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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.

Location & approach

The campus of the Technical University of Dortmund is located near the freeway junction Dortmund West, where the Sauerland line A45 crosses the Ruhr expressway B1/A40. The Dortmund-Eichlinghofen exit on the A45 leads to the South Campus, the Dortmund-Dorstfeld exit on the A40 leads to the North Campus. The university is signposted at both exits.

The "Dortmund Universität" S-Bahn station is located directly on the North Campus. From there, the S-Bahn line S1 runs every 20 or 30 minutes to Dortmund main station and in the opposite direction to Düsseldorf main station via Bochum, Essen and Duisburg. In addition, the university can be reached by bus lines 445, 447 and 462. Timetable information can be found on the homepage of the Rhine-Ruhr transport association, and DSW21 also offer an interactive route network map.

One of the landmarks of the TU Dortmund is the H-Bahn. Line 1 runs every 10 minutes between Dortmund Eichlinghofen and the Technology Center via Campus South and Dortmund University S, while Line 2 commutes every 5 minutes between Campus North and Campus South. It covers this distance in two minutes.