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Author  |
CALICE Collaboration (Lai, S. et al); Irles, A. |
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Title |
Shower separation in five dimensions for highly granular calorimeters using machine learning |
Type |
Journal Article |
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Year |
2024 |
Publication |
Journal of Instrumentation |
Abbreviated Journal |
J. Instrum. |
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Volume |
19 |
Issue |
10 |
Pages |
P10027 - 32pp |
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Keywords |
Large detector systems for particle and astroparticle physics; Pattern recognition; cluster finding; calibration and fitting methods; Performance of High Energy Physics Detectors |
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Abstract |
To achieve state-of-the-art jet energy resolution for Particle Flow, sophisticated energy clustering algorithms must be developed that can fully exploit available information to separate energy deposits from charged and neutral particles. Three published neural network-based shower separation models were applied to simulation and experimental data to measure the performance of the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL) technological prototype in distinguishing the energy deposited by a single charged and single neutral hadron for Particle Flow. The performance of models trained using only standard spatial, energy and charged track position information from an event was compared to models trained using timing information available from AHCAL, which is expected to improve sensitivity to shower development and, therefore, aid in clustering. Both simulation and experimental data were used to train and test the models and their performances were compared. The best-performing neural network achieved significantly superior event reconstruction when timing information was utilised in training for the case where the charged hadron had more energy than the neutral one, motivating temporally sensitive calorimeters. All models under test were observed to tend to allocate energy deposited by the more energetic of the two showers to the less energetic one. Similar shower reconstruction performance was observed for a model trained on simulation and applied to data and a model trained and applied to data. |
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Address |
[Lai, S.; Wilhahn, A.] Georg August Univ Gottingen, Phys Inst H, Friedrich Hund Pl 1, D-37077 Gottingen, Germany, Email: erika.garutti@uni-hamburg.de |
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Publisher |
IOP Publishing Ltd |
Place of Publication |
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Editor |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
1748-0221 |
ISBN |
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Medium |
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Expedition |
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Conference |
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Notes |
WOS:001381484600004 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
6406 |
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