Deep Forest: Neural Network reconstruction of intergalactic medium temperature

TL;DR

This paper demonstrates that deep learning can effectively reconstruct and map the temperature structure of the intergalactic medium from high-resolution Lyman-alpha forest spectra, aiding understanding of Helium reionization.

Contribution

It introduces a neural network approach trained on simulated spectra to accurately infer IGM temperature and detect hot regions, advancing methods for studying cosmic reionization.

Findings

Neural networks can reconstruct IGM temperature with high resolution spectra.

High temperature regions of 25 Mpc/h and 20,000 K are detectable.

Combining multiple sightlines enables tomographic imaging of hot bubbles.

Abstract

We explore the use of Deep Learning to infer the temperature of the intergalactic medium from the transmitted flux in the high redshift Lyman-alpha forest. We train Neural Networks on sets of simulated spectra from redshift z=2-3 outputs of cosmological hydrodynamic simulations, including high temperature regions added in post-processing to approximate bubbles heated by Helium-II reionization. We evaluate how well the trained networks are able to reconstruct the temperature from the effect of Doppler broadening in the simulated input Lyman-alpha forest absorption spectra. We find that for spectra with high resolution (10 km/s pixel) and moderate signal to noise (20-50), the neural network is able to reconstruct the IGM temperature smoothed on scales of 6 Mpc/h quite well. Concentrating on discontinuities we find that high temperature regions of width 25 Mpc/h and temperature 20,000 K can be fairly easily detected and characterized. We show an example where multiple sightlines are combined to yield tomographic images of hot bubbles. Deep Learning techniques may be useful in this way to help us understand the complex temperature structure of the intergalactic medium around the time of Helium reionization.