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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.17436 |
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| _version_ | 1866911965929013248 |
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| author | Karwowska, Maja Graczykowski, Łukasz Deja, Kamil Kasak, Miłosz Janik, Małgorzata |
| author_facet | Karwowska, Maja Graczykowski, Łukasz Deja, Kamil Kasak, Miłosz Janik, Małgorzata |
| contents | The ALICE experiment at the LHC measures properties of the strongly interacting matter formed in ultrarelativistic heavy-ion collisions. Such studies require accurate particle identification (PID). ALICE provides PID information via several detectors for particles with momentum from about 100 MeV/c up to 20 GeV/c. Traditionally, particles are selected with rectangular cuts. A much better performance can be achieved with machine learning (ML) methods. Our solution uses multiple neural networks (NN) serving as binary classifiers. Moreover, we extended our particle classifier with Feature Set Embedding and attention in order to train on data with incomplete samples. We also present the integration of the ML project with the ALICE analysis software, and we discuss domain adaptation, the ML technique needed to transfer the knowledge between simulated and real experimental data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17436 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Particle identification with machine learning from incomplete data in the ALICE experiment Karwowska, Maja Graczykowski, Łukasz Deja, Kamil Kasak, Miłosz Janik, Małgorzata High Energy Physics - Experiment Machine Learning The ALICE experiment at the LHC measures properties of the strongly interacting matter formed in ultrarelativistic heavy-ion collisions. Such studies require accurate particle identification (PID). ALICE provides PID information via several detectors for particles with momentum from about 100 MeV/c up to 20 GeV/c. Traditionally, particles are selected with rectangular cuts. A much better performance can be achieved with machine learning (ML) methods. Our solution uses multiple neural networks (NN) serving as binary classifiers. Moreover, we extended our particle classifier with Feature Set Embedding and attention in order to train on data with incomplete samples. We also present the integration of the ML project with the ALICE analysis software, and we discuss domain adaptation, the ML technique needed to transfer the knowledge between simulated and real experimental data. |
| title | Particle identification with machine learning from incomplete data in the ALICE experiment |
| topic | High Energy Physics - Experiment Machine Learning |
| url | https://arxiv.org/abs/2403.17436 |