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Q: Fachverband Quantenoptik und Photonik
Q 57: Poster VIII
Q 57.15: Poster
Donnerstag, 14. März 2024, 17:00–19:00, Aula Foyer
Enhancing multi-electron event reconstruction for delay line detectors using deep learning — •Tobias Volk1, Marco Knipfer1, Stefan Meier1, Jonas Heimerl1, Sergei Gleyzer2, and Peter Hommelhoff1 — 1Department of Physics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91058 Erlangen, Germany — 2Department of Physics and Astronomy, University of Alabama, Tuscaloosa, AL 35487, USA
Accurate detection of multiple, closely spaced electrons is of utmost interest for correlation experiments [1, 2]. However, the reconstruction of individual electrons becomes particularly challenging if multiple electrons arrive closely confined in space and time. One possibility for a multi-hit capable detector system are so-called delay line detectors, where the core aspect of electron event reconstruction is the detection of voltage peaks. While classical methods work well on single electron events, they fail to reconstruct multiple close electrons arriving within a narrow time window. The result is a profound dead zone hindering the evaluation of especially interesting, close electron events. To address this challenge, we introduce a deep learning approach for the spatio-temporal reconstruction of multi-electron events [3]. We achieve a dead radius of 2.5 mm, reducing the classical limit by a factor of 8 while improving the overall resolution. Based on this, already existing delay-line setups can be improved posterior, not limited to electrons.
[1] S. Meier et al., Nature Physics 19, 1402-1409 (2023)
[2] R. Haindl et al., Nature Physics 19, 1410-1417 (2023)
[3] M. Knipfer et al., arXiv:2306.09359 (2023)
Keywords: delay line; detector; machine learning; neural networks; electron correlations