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| 001 | 634234 | ||
| 005 | 20250818131152.0 | ||
| 024 | 7 | _ | |a 10.1088/1742-6596/2438/1/012111 |2 doi |
| 024 | 7 | _ | |a Charan:2023ldg |2 INSPIRETeX |
| 024 | 7 | _ | |a inspire:2627250 |2 inspire |
| 024 | 7 | _ | |a 1742-6588 |2 ISSN |
| 024 | 7 | _ | |a 1742-6596 |2 ISSN |
| 024 | 7 | _ | |a arXiv:2301.11654 |2 arXiv |
| 037 | _ | _ | |a PUBDB-2025-02482 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 530 |
| 088 | _ | _ | |a arXiv:2301.11654 |2 arXiv |
| 100 | 1 | _ | |a Charan, Abtin Narimani |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research |g ACAT 2021 |c Daejeon |d 2021-11-29 - 2021-12-03 |w South Korea |
| 245 | _ | _ | |a Particle identification with the Belle II calorimeter using machine learning |
| 260 | _ | _ | |a Bristol |c 2023 |b IOP Publ. |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Contribution to a conference proceedings |0 PUB:(DE-HGF)8 |2 PUB:(DE-HGF) |m contrib |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1755515414_3983139 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 500 | _ | _ | |a 5 pages, 6 figures, Proceedings for poster contribution to 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, 29 November - 3 December 2021, It will be published in: IOP Conference Series |
| 520 | _ | _ | |a I present an application of a convolutional neural network (CNN) to separate muons and pions in the Belle II electromagnetic calorimeter (ECL). The ECL is designed to measure the energy deposited by charged and neutral particles. It also provides important contributions to the particle identification (PID) system. Identification of low-momenta muons and pions in the ECL is crucial if they do not reach the outer muon detector. Track-seeded cluster energy images provide the maximal possible information. The shape of the energy depositions for muons and pions in the crystals around an extrapolated track at the entering point of the ECL is used together with crystal positions in θ − ϕ plane and transverse momentum of the track to train a CNN. The CNN exploits the difference between the dispersed energy depositions from pion hadronic interactions and the more localized muon electromagnetic interactions. Using simulation, the performance of the CNN algorithm is compared with other PID methods at Belle II which are based on track-matched clustering information. The results show that the CNN PID method improves muon-pion separation in low momentum. |
| 536 | _ | _ | |a 611 - Fundamental Particles and Forces (POF4-611) |0 G:(DE-HGF)POF4-611 |c POF4-611 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, INSPIRE, Journals: bib-pubdb1.desy.de |
| 650 | _ | 7 | |a electron positron: annihilation |2 INSPIRE |
| 650 | _ | 7 | |a electron positron: colliding beams |2 INSPIRE |
| 650 | _ | 7 | |a momentum: low |2 INSPIRE |
| 650 | _ | 7 | |a muon: detector |2 INSPIRE |
| 650 | _ | 7 | |a tracks: transverse momentum |2 INSPIRE |
| 650 | _ | 7 | |a calorimeter: electromagnetic |2 INSPIRE |
| 650 | _ | 7 | |a muon: particle identification |2 INSPIRE |
| 650 | _ | 7 | |a pi: particle identification |2 INSPIRE |
| 650 | _ | 7 | |a BELLE |2 INSPIRE |
| 650 | _ | 7 | |a crystal |2 INSPIRE |
| 650 | _ | 7 | |a electromagnetic interaction |2 INSPIRE |
| 650 | _ | 7 | |a cluster |2 INSPIRE |
| 650 | _ | 7 | |a machine learning |2 INSPIRE |
| 650 | _ | 7 | |a neutral particle |2 INSPIRE |
| 650 | _ | 7 | |a particle identification: performance |2 INSPIRE |
| 650 | _ | 7 | |a neural network |2 INSPIRE |
| 650 | _ | 7 | |a statistical analysis |2 INSPIRE |
| 650 | _ | 7 | |a data analysis method |2 INSPIRE |
| 650 | _ | 7 | |a numerical calculations: Monte Carlo |2 INSPIRE |
| 650 | _ | 7 | |a experimental results |2 INSPIRE |
| 693 | _ | _ | |a KEK |e KEK: BELLE I/II |1 EXP:(DE-H253)KEK-20150101 |0 EXP:(DE-H253)BELLE-20150101 |5 EXP:(DE-H253)BELLE-20150101 |x 0 |
| 773 | _ | _ | |a 10.1088/1742-6596/2438/1/012111 |g Vol. 2438, no. 1, p. 012111 - |0 PERI:(DE-600)2166409-2 |n 1 |p 012111 - |t Journal of physics / Conference Series |v 2438 |y 2023 |x 1742-6588 |
| 856 | 4 | _ | |u https://bib-pubdb1.desy.de/record/634234/files/Narimani_Charan_2023_J._Phys.__Conf._Ser._2438_012111.pdf |y Restricted |
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| 910 | 1 | _ | |a Deutsches Elektronen-Synchrotron |0 I:(DE-588b)2008985-5 |k DESY |b 0 |6 P:(DE-HGF)0 |
| 913 | 1 | _ | |a DE-HGF |b Forschungsbereich Materie |l Matter and the Universe |1 G:(DE-HGF)POF4-610 |0 G:(DE-HGF)POF4-611 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-600 |4 G:(DE-HGF)POF |v Fundamental Particles and Forces |x 0 |
| 915 | _ | _ | |a Nationallizenz |0 StatID:(DE-HGF)0420 |2 StatID |d 2024-12-20 |w ger |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2024-12-20 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2024-12-20 |
| 920 | 1 | _ | |0 I:(DE-H253)BELLE-20210408 |k BELLE |l BELLE II Experiment |x 0 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a EDITORS |
| 980 | _ | _ | |a VDBINPRINT |
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| 980 | _ | _ | |a I:(DE-H253)BELLE-20210408 |
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