  The estimation of spectroscopic and photometric redshifts (spec-z and
photo-z) is crucial for future cosmological surveys. It can directly affect
several powerful measurements of the Universe, e.g. weak lensing and galaxy
clustering. In this work, we explore the accuracies of spec-z and photo-z that
can be obtained in the China Space Station Optical Surveys (CSS-OS), which is a
next-generation space survey, using neural networks. The 1-dimensional
Convolutional Neural Networks (1-d CNN) and Multi-Layer Perceptron (MLP, one of
the simplest forms of Artificial Neural Network) are employed to derive the
spec-z and photo-z, respectively. The mock spectral and photometric data used
for training and testing the networks are generated based on the COSMOS
catalog. The networks have been trained with noisy data by creating Gaussian
random realizations to reduce the statistical effects, resulting in similar
redshift accuracy for both high-SNR (signal to noise ratio) and low-SNR data.
The probability distribution functions (PDFs) of the predicted redshifts are
also derived via Gaussian random realizations of the testing data, and then the
best-fit redshifts and 1-sigma errors also can be obtained. We find that our
networks can provide excellent redshift estimates with accuracies ~0.001 and
0.01 on spec-z and photo-z, respectively. Compared to existing photo-z codes,
our MLP has similar accuracy but is more efficient in the training process. The
fractions of catastrophic redshifts or outliers can be dramatically suppressed
comparing to the ordinary template-fitting method. This indicates that the
neural network method is feasible and powerful for spec-z and photo-z
estimations in future cosmological surveys.
