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.
Forecasting the E_G measurements from the photometric and spectroscopic surveys of Chinese Space Station Survey Telescope (CSST)
School of Physics and Astronomy, Sun Yat-sen University（中山大学物理学院）；CSST Science Center for the Guangdong-Hong Kong-Macau Greater Bay Area, Sun Yat-sen University（中山大学粤港澳大湾区天文科学中心）
Forecasting the Constraint on the Hu-Sawicki $f(R)$ Modified Gravity in the CSST $3\times2$pt Photometric Survey
National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；University of Chinese Academy of Sciences（中国科学院大学）；Science Center for China Space Station Telescope, National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台中国空间站望远镜科学中心）；Department of Physics, College of Sciences, Northeastern University（东北大学理学院物理系）；Centre for High Energy Physics, Peking University（北京大学高能物理研究中心）；State Key Laboratory of Radio Astronomy and Technology（射电天文与技术国家重点实验室）
Exploring Cosmological Constraints of the Void-Lensing Cross-Correlation in the CSST Photometric Survey
National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；School of Astronomy and Space Sciences, University of Chinese Academy of Sciences(UCAS)（中国科学院大学天文与空间科学学院）；Science Center for China Space Station Telescope, National Astronomical Observatories, Chinese Academy of Science（中国科学院国家天文台中国空间站望远镜科学中心）
Predicting Primordial Non-Gaussianity Measurements in the CSST Spectroscopic Survey
National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；School of Astronomy and Space Sciences, University of Chinese Academy of Sciences（中国科学院大学天文与空间科学学院）；Science Center for CSST, National Astronomical Observatories, CAS（中国科学院国家天文台CSST科学中心）；Department of Physics, College of Sciences, Northeastern University（东北大学理学院物理系）；Centre for High Energy Physics, Peking University（北京大学高能物理中心）；Institute for Frontiers in Astronomy and Astrophysics, Beijing Normal University（北京师范大学天文与前沿科学研究院）
Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey
National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；University of Chinese Academy of Sciences（中国科学院大学）；Science Center for China Space Station Telescope, National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台中国空间站望远镜科学中心）；Center for High Energy Physics, Peking University（北京大学高能物理中心）；Department of Physics, College of Sciences, Northeastern University（东北大学理学院物理系）；State Key Laboratory of Radio Astronomy and Technology（射电天文与技术国家重点实验室）
Filtering Interlopers with Photometry and Diagnostic Features: A Machine Learning Framework Validated with CSST Slitless Spectroscopy
Shanghai Jiao Tong University（上海交通大学）；State Key Laboratory of Dark Matter Physics, Shanghai Jiao Tong University（上海交通大学暗物质物理全国重点实验室）；Department of Astronomy, School of Physics and Astronomy, Shanghai Jiao Tong University（上海交通大学物理与天文学院天文学系）；Key Laboratory for Particle Astrophysics and Cosmology (MOE)/Shanghai Key Laboratory for Particle Physics and Cosmology, Shanghai Jiao Tong University（上海交通大学粒子天体物理与宇宙学教育部重点实验室/上海粒子物理与宇宙学重点实验室）
The Effect of Slitless Spectroscopic Redshift Uncertainty and Interlopers on BAO Measurements from CSST-like Samples
Nanjing Institute of Astronomical Optics & Technology, Chinese Academy of Sciences（中国科学院南京天文光学技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Department of Astronomy, School of Physics and Astronomy, Shanghai Jiao Tong University（上海交通大学物理与天文学院天文学系）；Key Laboratory for Particle Astrophysics and Cosmology (MOE)/Shanghai Key Laboratory for Particle Physics and Cosmology（粒子天体物理教育部重点实验室/上海粒子物理与宇宙学重点实验室）；Tsung-Dao Lee Institute, Shanghai Jiao Tong University（上海交通大学李政道研究所）
BayeSED-GALAXIES II. Bayesian full spectrum analysis of galaxies and application in the CSST wide-field slitless spectroscopy survey
International Centre of Supernovae (ICESUN), Yunnan Key Laboratory of Supernova Research, Yunnan Observatories, Chinese Academy of Sciences (CAS)（中国科学院云南天文台）；Center for Astronomical Mega-Science, Chinese Academy of Sciences（中国科学院国家天文台）；Tsung-Dao Lee Institute and State Key Laboratory of Dark Matter Physics, Shanghai Jiao Tong University（上海交通大学李政道研究所）；Department of Astronomy, School of Physics and Astronomy, Shanghai Jiao Tong University（上海交通大学物理与天文学院）；National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）
Cross-Comparison of Galaxies Detected in the CSST Spectroscopic Survey and the SKA HI Survey
National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Normal University（上海师范大学）
Extracting redshifts from 2D slitless spectroscopic images using deep learning for the CSST galaxy survey
National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；Science Center for China Space Station Telescope, National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台中国空间站望远镜科学中心）；University of Chinese Academy of Sciences（中国科学院大学）
