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Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks

Fuente: arXiv

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Bibliographische Detailangaben
Hauptverfasser:	Wemmer, F., Haide, I., Eppelt, J., Ferber, T., Beaubien, A., Branchini, P., Campajola, M., Cecchi, C., Cheema, P., De Nardo, G., Hearty, C., Kuzmin, A., Longo, S., Manoni, E., Meier, F., Merola, M., Miyabayashi, K., Moneta, S., Remnev, M., Roney, J. M., Shiu, J. -G., Shwartz, B., Unno, Y., van Tonder, R., Volpe, R.
Format:	Preprint
Veröffentlicht:	2023
Schlagworte:	High Energy Physics - Experiment Instrumentation and Detectors
Online-Zugang:	Acceder al recurso
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_version_	1866916144122691584
author	Wemmer, F. Haide, I. Eppelt, J. Ferber, T. Beaubien, A. Branchini, P. Campajola, M. Cecchi, C. Cheema, P. De Nardo, G. Hearty, C. Kuzmin, A. Longo, S. Manoni, E. Meier, F. Merola, M. Miyabayashi, K. Moneta, S. Remnev, M. Roney, J. M. Shiu, J. -G. Shwartz, B. Unno, Y. van Tonder, R. Volpe, R.
author_facet	Wemmer, F. Haide, I. Eppelt, J. Ferber, T. Beaubien, A. Branchini, P. Campajola, M. Cecchi, C. Cheema, P. De Nardo, G. Hearty, C. Kuzmin, A. Longo, S. Manoni, E. Meier, F. Merola, M. Miyabayashi, K. Moneta, S. Remnev, M. Roney, J. M. Shiu, J. -G. Shwartz, B. Unno, Y. van Tonder, R. Volpe, R.
contents	We present the study of a fuzzy clustering algorithm for the Belle II electromagnetic calorimeter using Graph Neural Networks. We use a realistic detector simulation including simulated beam backgrounds and focus on the reconstruction of both isolated and overlapping photons. We find significant improvements of the energy resolution compared to the currently used reconstruction algorithm for both isolated and overlapping photons of more than 30% for photons with energies E < 0.5 GeV and high levels of beam backgrounds. Overall, the GNN reconstruction improves the resolution and reduces the tails of the reconstructed energy distribution and therefore is a promising option for the upcoming high luminosity running of Belle II.
format	Preprint
id	arxiv_https___arxiv_org_abs_2306_04179
institution	arXiv
publishDate	2023
record_format	arxiv
spellingShingle	Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks Wemmer, F. Haide, I. Eppelt, J. Ferber, T. Beaubien, A. Branchini, P. Campajola, M. Cecchi, C. Cheema, P. De Nardo, G. Hearty, C. Kuzmin, A. Longo, S. Manoni, E. Meier, F. Merola, M. Miyabayashi, K. Moneta, S. Remnev, M. Roney, J. M. Shiu, J. -G. Shwartz, B. Unno, Y. van Tonder, R. Volpe, R. High Energy Physics - Experiment Instrumentation and Detectors We present the study of a fuzzy clustering algorithm for the Belle II electromagnetic calorimeter using Graph Neural Networks. We use a realistic detector simulation including simulated beam backgrounds and focus on the reconstruction of both isolated and overlapping photons. We find significant improvements of the energy resolution compared to the currently used reconstruction algorithm for both isolated and overlapping photons of more than 30% for photons with energies E < 0.5 GeV and high levels of beam backgrounds. Overall, the GNN reconstruction improves the resolution and reduces the tails of the reconstructed energy distribution and therefore is a promising option for the upcoming high luminosity running of Belle II.
title	Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks
topic	High Energy Physics - Experiment Instrumentation and Detectors
url	https://arxiv.org/abs/2306.04179