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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2303.00693 |
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| _version_ | 1866913326550745088 |
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| author | Hashemi, Baran Hartmann, Nikolai Kuhr, Thomas Ritter, Martin srebre, Matej |
| author_facet | Hashemi, Baran Hartmann, Nikolai Kuhr, Thomas Ritter, Martin srebre, Matej |
| contents | The pixel vertex detector (PXD) is an essential part of the Belle II detector recording particle positions. Data from the PXD and other sensors allow us to reconstruct particle tracks and decay vertices. The effect of background hits on track reconstruction is simulated by adding measured or simulated background hit patterns to the hits produced by simulated signal particles. This model requires a large set of statistically independent PXD background noise samples to avoid a systematic bias of reconstructed tracks. However, data from the fine-grained PXD requires a substantial amount of storage. As an efficient way of producing background noise, we explore the idea of an on-demand PXD background generator using conditional Generative Adversarial Networks (GANs) with contrastive learning, adapted by the number of PXD sensors in order to both increase the image fidelity and produce sensor-dependent PXD hitmaps. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_00693 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | PE-GAN: Prior Embedding GAN for PXD images at Belle II Hashemi, Baran Hartmann, Nikolai Kuhr, Thomas Ritter, Martin srebre, Matej High Energy Physics - Phenomenology Computer Vision and Pattern Recognition Machine Learning The pixel vertex detector (PXD) is an essential part of the Belle II detector recording particle positions. Data from the PXD and other sensors allow us to reconstruct particle tracks and decay vertices. The effect of background hits on track reconstruction is simulated by adding measured or simulated background hit patterns to the hits produced by simulated signal particles. This model requires a large set of statistically independent PXD background noise samples to avoid a systematic bias of reconstructed tracks. However, data from the fine-grained PXD requires a substantial amount of storage. As an efficient way of producing background noise, we explore the idea of an on-demand PXD background generator using conditional Generative Adversarial Networks (GANs) with contrastive learning, adapted by the number of PXD sensors in order to both increase the image fidelity and produce sensor-dependent PXD hitmaps. |
| title | PE-GAN: Prior Embedding GAN for PXD images at Belle II |
| topic | High Energy Physics - Phenomenology Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2303.00693 |