Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks

Abstract 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 signif...
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Autor*in:	Wemmer, F. [verfasserIn] 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:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Calorimeter Photon reconstruction Overlapping clusters High background Fuzzy clustering Machine learning Deep learning Graph neural networks End-to-end representation spaces

Anmerkung:	© The Author(s) 2023

Übergeordnetes Werk:	Enthalten in: Computing and software for big science - Cham, Switzerland : Springer International Publishing, 2017, 7(2023), 1 vom: Dez.
Übergeordnetes Werk:	volume:7 ; year:2023 ; number:1 ; month:12

Links:	Volltext

DOI / URN:	10.1007/s41781-023-00105-w

Katalog-ID:	SPR05410727X

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allfields	10.1007/s41781-023-00105-w doi (DE-627)SPR05410727X (SPR)s41781-023-00105-w-e DE-627 ger DE-627 rakwb eng Wemmer, F. verfasserin (orcid)0000-0002-6475-0834 aut Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract 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 %$E_{\gamma }<0.5\,\mathrm {\,Ge\hspace{-1.00006pt}V}%$ 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. Calorimeter (dpeaa)DE-He213 Photon reconstruction (dpeaa)DE-He213 Overlapping clusters (dpeaa)DE-He213 High background (dpeaa)DE-He213 Fuzzy clustering (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Graph neural networks (dpeaa)DE-He213 End-to-end representation spaces (dpeaa)DE-He213 Haide, I. (orcid)0000-0003-0962-6344 aut Eppelt, J. (orcid)0000-0001-8368-3721 aut Ferber, T. (orcid)0000-0002-6849-0427 aut Beaubien, A. (orcid)0000-0001-9438-089X aut Branchini, P. (orcid)0000-0002-2270-9673 aut Campajola, M. (orcid)0000-0003-2518-7134 aut Cecchi, C. (orcid)0000-0002-2192-8233 aut Cheema, P. (orcid)0000-0001-8472-5727 aut De Nardo, G. (orcid)0000-0002-2047-9675 aut Hearty, C. (orcid)0000-0001-6568-0252 aut Kuzmin, A. (orcid)0000-0002-7011-5044 aut Longo, S. (orcid)0000-0002-8124-8969 aut Manoni, E. (orcid)0000-0002-9826-7947 aut Meier, F. (orcid)0000-0002-6088-0412 aut Merola, M. (orcid)0000-0002-7082-8108 aut Miyabayashi, K. (orcid)0000-0003-4352-734X aut Moneta, S. (orcid)0000-0003-2184-7510 aut Remnev, M. (orcid)0000-0001-6975-1724 aut Roney, J. M. (orcid)0000-0001-7802-4617 aut Shiu, J.-G. (orcid)0000-0002-8478-5639 aut Shwartz, B. (orcid)0000-0002-1456-1496 aut Unno, Y. (orcid)0000-0003-3355-765X aut van Tonder, R. (orcid)0000-0002-7448-4816 aut Volpe, R. (orcid)0000-0003-1782-2978 aut Enthalten in Computing and software for big science Cham, Switzerland : Springer International Publishing, 2017 7(2023), 1 vom: Dez. (DE-627)1000743217 (DE-600)2908677-2 2510-2044 nnns volume:7 year:2023 number:1 month:12 https://dx.doi.org/10.1007/s41781-023-00105-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2050 GBV_ILN_2188 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2023 1 12
spelling	10.1007/s41781-023-00105-w doi (DE-627)SPR05410727X (SPR)s41781-023-00105-w-e DE-627 ger DE-627 rakwb eng Wemmer, F. verfasserin (orcid)0000-0002-6475-0834 aut Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract 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 %$E_{\gamma }<0.5\,\mathrm {\,Ge\hspace{-1.00006pt}V}%$ 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. Calorimeter (dpeaa)DE-He213 Photon reconstruction (dpeaa)DE-He213 Overlapping clusters (dpeaa)DE-He213 High background (dpeaa)DE-He213 Fuzzy clustering (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Graph neural networks (dpeaa)DE-He213 End-to-end representation spaces (dpeaa)DE-He213 Haide, I. (orcid)0000-0003-0962-6344 aut Eppelt, J. (orcid)0000-0001-8368-3721 aut Ferber, T. (orcid)0000-0002-6849-0427 aut Beaubien, A. (orcid)0000-0001-9438-089X aut Branchini, P. (orcid)0000-0002-2270-9673 aut Campajola, M. (orcid)0000-0003-2518-7134 aut Cecchi, C. (orcid)0000-0002-2192-8233 aut Cheema, P. (orcid)0000-0001-8472-5727 aut De Nardo, G. (orcid)0000-0002-2047-9675 aut Hearty, C. (orcid)0000-0001-6568-0252 aut Kuzmin, A. (orcid)0000-0002-7011-5044 aut Longo, S. (orcid)0000-0002-8124-8969 aut Manoni, E. (orcid)0000-0002-9826-7947 aut Meier, F. (orcid)0000-0002-6088-0412 aut Merola, M. (orcid)0000-0002-7082-8108 aut Miyabayashi, K. (orcid)0000-0003-4352-734X aut Moneta, S. (orcid)0000-0003-2184-7510 aut Remnev, M. (orcid)0000-0001-6975-1724 aut Roney, J. M. (orcid)0000-0001-7802-4617 aut Shiu, J.-G. (orcid)0000-0002-8478-5639 aut Shwartz, B. (orcid)0000-0002-1456-1496 aut Unno, Y. (orcid)0000-0003-3355-765X aut van Tonder, R. (orcid)0000-0002-7448-4816 aut Volpe, R. (orcid)0000-0003-1782-2978 aut Enthalten in Computing and software for big science Cham, Switzerland : Springer International Publishing, 2017 7(2023), 1 vom: Dez. (DE-627)1000743217 (DE-600)2908677-2 2510-2044 nnns volume:7 year:2023 number:1 month:12 https://dx.doi.org/10.1007/s41781-023-00105-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2050 GBV_ILN_2188 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2023 1 12
allfields_unstemmed	10.1007/s41781-023-00105-w doi (DE-627)SPR05410727X (SPR)s41781-023-00105-w-e DE-627 ger DE-627 rakwb eng Wemmer, F. verfasserin (orcid)0000-0002-6475-0834 aut Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract 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 %$E_{\gamma }<0.5\,\mathrm {\,Ge\hspace{-1.00006pt}V}%$ 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. Calorimeter (dpeaa)DE-He213 Photon reconstruction (dpeaa)DE-He213 Overlapping clusters (dpeaa)DE-He213 High background (dpeaa)DE-He213 Fuzzy clustering (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Graph neural networks (dpeaa)DE-He213 End-to-end representation spaces (dpeaa)DE-He213 Haide, I. (orcid)0000-0003-0962-6344 aut Eppelt, J. (orcid)0000-0001-8368-3721 aut Ferber, T. (orcid)0000-0002-6849-0427 aut Beaubien, A. (orcid)0000-0001-9438-089X aut Branchini, P. (orcid)0000-0002-2270-9673 aut Campajola, M. (orcid)0000-0003-2518-7134 aut Cecchi, C. (orcid)0000-0002-2192-8233 aut Cheema, P. (orcid)0000-0001-8472-5727 aut De Nardo, G. (orcid)0000-0002-2047-9675 aut Hearty, C. (orcid)0000-0001-6568-0252 aut Kuzmin, A. (orcid)0000-0002-7011-5044 aut Longo, S. (orcid)0000-0002-8124-8969 aut Manoni, E. (orcid)0000-0002-9826-7947 aut Meier, F. (orcid)0000-0002-6088-0412 aut Merola, M. (orcid)0000-0002-7082-8108 aut Miyabayashi, K. (orcid)0000-0003-4352-734X aut Moneta, S. (orcid)0000-0003-2184-7510 aut Remnev, M. (orcid)0000-0001-6975-1724 aut Roney, J. M. (orcid)0000-0001-7802-4617 aut Shiu, J.-G. (orcid)0000-0002-8478-5639 aut Shwartz, B. (orcid)0000-0002-1456-1496 aut Unno, Y. (orcid)0000-0003-3355-765X aut van Tonder, R. (orcid)0000-0002-7448-4816 aut Volpe, R. (orcid)0000-0003-1782-2978 aut Enthalten in Computing and software for big science Cham, Switzerland : Springer International Publishing, 2017 7(2023), 1 vom: Dez. (DE-627)1000743217 (DE-600)2908677-2 2510-2044 nnns volume:7 year:2023 number:1 month:12 https://dx.doi.org/10.1007/s41781-023-00105-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2050 GBV_ILN_2188 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2023 1 12
allfieldsGer	10.1007/s41781-023-00105-w doi (DE-627)SPR05410727X (SPR)s41781-023-00105-w-e DE-627 ger DE-627 rakwb eng Wemmer, F. verfasserin (orcid)0000-0002-6475-0834 aut Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract 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 %$E_{\gamma }<0.5\,\mathrm {\,Ge\hspace{-1.00006pt}V}%$ 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. Calorimeter (dpeaa)DE-He213 Photon reconstruction (dpeaa)DE-He213 Overlapping clusters (dpeaa)DE-He213 High background (dpeaa)DE-He213 Fuzzy clustering (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Graph neural networks (dpeaa)DE-He213 End-to-end representation spaces (dpeaa)DE-He213 Haide, I. (orcid)0000-0003-0962-6344 aut Eppelt, J. (orcid)0000-0001-8368-3721 aut Ferber, T. (orcid)0000-0002-6849-0427 aut Beaubien, A. (orcid)0000-0001-9438-089X aut Branchini, P. (orcid)0000-0002-2270-9673 aut Campajola, M. (orcid)0000-0003-2518-7134 aut Cecchi, C. (orcid)0000-0002-2192-8233 aut Cheema, P. (orcid)0000-0001-8472-5727 aut De Nardo, G. (orcid)0000-0002-2047-9675 aut Hearty, C. (orcid)0000-0001-6568-0252 aut Kuzmin, A. (orcid)0000-0002-7011-5044 aut Longo, S. (orcid)0000-0002-8124-8969 aut Manoni, E. (orcid)0000-0002-9826-7947 aut Meier, F. (orcid)0000-0002-6088-0412 aut Merola, M. (orcid)0000-0002-7082-8108 aut Miyabayashi, K. (orcid)0000-0003-4352-734X aut Moneta, S. (orcid)0000-0003-2184-7510 aut Remnev, M. (orcid)0000-0001-6975-1724 aut Roney, J. M. (orcid)0000-0001-7802-4617 aut Shiu, J.-G. (orcid)0000-0002-8478-5639 aut Shwartz, B. (orcid)0000-0002-1456-1496 aut Unno, Y. (orcid)0000-0003-3355-765X aut van Tonder, R. (orcid)0000-0002-7448-4816 aut Volpe, R. (orcid)0000-0003-1782-2978 aut Enthalten in Computing and software for big science Cham, Switzerland : Springer International Publishing, 2017 7(2023), 1 vom: Dez. (DE-627)1000743217 (DE-600)2908677-2 2510-2044 nnns volume:7 year:2023 number:1 month:12 https://dx.doi.org/10.1007/s41781-023-00105-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2050 GBV_ILN_2188 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2023 1 12
allfieldsSound	10.1007/s41781-023-00105-w doi (DE-627)SPR05410727X (SPR)s41781-023-00105-w-e DE-627 ger DE-627 rakwb eng Wemmer, F. verfasserin (orcid)0000-0002-6475-0834 aut Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract 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 %$E_{\gamma }<0.5\,\mathrm {\,Ge\hspace{-1.00006pt}V}%$ 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. Calorimeter (dpeaa)DE-He213 Photon reconstruction (dpeaa)DE-He213 Overlapping clusters (dpeaa)DE-He213 High background (dpeaa)DE-He213 Fuzzy clustering (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Graph neural networks (dpeaa)DE-He213 End-to-end representation spaces (dpeaa)DE-He213 Haide, I. (orcid)0000-0003-0962-6344 aut Eppelt, J. (orcid)0000-0001-8368-3721 aut Ferber, T. (orcid)0000-0002-6849-0427 aut Beaubien, A. (orcid)0000-0001-9438-089X aut Branchini, P. (orcid)0000-0002-2270-9673 aut Campajola, M. (orcid)0000-0003-2518-7134 aut Cecchi, C. (orcid)0000-0002-2192-8233 aut Cheema, P. (orcid)0000-0001-8472-5727 aut De Nardo, G. (orcid)0000-0002-2047-9675 aut Hearty, C. (orcid)0000-0001-6568-0252 aut Kuzmin, A. (orcid)0000-0002-7011-5044 aut Longo, S. (orcid)0000-0002-8124-8969 aut Manoni, E. (orcid)0000-0002-9826-7947 aut Meier, F. (orcid)0000-0002-6088-0412 aut Merola, M. (orcid)0000-0002-7082-8108 aut Miyabayashi, K. (orcid)0000-0003-4352-734X aut Moneta, S. (orcid)0000-0003-2184-7510 aut Remnev, M. (orcid)0000-0001-6975-1724 aut Roney, J. M. (orcid)0000-0001-7802-4617 aut Shiu, J.-G. (orcid)0000-0002-8478-5639 aut Shwartz, B. (orcid)0000-0002-1456-1496 aut Unno, Y. (orcid)0000-0003-3355-765X aut van Tonder, R. (orcid)0000-0002-7448-4816 aut Volpe, R. (orcid)0000-0003-1782-2978 aut Enthalten in Computing and software for big science Cham, Switzerland : Springer International Publishing, 2017 7(2023), 1 vom: Dez. (DE-627)1000743217 (DE-600)2908677-2 2510-2044 nnns volume:7 year:2023 number:1 month:12 https://dx.doi.org/10.1007/s41781-023-00105-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2050 GBV_ILN_2188 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2023 1 12
language	English
source	Enthalten in Computing and software for big science 7(2023), 1 vom: Dez. volume:7 year:2023 number:1 month:12
sourceStr	Enthalten in Computing and software for big science 7(2023), 1 vom: Dez. volume:7 year:2023 number:1 month:12
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Calorimeter Photon reconstruction Overlapping clusters High background Fuzzy clustering Machine learning Deep learning Graph neural networks End-to-end representation spaces
isfreeaccess_bool	true
container_title	Computing and software for big science
authorswithroles_txt_mv	Wemmer, F. @@aut@@ Haide, I. @@aut@@ Eppelt, J. @@aut@@ Ferber, T. @@aut@@ Beaubien, A. @@aut@@ Branchini, P. @@aut@@ Campajola, M. @@aut@@ Cecchi, C. @@aut@@ Cheema, P. @@aut@@ De Nardo, G. @@aut@@ Hearty, C. @@aut@@ Kuzmin, A. @@aut@@ Longo, S. @@aut@@ Manoni, E. @@aut@@ Meier, F. @@aut@@ Merola, M. @@aut@@ Miyabayashi, K. @@aut@@ Moneta, S. @@aut@@ Remnev, M. @@aut@@ Roney, J. M. @@aut@@ Shiu, J.-G. @@aut@@ Shwartz, B. @@aut@@ Unno, Y. @@aut@@ van Tonder, R. @@aut@@ Volpe, R. @@aut@@
publishDateDaySort_date	2023-12-01T00:00:00Z
hierarchy_top_id	1000743217
id	SPR05410727X
language_de	englisch
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author	Wemmer, F.
spellingShingle	Wemmer, F. misc Calorimeter misc Photon reconstruction misc Overlapping clusters misc High background misc Fuzzy clustering misc Machine learning misc Deep learning misc Graph neural networks misc End-to-end representation spaces Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks
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topic_title	Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks Calorimeter (dpeaa)DE-He213 Photon reconstruction (dpeaa)DE-He213 Overlapping clusters (dpeaa)DE-He213 High background (dpeaa)DE-He213 Fuzzy clustering (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Graph neural networks (dpeaa)DE-He213 End-to-end representation spaces (dpeaa)DE-He213
topic	misc Calorimeter misc Photon reconstruction misc Overlapping clusters misc High background misc Fuzzy clustering misc Machine learning misc Deep learning misc Graph neural networks misc End-to-end representation spaces
topic_unstemmed	misc Calorimeter misc Photon reconstruction misc Overlapping clusters misc High background misc Fuzzy clustering misc Machine learning misc Deep learning misc Graph neural networks misc End-to-end representation spaces
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title	Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks
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title_sort	photon reconstruction in the belle ii calorimeter using graph neural networks
title_auth	Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks
abstract	Abstract 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 %$E_{\gamma }<0.5\,\mathrm {\,Ge\hspace{-1.00006pt}V}%$ 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. © The Author(s) 2023
abstractGer	Abstract 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 %$E_{\gamma }<0.5\,\mathrm {\,Ge\hspace{-1.00006pt}V}%$ 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. © The Author(s) 2023
abstract_unstemmed	Abstract 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 %$E_{\gamma }<0.5\,\mathrm {\,Ge\hspace{-1.00006pt}V}%$ 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. © The Author(s) 2023
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title_short	Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks
url	https://dx.doi.org/10.1007/s41781-023-00105-w
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author2	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.
author2Str	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.
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doi_str	10.1007/s41781-023-00105-w
up_date	2024-08-28T05:54:07.557Z
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Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks

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