural network

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