Prof. Christian Glaser is awarded the 2026 Wallmark Prize

Prof. Christian Glaser, a professor in our department, has been awarded the 2026 Wallmark Prize by the Royal Swedish Academy of Sciences. The prize has been awarded since 1859 to scientists whose discoveries or inventions have significantly advanced science and industry, particularly in the fields of mathematics, astronomy, applied mechanics, physics, chemistry, mineralogy, or engineering. It is endowed with 435,000 Swedish kronor, equivalent to more than 40,000 euros, and is named after the Swedish physicist Lars Johan Wallmark.

Glaser began the work for which he has now received the prize as a junior professor at Uppsala University in Sweden, later becoming an associate professor. He continues this research at TU Dortmund University, where he is Professor of Experimental Astroparticle Physics at our department, and at the Lamarr Institute for Machine Learning and Artificial Intelligence. His research focuses on high-energy cosmic neutrinos. His research group is involved in several major astroparticle physics experiments, including the IceCube Neutrino Observatory and the ARIANNA experiment in Antarctica, the Radio Neutrino Observatory in Greenland, and the Pierre Auger Observatory in Argentina. The Wallmark Prize particularly recognizes Glaser’s contributions to the use of machine learning in the radio detection of ultra-high-energy neutrinos to improve real-time analysis of data from detectors installed in ice.

Neutrinos interact only very rarely with matter, which makes them valuable messenger particles. Unlike charged cosmic rays, they are not deflected by magnetic fields on their way through the universe, and they can escape from dense astrophysical environments from which even light can barely escape. However, due to these properties, neutrinos are also difficult to detect. Researchers need large detector volumes and often use natural detector media, such as the ice in Antarctica. When a high-energy neutrino interacts with the detector medium, it can produce secondary particles, which can generate detectable signals by generating visible light or by emitting radio signals.

In optical detection, light is emitted due to the Cherenkov effect when a particle travels faster than the phase velocity of light in the detector medium. This light can be measured by optical sensors, such as photomultiplier tubes, which are installed throughout the detector volume. An experiment using this detection principle is the IceCube Observatory. Its sensors are embedded deep in the Antarctic ice, which serves as a natural detector medium. Currently, the IceCube observatory is being upgraded to enhance its detection rate and measurement precision. Part of this upgrade is to enable the detection of radio signals. Radio detectors are being installed close to the surface over an area of 500 square kilometers. They will improve the sensitivity of the detector at the highest energies by two orders of magnitude.

Radio detection is based on the Askaryan effect and is especially important for the most energetic neutrinos. When an ultra-high-energy particle cascade crosses a dense dielectric medium such as ice, salt, or sand, the cascade can develop a small charge asymmetry, leading to the emission of a short radio pulse. At wavelengths larger than the size of the cascade, the radio emission becomes coherent, creating a detectable signal. Radio detection is the central detection principle of the Radio Neutrino Observatory (RNO-G) in Greenland. Like IceCube, it uses a large volume of ice as a detector medium. However, while IceCube primarily measures visible Cherenkov light with optical sensors, RNO-G is designed to use only the radio detection principle.

Glaser’s research group contributes to the development of radio-detection techniques for these experiments. In this context, the machine learning methods developed by Glaser and his group play an important role because they can help analyze the large amounts of data recorded by radio detector systems in real time, filtering relevant signals from background events, identifying rare astrophysical events, and improving the sensitivity of neutrino detectors.

With his research on machine-learning methods for radio detection, Prof. Christian Glaser is helping to open new ways of observing ultra-high-energy neutrinos. We warmly congratulate him on receiving the 2026 Wallmark Prize.