New Data-Driven Method Improves Particle Detection Accuracy in Segmented Detectors
A team of researchers has introduced a fully data-driven technique to directly measure the 'corner-clipping' probability in segmented particle detectors, eliminating reliance on Monte Carlo simulations. The method exploits nanosecond timing resolution to statistically separate genuine corner-clipping events from random coincidences, and was validated using simulations of the Pierre Auger Observatory's Underground Muon Detector. The advance could improve the accuracy of muon counting in extensive air shower measurements and is broadly applicable across high-energy and astroparticle physics experiments.
Corner-clipping is a systematic effect in segmented particle detectors whereby a single ionizing particle triggers signals in adjacent detector elements, causing direction-dependent overcounting that distorts reconstructed physical observables. Traditionally, this bias has been corrected using Monte Carlo simulations, which introduce their own modeling uncertainties. The new method, presented in a preprint submitted to The European Physical Journal C, instead uses the detectors' own nanosecond-scale timing data to statistically distinguish true corner-clipping events from accidental coincidences, using non-neighboring detector elements as an intrinsic control sample. Validation against detailed simulations of the Pierre Auger Observatory's Underground Muon Detector showed the technique reproduces the true angular dependence of corner-clipping probability with absolute deviations below 0.01. The authors also introduce a compact analytical parameterization that accounts for detector geometry, minimum detectable path length, and orientation-independent contributions, facilitating direct integration into reconstruction algorithms. The methodology is designed to be general-purpose, applicable to any segmented detector with sufficient timing resolution.
What's missing
The paper is a preprint and has not yet completed peer review at The European Physical Journal C. The study's validation relies entirely on simulations rather than real experimental data from the Pierre Auger Observatory, leaving open the question of how the method performs on actual detector data with real noise conditions and hardware imperfections.
What different sources said
- arXiv astro-phCenter
A data-driven method for measuring corner-clipping probabilities in segmented particle detectors
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