English

GAMMA_FLOW: Guided Analysis of Multi-label spectra by MAtrix Factorization for Lightweight Operational Workflows

Machine Learning 2025-11-13 v1 Data Analysis, Statistics and Probability

Abstract

GAMMA_FLOW is an open-source Python package for real-time analysis of spectral data. It supports classification, denoising, decomposition, and outlier detection of both single- and multi-component spectra. Instead of relying on large, computationally intensive models, it employs a supervised approach to non-negative matrix factorization (NMF) for dimensionality reduction. This ensures a fast, efficient, and adaptable analysis while reducing computational costs. gamma_flow achieves classification accuracies above 90% and enables reliable automated spectral interpretation. Originally developed for gamma-ray spectra, it is applicable to any type of one-dimensional spectral data. As an open and flexible alternative to proprietary software, it supports various applications in research and industry.

Keywords

Cite

@article{arxiv.2511.09326,
  title  = {GAMMA_FLOW: Guided Analysis of Multi-label spectra by MAtrix Factorization for Lightweight Operational Workflows},
  author = {Viola Rädle and Tilman Hartwig and Benjamin Oesen and Emily Alice Kröger and Julius Vogt and Eike Gericke and Martin Baron},
  journal= {arXiv preprint arXiv:2511.09326},
  year   = {2025}
}
R2 v1 2026-07-01T07:33:56.592Z