{"ID":2842840,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.09326","arxiv_id":"2511.09326","title":"GAMMA_FLOW: Guided Analysis of Multi-label spectra by MAtrix Factorization for Lightweight Operational Workflows","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.","short_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 ma...","url_abs":"https://arxiv.org/abs/2511.09326","url_pdf":"https://arxiv.org/pdf/2511.09326v1","authors":"[\"Viola Rädle\",\"Tilman Hartwig\",\"Benjamin Oesen\",\"Emily Alice Kröger\",\"Julius Vogt\",\"Eike Gericke\",\"Martin Baron\"]","published":"2025-11-12T13:37:00Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"physics.data-an\"]","methods":"[]","has_code":false}