Extracting Photometric Redshift from Galaxy Flux and Image Data using Neural Networks in the CSST Survey

Abstract

The accuracy of galaxy photometric redshift (photo-$z$) can significantly affect the analysis of weak gravitational lensing measurements, especially for future high-precision surveys. In this work, we try to extract photo-$z$ information from both galaxy flux and image data expected to be obtained by China Space Station Telescope (CSST) using neural networks. We generate mock galaxy images based on the observational images from the Advanced Camera for Surveys of Hubble Space Telescope (HST-ACS) and COSMOS catalogs, considering the CSST instrumental effects. Galaxy flux data are then measured directly from these images by aperture photometry. The Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) are constructed to predict photo-$z$ from fluxes and images, respectively. We also propose to use an efficient hybrid network, which combines MLP and CNN, by employing transfer learning techniques to investigate the improvement of the result with both flux and image data included. We find that the photo-$z$ accuracy and outlier fraction can achieve ${\sigma }_{\mathrm{NMAD}}=0.023$ and $\eta =1.43\%$ for the MLP using flux data only, and ${\sigma }_{\mathrm{NMAD}}=0.025$ and $\eta =1.21\%$ for the CNN using image data only. The result can be further improved in high efficiency as ${\sigma }_{\mathrm{NMAD}}=0.020$ and $\eta =0.90\%$ for the hybrid transfer network. These approaches result in similar galaxy median and mean redshifts ~0.8 and 0.9, respectively, for the redshift range from 0 to 4. This indicates that our networks can effectively and properly extract photo-$z$ information from the CSST galaxy flux and image data.

arXiv comment11 pages, 8 figures, 2 tables. Accepted for publication in MNRAS

References in corpus (34)

• Stellar population synthesis at the resolution of 2003
• The Sloan Digital Sky Survey: Technical Summary
• LSST: from Science Drivers to Reference Design and Anticipated Data Products
• EAZY: A Fast, Public Photometric Redshift Code
• Accurate photometric redshifts for the CFHT Legacy Survey calibrated using the VIMOS VLT Deep Survey
• The COSMOS2015 Catalog: Exploring the 1<z<6 Universe with half a million galaxies
• The Dark Energy Survey: more than dark energy - an overview
• Measuring and Modelling the Redshift Evolution of Clustering: the Hubble Deep Field North
• The COSMOS Survey: Hubble Space Telescope / Advanced Camera for Surveys (HST/ACS) Observations and Data Processing
• ANNz: estimating photometric redshifts using artificial neural networks
• COSMOS2020: A panchromatic view of the Universe to $z\sim 10$ from two complementary catalogs
• Weak Gravitational Lensing with COSMOS: Galaxy Selection and Shape Measurements
• A new catalog of photometric redshifts in the Hubble Deep Field
• The Dark Energy Survey Data Release 2
• 4MOST: Project overview and information for the First Call for Proposals
• Cosmology from the Chinese Space Station Optical Survey (CSS-OS)
• Effect of Photometric Redshift Uncertainties on Weak Lensing Tomography
• Star-forming galaxies at very high redshifts
• Mapping the Galaxy Color-Redshift Relation: Optimal Photometric Redshift Calibration Strategies for Cosmology Surveys
• ANNz2 - photometric redshift and probability distribution function estimation using machine learning
• Photometric redshifts from SDSS images using a Convolutional Neural Network
• Precision photometric redshift calibration for galaxy-galaxy weak lensing
• Estimating Photometric Redshifts Using Support Vector Machines
• Photo-z for weak lensing tomography from space: the role of optical and near-IR photometry
• Testing photometric redshift measurements with filter definition of the Chinese Space Station Optical Survey (CSS-OS)
• A General Study of the Influence of Catastrophic Photometric Redshift Errors on Cosmology with Cosmic Shear Tomography
• Perfecting the Photometric Calibration of the ACS CCD Cameras
• Photometric Redshifts for the Dark Energy Survey and VISTA and Implications for Large Scale Structure
• Morpho-z: improving photometric redshifts with galaxy morphology
• Investigating Deep Learning Methods for Obtaining Photometric Redshift Estimations from Images
• Spectroscopic and Photometric Redshift Estimation by Neural Networks For the China Space Station Optical Survey (CSS-OS)
• Exploring Large-scale Structure with Billions of Galaxies
• Photometric redshifts with machine learning, lights and shadows on a complex data science use case
• Photometric Redshift Estimation with Galaxy Morphology using Self-Organizing Maps

Cited by in corpus (10)

• Synergy between CSST galaxy survey and gravitational-wave observation: Inferring the Hubble constant from dark standard sirens
• Cosmological Constraint Precision of the Photometric and Spectroscopic Multi-probe Surveys of China Space Station Telescope (CSST)
• Photometric redshift estimates using Bayesian neural networks in the CSST survey
• Galaxy morphoto-Z with neural Networks (GaZNets). I. Optimized accuracy and outlier fraction from Imaging and Photometry
• Future Cosmology: New Physics and Opportunity from the China Space Station Telescope (CSST)
• Improving the Cosmological Constraints by Inferring the Formation Channel of Extreme-mass-ratio Inspirals
• Estimating Photometric Redshifts for Galaxies from the DESI Legacy Imaging Surveys with Bayesian Neural Networks Trained by DESI EDR
• Accurately Estimating Redshifts from CSST Slitless Spectroscopic Survey using Deep Learning
• CLAP. I. Resolving miscalibration for deep learning-based galaxy photometric redshift estimation
• GalaxyGenius: Mock galaxy image generator for various telescopes from hydrodynamical simulations