Accurate and computationally efficient photometric redshift estimation is essential for cosmological analyses and studies of the large-scale structure of the Universe. However, applying existing template-fitting and machine learning methods to rapidly growing survey data sets can involve trade-offs between predictive p...
Yu-Chen He, Jing-Jing Wu, Bin Jiang et al.· Publications of the Astronom...· 0 citations
Identifying unresolved binary stars from broadband photometry is important for studies of stellar evolution, binary populations, and Galactic structure, but remains challenging because many single and binary systems occupy overlapping regions of photometric feature space. We use mock seven-band Chinese Space Station Te...
A novel white-box approach to photometric data classification using Symbolic Regression, which enables not only efficient classification but also the discovery of new astrophysical insights, offering a clearer path toward understanding the underlying physical processes of celestial phenomena.
Yu-Chen He, Yanhao Cui, Jingjing Wu et al.· Publications of the Astronom...· 0 citations
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