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T: Fachverband Teilchenphysik

T 34: Data, AI, Computing, Electronics IV

T 34.8: Vortrag

Dienstag, 17. März 2026, 18:00–18:15, KH 02.014

Python framework for the topological track reconstruction in JUNO — •Mikhail Smirnov, Daniel Bick, Milo Charavet, and Caren Hagner — Institute of Experimental Physics, University of Hamburg, Hamburg, Germany

The Jiangmen Underground Neutrino Observatory (JUNO) is a new-generation kiloton-scale neutrino detector based on liquid scintillator (LSc). With a target mass of 20 kilotons, it is the largest LSc detector in the world. Utilizing the antineutrino flux from two nuclear power plants at a baseline of approximately 53 km, JUNO aims to determine the neutrino mass ordering with high statistical significance and to perform precision measurements of neutrino oscillation parameters. JUNO has successfully started to operate in August 2025. Topological Track Reconstruction (TTR) was originally developed to reconstruct energetic muon events in large unsegmented LSc detectors. The algorithm exploits the time and spatial distributions of PMT first-hit times to reconstruct a three-dimensional photon-emission density inside the detector. In the original C++ implementation, this density is obtained via deterministic kernel summation, which provides a heuristic estimate but lacks a well-defined statistical interpretation. This talk presents pyTTR, a Python-based reimplementation and conceptual extension of the TTR. The new framework reformulates TTR as a likelihood-based inverse problem, naturally separating detector geometry, photon propagation, PMT response, and statistical inference, and provides increased flexibility for future physics analyses

Keywords: neutrino; reconstruction; topology; JUNO; python