A Production-Grade Scalable Photometric Pipeline for Asteroid (4) Vesta Using NASA Dawn Framing Camera Data: Disk-Resolved and Disk-Integrated Hapke Parameter Retrieval Across the Approach Data phase.

We present a reproducible, open-source photometric analysis  pipeline for asteroid (4) Vesta developed using the complete  NASA Dawn Framing Camera 2 dataset spanning four mission  phases of  Rotational Characterization (RC), Survey, High Altitude  Mapping Orbit (HAMO), and Low Altitude Mapping Orbit (LAMO) comprising  a vast photometric geometry observations. The pipeline integrates  SPICE-based ray tracing against a Vesta ellipsoid surface  model, per-pixel radiometric calibration to dimensionless  I/F reflectance, and a DuckDB-powered columnar data lake  architecture. We implement and validate a hierarchical photometric model  sequence following Li et al. (2013) and Schröder et al.  (2013). Disk-resolved Minnaert analysis using pixel-level  RC approach-phase data yields limb-darkening parameters  k₀ and the phase-dependent albedo A consistent  with published Vesta values within measurement uncertainty.  For disk-resolved Hapke model fitting is done to combined Survey  and RC phase curve data covering phase angles from  approximately 8 to 80 degrees yields physical scattering  parameters including single scattering albedo,  Henyey-Greenstein asymmetry, opposition surge amplitude  and width, and macroscopic roughness. Bayesian MCMC  sampling using emcee provides full posterior distributions  and parameter correlations, enabling rigorous comparison  to published Vesta photometry and detection of  phase-dependent residuals that motivate physics-informed  neural network (PINNs) acceleration of parameter retrieval. The robust pipeline is designed as a general planetary photometry  framework with a configurable body and instrument layer,  enabling direct adaptation to the upcoming NASA Psyche  mission and to other airless bodies including Ceres and  icy moons. All code, data provenance records, and  reproducibility artifacts are archived in Github.