Erlangen 2026 – scientific programme

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T: Fachverband Teilchenphysik

T 50: Data, AI, Computing, Electronics V

T 50.2: Talk

Wednesday, March 18, 2026, 16:30–16:45, KH 00.024

Search for keV-Scale Sterile Neutrinos with TRISTAN at KATRIN Using Neural Simulation-Based Inference — •Luca Fallböhmer for the KATRIN collaboration — Max-Planck-Institute for Nuclear Physics

Following the completion of its neutrino mass measurement program at the end of 2025, the KATRIN experiment aims to probe keV-scale sterile neutrinos by analyzing the full tritium beta decay spectrum with a novel detector system, TRISTAN. Leveraging KATRINs high source activity, this search is sensitive to mixing amplitudes at the parts-per-million level. However, extracting a potential sterile neutrino signature is challenging, as it relies on detailed modeling of the the observed tritium spectrum and requires computationally intensive Monte Carlo simulations. To address this challenge, we implement neural simulation-based inference using normalizing flows to approximate the underlying probability density of the physics simulation. We demonstrate that continuous normalizing flows trained via conditional flow-matching enable highly efficient modeling of experimental spectra. This approach opens up the possibility of a fast surrogate model for rapid sampling and generates a continuous, unbinned representation of the KATRIN beamline response, accelerating and enabling the analysis pipeline.

Keywords: Normalizing Flows; Neural Simulation-Based Inference; Machine Learning; Sterile Neutrinos; Dark Matter