Kavli Affiliate: Xuebing Wu
| First 5 Authors: Shirui Wei, Changhua Li, Yanxia Zhang, Chenzhou Cui, Chao Tang
| Summary:
Emission Line Galaxies (ELGs) are crucial for cosmological studies,
particularly in understanding the large-scale structure of the Universe and the
role of dark energy. ELGs form an essential component of the target catalogue
for the Dark Energy Spectroscopic Instrument (DESI), a major astronomical
survey. However, the accurate selection of ELGs for such surveys is challenging
due to the inherent uncertainties in determining their redshifts with
photometric data. In order to improve the accuracy of photometric redshift
estimation for ELGs, we propose a novel approach CNN-MLP that combines
Convolutional Neural Networks (CNNs) with Multilayer Perceptrons (MLPs). This
approach integrates both images and photometric data derived from the DESI
Legacy Imaging Surveys Data Release 10. By leveraging the complementary
strengths of CNNs (for image data processing) and MLPs (for photometric feature
integration), the CNN-MLP model achieves a $sigma_{mathrm{NMAD}}$ (normalised
median absolute deviation) of 0.0140 and an outlier fraction of 2.57%. Compared
to other models, CNN-MLP demonstrates a significant improvement in the accuracy
of ELG photometric redshift estimation, which directly benefits the target
selection process for DESI. In addition, we explore the photometric redshifts
of different galaxy types (Starforming, Starburst, AGN, Broadline).
Furthermore, this approach will contribute to more reliable photometric
redshift estimation in ongoing and future large-scale sky surveys (e.g. LSST,
CSST, Euclid), enhancing the overall efficiency of cosmological research and
galaxy surveys.
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