  During the Epoch of reionisation, the intergalactic medium is reionised by
the UV radiation from the first generation of stars and galaxies. One tracer of
the process is the 21 cm line of hydrogen that will be observed by the Square
Kilometre Array (SKA) at low frequencies, thus imaging the distribution of
ionised and neutral regions and their evolution. To prepare for these upcoming
observations, we investigate a deep learning method to predict from 21 cm maps
the reionisation time field (treion(r)), i.e. the time at which each location
has been reionised. treion(r) encodes the propagation of ionisation fronts in a
single field, gives access to times of local reionisation or to the extent of
the radiative reach of early sources. Moreover it gives access to the time
evolution of ionisation on the plane of sky, when such evolution is usually
probed along the line-of-sight direction. We trained a convolutional neural
network (CNN) using simulated 21 cm maps and reionisation times fields produced
by the simulation code 21cmFAST . We also investigate the performance of the
CNN when adding instrumental effects. Globally, we find that without
instrumental effects the 21 cm maps can be used to reconstruct the associated
reionisation times field in a satisfying manner: the quality of the
reconstruction is dependent on the redshift at which the 21 cm observation is
being made and in general it is found that small scale (<10cMpc/h) features are
smoothed in the reconstructed field, while larger scale features are well
recovered. When instrumental effects are included, the scale dependance of
reconstruction is even further pronounced, with significant smoothing on small
and intermediate scales.
