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A potential site for a Swiss deep underground laboratory is currently being explored in the Bedretto Tunnel beneath the Gotthard massif in the canton of Ticino. Located 3.5 km from the tunnel entrance, the site lies beneath up to 1500 metres of rock. This natural shielding makes it an exceptional location for experiments that require an environment almost entirely free from interference caused by cosmic radiation.

The results of a comprehensive campaign to characterize the underground environment in the Bedretto tunnel have now beenpublished in European Physics Journal C. The study, which was carried out in close collaboration with the BedrettoLab group at the ETH Zürich, confirms the site's remarkable potential. In particular, the measured flux of cosmic muons is found to be reduced by a factor of one million inside the cavern. This would make the Bedretto Tunnel the second deepest underground laboratory site in Europe.

Transformer-based models outperform a feature-based baseline for pulse-shape discrimination in Majorana Demonstrator HPGe waveforms, with masked-autoencoder pre-training reducing the need for labelled data by factors of 2-4 in low-label regimes. Marta Babicz and collaborators published the study in Machine Learning: Science and Technology, showing how modern sequence-learning methods can use the full information contained in digitised detector waveforms.

Pulse-shape discrimination is a key tool in rare-event searches such as neutrinoless double-beta decay, where HPGe detectors must distinguish candidate signal-like events from backgrounds. Instead of compressing each waveform into a few hand-crafted summary quantities, the study trains detector-conditioned transformer models directly on charge traces and their gradients from the Majorana Demonstrator AI/ML data release. The transformers improve classification performance across standard PSD selections, especially for the more challenging labels and for the combined PSD-pass definition. The work also shows that self-supervised masked-autoencoder pre-training on unlabelled calibration data can make the models substantially more sample-efficient, an encouraging result for future applications in LEGEND-200, LEGEND-1000, and other HPGe-based low-background experiments.

You can read the paper in Machine Learning: Science and Technology, and the preprint is available as arXiv:2603.06192.

Congratulations to Dr. Andrea Paloma Cimental Chávez, who successfully defended her Ph.D. thesis this May!

During her thesis, Paloma worked on the XENONnT experiment at LNGS and on the Xenoscope facility at UZH. In particular, Paloma contributed to the background simulation development in the XENONnT high-energy regime (in the MeV region), and she investigated the sensitivity of the experiment to the 2vECß+ decay of Xe124. For Xenoscope, a vertical demonstrator for the future XLZD experiment, she succesfully designed and tested a new high-voltage delivery system bringing HV from air to vacuum to liquid xenon.

Congrats Paloma!!

Congratulations to our group member Gloria Senatore for being awarded the prestigious UZH Candoc Grant for doctoral students.

Gloria, member of the LEGEND collaboration, is characterizing candidate novel wavelength-shifting and reflective materials which have the potential to enhance light collection efficiency in the liquid argon instrumentation of LEGEND-1000. This is the next-generation neutrinoless double beta decay experiment, currently in the technical design phase, which will start data-taking around 2030 and probe the fundamental nature of neutrinos.

We congratulate Gloria for this achievement!

We congratulate former postdoctoral researcher Dr. Shingo Kazama on starting his new position as Associate Professor in the Department of Physics, School of Science, at Institute of Science Tokyo in April 2026, where he is establishing a new research group.

Shingo’s research focuses on rare-event searches with liquid xenon detectors, in particular direct searches for dark matter and low-energy neutrino physics. As a member of the XENONnT experiment, he contributes to searches for dark matter and rare low-energy interactions using one of the world’s most sensitive liquid xenon time projection chambers. His work combines physics analysis with detector development, with the goal of advancing the sensitivity of current and future liquid xenon experiments.

For the future XLZD experiment, Shingo is developing ultra-low-background detector technologies for next-generation xenon detectors. These include low-dark-count VUV SiPMs, novel hybrid PMT/SiPM photosensors, and hermetic xenon detectors for low-background R&D. These developments aim to enable more sensitive searches for dark matter, neutrinos, and other rare processes. He will also continue collaborating with the Baudis group on photosensor development for future xenon detectors.