2025-06-16 –, Palais Atelier
gamma_flow is an open-source Python package for real-time spectral data analysis. Designed for speed and efficiency, it avoids large models, opting instead for a novel supervised dimensionality reduction approach. This enables seamless denoising, classification, and disentangling of single-label or multi-label spectra.
In many research fields, spectral measurements help to assess material properties. In this context, an area of interest for many researchers is the classification (automated labelling) of the measured spectra. Additionally, there may be a need to decompound multi-label spectra (linear combinations of different substances) and identify their constituents.
As proprietary spectral analysis software are often limited in their functionality and adaptability, a Python package was developed and will be presented in this talk.
gamma_flow (Guided Analysis of Multi-label spectra by Matrix Factorization for Lightweight Operational Workflows) includes the
- classification of test spectra to predict their constituents
- denoising of test spectra for better recognizability
- outlier detection to evaluate the model's applicability to test spectra
It is based on a dimensionality reduction model that constitutes a novel, supervised approach to non-negative matrix factorization (NMF). Hence, it exploits and adapts conventional data science methods rather than using extensive, energy-intensive models like neural networks. This results in a fast, robust and reliable automated analysis, leading to classification accuracies above 90%.
Data Science, Stories
Level:Intermediate
Viola Rädle works at the interface between environmental and data science. She discovered her interest in environmental dynamics while studying physics at the University of Heidelberg. In her master's thesis, she researched groundwater systems and later deepened this topic through Bayesian data analysis. She expanded her Python skills as a junior researcher at HTWK Leipzig, where she worked on asphalt recycling and alternative methods of hydrogen production. Since 2023, she has been working as a data scientist at the Federal Environment Agency's AI Lab, where she supports authorities in the field of digitalization and data analysis. In addition to developing prototypes, where she is responsible for programming, project organization and science communication, she gives exciting and accessible lectures in the field of artificial intelligence.
Raphael Franke is a Data Scientist at the Application Lab for Artificial Intelligence and Big Data at the German Environment Agency. With an academic background in mathematical statistics and data analysis he specializes in applying AI to real-world environmental challenges. His interests lie in probabilistic time series forecasting and leveraging data-driven insights for sustainable impact.