Template-Fitting Meets Deep Learning: Redshift Estimation Using Physics-Guided Neural Networks
Journal:
arXiv
Published Date:
Jul 1, 2025
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.
