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The Epoch of Reionization (EoR) features a rich interplay between the firstluminous sources and the low-density gas of the intergalactic medium (IGM),where photons from these sources ionize the IGM. There are currently fewobservational constraints on key observables related to the EoR, such as themidpoint and duration of reionization. Although upcoming observations of the 21cm power spectrum with next-generation radio interferometers such as theHydrogen Epoch of Reionization Array (HERA) and the Square Kilometre Array(SKA) are expected to provide information about the midpoint of reionizationreadily, extracting the duration from the power spectrum alone is a moredifficult proposition. As an alternative method for extracting informationabout reionization, we present an application of convolutional neural networks(CNNs) to images of reionization. These images are two-dimensional in the planeof the sky, and extracted at a series of redshift values to generate "imagecubes" that are qualitatively similar to those of the HERA and the SKA willgenerate in the near future. Additionally, we include the impact that thebright foreground signal from the the Milky Way imparts on such image cubesfrom interferometers, but do not include the noise induced from observations.We show that we are able to recover the duration of reionization $\Delta$z towithin 5% using CNNs, assuming that the midpoint of reionization is alreadyrelatively well constrained. These results have exciting impacts for estimating$\tau$, the optical depth to the cosmic microwave background, which can helpconstrain other cosmological parameters.

Machine Learning Applied to the Reionization History of the Universe in the 21 cm Signal