Photometric Redshift Estimation Using Scaled Ensemble Learning

• URL: http://arxiv.org/abs/2601.07292v1
• Date: Mon, 12 Jan 2026 07:55:24 GMT
• Title: Photometric Redshift Estimation Using Scaled Ensemble Learning
• Authors: Swagata Biswas, Shubhrangshu Ghosh, Avyarthana Ghosh, Yogesh Wadadekar, Abhishek Roy Choudhury, Arijit Mukherjee, Shailesh Deshpande, Arpan Pal,
• Abstract summary: This study presents a new ensemble-based ML framework aimed at predicting Pz for faint galaxies and higher redshift ranges.<n>By using bagged input data, the ensemble approach delivers improved predictive performance compared to stand-alone models.<n>Our results show marked improvements in the precision and reliability of Pz estimation.
• Score: 4.575096688254749
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The development of the state-of-the-art telescopic systems capable of performing expansive sky surveys such as the Sloan Digital Sky Survey, Euclid, and the Rubin Observatory's Legacy Survey of Space and Time (LSST) has significantly advanced efforts to refine cosmological models. These advances offer deeper insight into persistent challenges in astrophysics and our understanding of the Universe's evolution. A critical component of this progress is the reliable estimation of photometric redshifts (Pz). To improve the precision and efficiency of such estimations, the application of machine learning (ML) techniques to large-scale astronomical datasets has become essential. This study presents a new ensemble-based ML framework aimed at predicting Pz for faint galaxies and higher redshift ranges, relying solely on optical (grizy) photometric data. The proposed architecture integrates several learning algorithms, including gradient boosting machine, extreme gradient boosting, k-nearest neighbors, and artificial neural networks, within a scaled ensemble structure. By using bagged input data, the ensemble approach delivers improved predictive performance compared to stand-alone models. The framework demonstrates consistent accuracy in estimating redshifts, maintaining strong performance up to z ~ 4. The model is validated using publicly available data from the Hyper Suprime-Cam Strategic Survey Program by the Subaru Telescope. Our results show marked improvements in the precision and reliability of Pz estimation. Furthermore, this approach closely adheres to-and in certain instances exceeds-the benchmarks specified in the LSST Science Requirements Document. Evaluation metrics include catastrophic outlier, bias, and rms.

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