Towards optimal photometric calibration of digital astronomical plates with deep learning

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.