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The estimation of spectroscopic and photometric redshifts (spec-z andphoto-z) is crucial for future cosmological surveys. It can directly affectseveral powerful measurements of the Universe, e.g. weak lensing and galaxyclustering. In this work, we explore the accuracies of spec-z and photo-z thatcan be obtained in the China Space Station Optical Surveys (CSS-OS), which is anext-generation space survey, using neural networks. The 1-dimensionalConvolutional Neural Networks (1-d CNN) and Multi-Layer Perceptron (MLP, one ofthe simplest forms of Artificial Neural Network) are employed to derive thespec-z and photo-z, respectively. The mock spectral and photometric data usedfor training and testing the networks are generated based on the COSMOScatalog. The networks have been trained with noisy data by creating Gaussianrandom realizations to reduce the statistical effects, resulting in similarredshift accuracy for both high-SNR (signal to noise ratio) and low-SNR data.The probability distribution functions (PDFs) of the predicted redshifts arealso derived via Gaussian random realizations of the testing data, and then thebest-fit redshifts and 1-sigma errors also can be obtained. We find that ournetworks can provide excellent redshift estimates with accuracies ~0.001 and0.01 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. Thefractions of catastrophic redshifts or outliers can be dramatically suppressedcomparing to the ordinary template-fitting method. This indicates that theneural network method is feasible and powerful for spec-z and photo-zestimations in future cosmological surveys.

Spectroscopic and Photometric Redshift Estimation by Neural Networks For the China Space Station Optical Survey (CSS-OS)