  We propose a new scheme to reconstruct the baryon acoustic oscillations (BAO)
signal, which contains key cosmological information, based on deep
convolutional neural networks (CNN). Trained with almost no fine-tuning, the
network can recover large-scale modes accurately in the test set: the
correlation coefficient between the true and reconstructed initial conditions
reaches $90\%$ at $k\leq 0.2 h\mathrm{Mpc}^{-1}$, which can lead to significant
improvements of the BAO signal-to-noise ratio down to
$k\simeq0.4h\mathrm{Mpc}^{-1}$. Since this new scheme is based on the
configuration-space density field in sub-boxes, it is local and less affected
by survey boundaries than the standard reconstruction method, as our tests
confirm. We find that the network trained in one cosmology is able to
reconstruct BAO peaks in the others, i.e. recovering information lost to
non-linearity independent of cosmology. The accuracy of recovered BAO peak
positions is far less than that caused by the difference in the cosmology
models for training and testing, suggesting that different models can be
distinguished efficiently in our scheme. It is very promising that Our scheme
provides a different new way to extract the cosmological information from the
ongoing and future large galaxy surveys.
