Photometric Redshift Estimation Using Scaled Ensemble Learning

2025-12-31

Astrophysical Journal

AI Summary

This study addresses the limited accuracy of photometric redshift estimation for high-redshift galaxies (z ∼ 4) when using only optical-band (grizy) photometry. To overcome this challenge, the authors propose a scaled ensemble learning framework that integrates gradient boosting machines, XGBoost, k-nearest neighbors, and artificial neural networks, enhanced with bagging to improve model robustness. Evaluated on the publicly available Subaru HSC-SSP dataset, the method significantly reduces the catastrophic outlier rate, bias, and root-mean-square error. Remarkably, under optical-only conditions, it meets or even exceeds the baseline requirements set by the LSST science benchmarks, thereby providing a highly accurate and reliable tool for photometric redshift estimation in high-redshift galaxy studies.

Technology Category

• Machine Learning: Ensemble Methods
• Intelligent Robots: State Estimation
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Photometric Redshift
• Machine Learning
• Ensemble Learning
• High-redshift Galaxies
• Optical Photometry

Innovation

• photometric redshift
• ensemble learning
• machine learning
• scaled ensemble
• galaxy redshift estimation