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

TL;DR

GAMMA_FLOW is an open-source Python tool that uses supervised non-negative matrix factorization for fast, accurate, and adaptable real-time analysis of spectral data, including classification and outlier detection.

Contribution

It introduces a lightweight, supervised NMF-based approach for spectral analysis that is efficient, accurate, and applicable across various spectral data types.

Findings

Achieves over 90% classification accuracy

Supports real-time spectral analysis

Reduces computational costs significantly

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.