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arXiv · 2608.01391

Towards optimal photometric calibration of digital astronomical plates with deep learning

Mingyang Ma·Haibo Yuan·Lin Yang·Kai Xiao·Bowen Huang·Shiyin Shen·Zhengjun Shang·Yong Yu·Meiting Yang·Zhenghong Tang·Jianhai Zhao
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Abstract

Photometric calibration of digitized photographic plates is commonly modeled with separable magnitude-, color-, and position-dependent terms, but this separability can break down when image quality varies across the field in a magnitude-dependent way, leaving coupled spatial systematics in the residuals. We introduce a deep-learning calibration framework, the Multi-Feature Fused Network (MFF-Net), which takes instrumental magnitude, color, and pixel coordinates as input and learns a single nonlinear correction that jointly captures their coupled dependencies. Tests on 1{,}200 digitized Chinese plates show that MFF-Net consistently outperforms the MYX25 method (Ma et al. 2025), improving the 5th--95th percentile precision from 0.11--0.26~mag to 0.08--0.18~mag and delivering an approximately factor-of-two gain for bright sources. The learned correction largely removes the magnitude--position coupling seen in post-calibration residual maps, enabling higher-precision plate photometry and more reliable use of large historical plate archives.

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Mingyang Ma, Haibo Yuan, Lin Yang, Kai Xiao, Bowen Huang, Shiyin Shen, Zhengjun Shang, Yong Yu, Meiting Yang, Zhenghong Tang, Jianhai Zhao. 2026-08-02. Towards optimal photometric calibration of digital astronomical plates with deep learning. https://arxiv.org/abs/2608.01391

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