Deep Forest: Neural Network reconstruction of intergalactic medium
  temperature
  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.
