Wolfgang Rhode speaks about IceCube and machine learning

Prof. Dr. Dr. Wolfgang Rhode was a guest on the platform “Lernende Systeme: Germany’s Platform for Artificial Intelligence,” where he spoke about the IceCube Neutrino Observatory, its construction in the Antarctic ice, and the role of machine learning in analyzing the data.

The IceCube experiment is one of the largest experiments in astroparticle physics and is located deep in the Antarctic ice. There, the detector measures high energy neutrinos, nearly massless elementary particles that interact with matter only very rarely and can therefore travel vast distances through the universe without being strongly deflected or absorbed. Measuring them can provide clues about the astrophysical objects in which they were produced.

To build IceCube, holes up to around 2.5 kilometers deep were melted into the Antarctic ice. Long cables carrying light sensors, each about the size of a basketball, were then lowered into them. In total, the detector comprises more than 80 such boreholes, with thousands of sensors installed in the ice. Deep below the surface, the ice is completely dark, creating ideal conditions for measuring the faint light signals produced when neutrinos interact in the ice or in the rock beneath it.

One of the central questions IceCube addresses is the origin of cosmic rays, which were discovered more than 100 years ago. To this day, it is not fully understood where and through which astrophysical processes the high-energy particles are produced and accelerated. Neutrinos can provide important clues if they can be associated with possible sources such as active galactic nuclei, allowing researchers to draw conclusions about the physical conditions in these objects.

Rhode has contributed to research connected with IceCube for 37 years. While the early years relied on rather simple models for data analysis, machine learning methods are now used. They help researchers evaluate the complex data and reliably distinguish astrophysically interesting signals from the vast number of background events. For every relevant signal, there are around 10⁸ background events. The events classified as relevant are then analyzed in more detail. Researchers reconstruct, among other things, the energy and direction of origin of the detected particle and determine what type of particle it is.

At the beginning of 2026, new sensors were installed in the ice to extend IceCube’s measurement range to even higher energies. At such high energies, the events of interest are especially rare, making a larger detector volume necessary.

The interview is part of the work of Plattform Lernende Systeme, which brings together people from science, industry, and society to inform people about the responsible use of learning systems and encourage exchange on the topic. The platform was founded in 2017 by the German Federal Ministry of Education and Research and acatech. Experts discuss the opportunities these systems offer and the challenges associated with them. It aims to explain clearly the role that learning systems can play in everyday life, research, and industry, while also providing guidance on their responsible use.

We thank Plattform Lernende Systeme for the opportunity to provide insights into research on IceCube and the use of machine learning in astroparticle physics.