Enhanced deep-learning approach to spatiotemporal multi-hit reconstruction with delay-line detectors

Knipfer M, Meier S, Heimerl J, López Hoffmann F, Volk T, Gleyzer S, Hommelhoff P (2026)

Publication Type: Journal article

Publication year: 2026

Journal

Book Volume: 25

Article Number: 034062

Journal Issue: 3

DOI: 10.1103/4rqm-y1l9

Abstract

Delay-line detectors (DLDs) are essential instruments for precise spatiotemporal particle detection. Traditional DLD event reconstruction methods struggle with overlapping signals from particles that arrive close together in space and time, limiting their multi-hit capabilities. Here, we report substantial updates to machine learning-based multi-hit reconstruction in DLDs, with a focus on the double-hit events and the first results for triple-hit events. Our enhanced neural-network pipeline includes improved data generation, cross-channel peak detection, and peak matching models. Using a spatial grid measurement, as well as a measurement with a discrete temporal structure, as benchmarks, we demonstrate significant improvements in both the spatial and energy domains. These advancements enable improved temporal and spatially resolved multi-hit detection in particle correlation experiments, with potential applications in attosecond physics, cold target recoil ion momentum spectroscopy, ultracold chemistry, and other fields.

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How to cite

APA:

Knipfer, M., Meier, S., Heimerl, J., López Hoffmann, F., Volk, T., Gleyzer, S., & Hommelhoff, P. (2026). Enhanced deep-learning approach to spatiotemporal multi-hit reconstruction with delay-line detectors. Physical Review Applied, 25(3). https://doi.org/10.1103/4rqm-y1l9

MLA:

Knipfer, Marco, et al. "Enhanced deep-learning approach to spatiotemporal multi-hit reconstruction with delay-line detectors." Physical Review Applied 25.3 (2026).

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