Template-Fitting Meets Deep Learning: Redshift Estimation Using Physics-Guided Neural Networks

[2507.00866] Template-Fitting Meets Deep Learning: Redshift Estimation Using Physics-Guided Neural Networks Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Astrophysics > Instrumentation and Methods for Astrophysics arXiv:2507.00866 (astro-ph) [Submitted on 1 Jul 2025] Title: Template-Fitting Meets Deep Learning: Redshift Estimation Using Physics-Guided Neural Networks Authors: Jonas Chris Ferrao , Dickson Dias , Pranav Naik , Glory D'Cruz , Anish Naik , Siya Khandeparkar , Manisha Gokuldas Fal Dessai View a PDF of the paper titled Template-Fitting Meets Deep Learning: Redshift Estimation Using Physics-Guided Neural Networks, by Jonas Chris Ferrao and 6 other authors View PDF HTML (experimental) Abstract: Accurate photometric redshift estimation is critical for observational cosmology, especially in large-scale surveys where spectroscopic measurements are impractical. Traditional approaches include template fitting and machine learning, each with distinct strengths and limitations. We present a hybrid method that integrates template fitting with deep learning using physics-guided neural networks. By embedding spectral energy distribution templates into the network architecture, our model encodes physical priors into the training process. The system employs a multimodal design, incorporating cross-attention mechanisms to fuse photometric and image data, along with Bayesian layers for uncertainty estimation. We evaluate our model on the publicly available PREML dataset, which includes approximately 400,000 galaxies from the Hyper Suprime-Cam PDR3 release, with 5-band photometry, multi-band imaging, and spectroscopic redshifts. Our approach achieves an RMS error of 0.0507, a 3-sigma catastrophic outlier rate of 0.13%, and a bias of 0.0028. The model satisfies two of the three LSST photometric redshift requirements for redshifts below 3. These results highlight the potential of combining physically motivated templates with data-driven models for robust redshift estimation in upcoming cosmological surveys. Subjects: Instrumentation and Methods for Astrophysics (astro-ph.IM) ; Machine Learning (cs.LG) Cite as: arXiv:2507.00866 [astro-ph.IM] (or arXiv:2507.00866v1 [astro-ph.IM] for this version) https://doi.org/10.48550/arXiv.2507.00866 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1007/s12036-026-10144-5 Focus to learn more DOI(s) linking to related resources Submission history From: Jonas Ferrao [ view email ] [v1] Tue, 1 Jul 2025 15:29:45 UTC (2,566 KB) Full-text links: Access Paper: View a PDF of the paper titled Template-Fitting Meets Deep Learning: Redshift Estimation Using Physics-Guided Neural Networks, by Jonas Chris Ferrao and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: astro-ph.IM < prev | next > new | recent | 2025-07 Change to browse by: astro-ph cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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