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During the Epoch of reionisation, the intergalactic medium is reionised bythe UV radiation from the first generation of stars and galaxies. One tracer ofthe process is the 21 cm line of hydrogen that will be observed by the SquareKilometre Array (SKA) at low frequencies, thus imaging the distribution ofionised and neutral regions and their evolution. To prepare for these upcomingobservations, we investigate a deep learning method to predict from 21 cm mapsthe reionisation time field (treion(r)), i.e. the time at which each locationhas been reionised. treion(r) encodes the propagation of ionisation fronts in asingle field, gives access to times of local reionisation or to the extent ofthe radiative reach of early sources. Moreover it gives access to the timeevolution of ionisation on the plane of sky, when such evolution is usuallyprobed along the line-of-sight direction. We trained a convolutional neuralnetwork (CNN) using simulated 21 cm maps and reionisation times fields producedby the simulation code 21cmFAST . We also investigate the performance of theCNN when adding instrumental effects. Globally, we find that withoutinstrumental effects the 21 cm maps can be used to reconstruct the associatedreionisation times field in a satisfying manner: the quality of thereconstruction is dependent on the redshift at which the 21 cm observation isbeing made and in general it is found that small scale (<10cMpc/h) features aresmoothed in the reconstructed field, while larger scale features are wellrecovered. When instrumental effects are included, the scale dependance ofreconstruction is even further pronounced, with significant smoothing on smalland intermediate scales.

Reionisation time fields reconstruction from 21 cm signal maps