AI 中文总结
该研究针对数字化天文底片光度定标的耦合系统误差问题，提出MFF-Net深度学习框架，在1200张中国底片测试中优于MYX25方法，提升测光精度并消除星等-位置耦合。
AI 中文摘要
数字化天文底片的光度定标通常被建模为与星等、颜色和位置相关的可分项，但当天区图像质量随星等变化时，这种可分性会失效，导致残差中出现耦合的空间系统误差。我们提出一种深度学习定标框架——多特征融合网络（Multi-Feature Fused Network, MFF-Net），该网络以仪器星等、颜色和像素坐标为输入，学习单一非线性校正项以联合捕捉它们的耦合依赖关系。对1200张数字化中国底片的测试表明，MFF-Net的表现始终优于MYX25方法（Ma et al. 2025），将5%~95%分位精度从0.11~0.26星等提升至0.08~0.18星等，且对亮源实现了约两倍的增益。学习到的校正项大幅消除了定标后残差图中可见的星等-位置耦合，从而实现更高精度的底片测光，并让大型历史底片档案的使用更可靠。
英文摘要
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.
CommentsThis manuscript has been accepted by The Astrophysical Journal Supplement Series (ApJS)
