A Fluid-Dynamic-Inspired Computational Framework for Morphological and Flux-Based Analysis of Galactic Systems Using HST Archival Data
Abstract
This paper presents a revised and scientifically cautious formulation of the Fluid-dynamic Morphological Model (FMM), an exploratory computational framework for flux-based analysis of galactic systems using Hubble Space Telescope archival data. The method focuses on raw or minimally processed .flt images retrieved from MAST and extracts reproducible image-level descriptors, including mean flux, peak flux, integrated flux, radial intensity profiles, residual components, nuclear concentration indices, and VDR-like activity indicators. Rather than replacing standard gravitational, photometric, spectroscopic, or cosmological interpretations, FMM is presented as a complementary image-analysis approach inspired by fluid-dynamic terminology. In this formulation, galaxy morphology and nuclear activity are examined through spatial variations in flux concentration, radial structure, and residual asymmetry, with special attention to central regions and wake-like patterns. The framework is demonstrated through a representative HST FITS dataset and compared qualitatively with standard astronomical diagnostics. The results suggest that flux-derived indicators may help organize archival galaxy images, identify regions of morphological asymmetry or enhanced activity, and select targets for deeper astrophysical modeling. The paper defines the model assumptions, diagnostic quantities, processing workflow, limitations, and reproducibility requirements. The revised interpretation emphasizes uncertainty, dimensional consistency, and compatibility with established astrophysical methods, while preserving the author’s original aim of developing an accessible and automated pipeline for exploratory analysis of galactic systems.