用轻量矩阵分解实现光谱数据实时分析，精度超90%。
GAMMA_FLOW: Guided Analysis of Multi-label spectra by MAtrix Factorization for Lightweight Operational Workflows
- 基于监督非负矩阵分解降维，避免大模型计算开销。
- 分类准确率超90%，支持单/多组分光谱分析。
- 开源灵活，适用于伽马射线及其他一维光谱数据。
GAMMA_FLOW 是一个用于实时光谱数据分析的开源 Python 工具包，支持单组分与多组分光谱的分类、去噪、分解及异常检测。它不依赖大型计算密集型模型，而是采用监督式非负矩阵分解（NMF）进行降维，确保分析快速、高效且可适应，同时显著降低计算成本。该工具在伽马射线光谱分析中表现优异，分类准确率超过 90%，可实现可靠的自动化光谱解读。其设计初衷虽面向伽马射线数据，但同样适用于任何一维光谱数据。作为开源且灵活的替代方案，GAMMA_FLOW 可广泛应用于科研与工业领域。
原文摘要 · Abstract (English)
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.
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