Toward Optimal Photometric Calibration of Digital Astronomical Plates with Deep Learning

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 1200 digitized Chinese plates show that MFF-Net consistently outperforms the MYX25 method, improving the 5th–95th percentile precision from 0.11–0.26 mag to 0.08–0.18 mag and delivering an approximately factor-of-2 gain for bright sources. The learned correction largely removes the magnitude–position coupling seen in postcalibration residual maps, enabling higher-precision plate photometry and more reliable use of large historical plate archives.