GAMMA_FLOW: Guided Analysis of Multi-label spectra by MAtrix Factorization for Lightweight Operational Workflows
It provides a fast and adaptable alternative to proprietary software for researchers and industry professionals working with spectral data, though it is incremental as it builds on existing matrix factorization methods.
The paper tackles the problem of real-time analysis of spectral data by developing GAMMA_FLOW, an open-source Python package that uses supervised non-negative matrix factorization for dimensionality reduction, achieving classification accuracies above 90% and reducing computational costs.
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.