Enhancing RAW images captured under low light conditions is a challenging task. Recent deep learning based RAW enhancement methods have shifted from using real paired data to relying on synthetic datasets. These synthetic datasets are typically generated by physically modeling sensor noise, but existing approaches often consider only additive noise, ignore multiplicative components, and rely on global calibration that overlooks pixel level manufacturing variations. As a result, such methods struggle to accurately reproduce real sensor noise. To address these limitations, this paper derives a noise model from the physical noise generation mechanisms that occur under low illumination and proposes a novel composite model that integrates both additive and multiplicative noise. To solve the model, we introduce a physics based per pixel noise simulation and calibration scheme that estimates and synthesizes noise for each individual pixel, thereby overcoming the restrictions of traditional global calibration and capturing spatial noise variations induced by microscopic CMOS manufacturing differences. Motivated by the strong performance of rectified flow methods in image generation and processing, we further combine the physics-based noise synthesis with a rectified flow generative framework and present PGRF a physics-guided rectified flow framework for low light image enhancement. PGRF leverages the ability of rectified flows to model complex data distributions and uses physical guidance to steer the generation toward the desired clean image. To validate the effectiveness of the proposed model, we established the LLID dataset, an indoor low light benchmark captured with the Sony A7S II camera. Experimental results demonstrate that the proposed framework achieves significant improvements in low light RAW image enhancement.