Template-Fitting Meets Deep Learning: Redshift Estimation Using Physics-Guided Neural Networks
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915935502204928 |
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| author | Ferrao, Jonas Chris Dias, Dickson Naik, Pranav D'Cruz, Glory Naik, Anish Khandeparkar, Siya Dessai, Manisha Gokuldas Fal |
| author_facet | Ferrao, Jonas Chris Dias, Dickson Naik, Pranav D'Cruz, Glory Naik, Anish Khandeparkar, Siya Dessai, Manisha Gokuldas Fal |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_00866 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Template-Fitting Meets Deep Learning: Redshift Estimation Using Physics-Guided Neural Networks Ferrao, Jonas Chris Dias, Dickson Naik, Pranav D'Cruz, Glory Naik, Anish Khandeparkar, Siya Dessai, Manisha Gokuldas Fal Instrumentation and Methods for Astrophysics Machine Learning 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. |
| title | Template-Fitting Meets Deep Learning: Redshift Estimation Using Physics-Guided Neural Networks |
| topic | Instrumentation and Methods for Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2507.00866 |