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Fakultät Physik

Method Development

Machine learning and automatic recognition of inherent patterns play an important role in experimental astroparticle physics, as in many other areas of daily life.

Interpretaton of an image of the campus by a neuronal network © Richard Wiemann ​/​ TU Dortmund

Only with the help of suitable learning algorithms can the large amounts of data (Big Data) be efficiently analyzed for cosmic messenger particles such as high-energy photons or neutrinos and these selected in a suitable manner from the overwhelming background of atmospheric muons.

To this end, the learning algorithms must not only be properly trained, but their performance must also be suitably validated. The work on automated selection of astrophysical messenger particles is carried out, among others, in the framework of subproject C3 of the Collaborative Research Center SFB 876. The areas of work in the field of machine learning include, among others, the following subprojects:

    Estimation of primary particle properties using machine learning, especially ensemble methods and deep learning.
    Spectral reconstruction using learning algorithms
    Efficiency improvement of air shower simulations by machine learning

Visit Sonderforschungsbereich SFB 876 for more information on deconvolution algorithms and inverse problems in astroparticle physics!

You can also learn more about end-to-end analysis with machine learning in this article.

Deconvolution projects developed at E5b

Project-specific deconvolution algorithms have been developed at E5b, some of which are publicly available.


The Dortmund Spectrum Estimation Algorithm is a method for the reconstruction of spectra in which the deconvolution is conceived as a multinomial classification problem.


Funfolding has been developed specifically for deconvolution of IceCube data. Funfolding is a Python library in which several algorithms are implemented, among others the Blob/Run Likelihood Unfolding Algorithm. Main features are "likelihood-based unfolding techniques and decision tree-based binning". You can find this project at GitHub.


TRUEE (Time-dependent Regularized Unfolding for Economics and Engineering problems) is a new software package for numerical solution of inverse problems (unfolding). The algorithm is based on the application RUN (Regularized UNfolding) written in FORTRAN 77. The deconvolution algorithm was used in the analysis in experiments of particle and astroparticle physics and was characterized by particularly stable results and reliable uncertainties. Nowadays, besides FORTRAN, the C++ programming language is widely used in analysis programs in various research areas. Therefore, the C++ deconvolution program TRUEE was developed, which contains the RUN algorithm and additional extensions, which allow a comfortable and user-friendly application. The results of TRUEE and RUN are identical.