Parnassus: An Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909397959049216 |
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| author | Dreyer, Etienne Gross, Eilam Kobylianskii, Dmitrii Mikuni, Vinicius Nachman, Benjamin Soybelman, Nathalie |
| author_facet | Dreyer, Etienne Gross, Eilam Kobylianskii, Dmitrii Mikuni, Vinicius Nachman, Benjamin Soybelman, Nathalie |
| contents | Detector simulation and reconstruction are a significant computational bottleneck in particle physics. We develop Particle-flow Neural Assisted Simulations (Parnassus) to address this challenge. Our deep learning model takes as input a point cloud (particles impinging on a detector) and produces a point cloud (reconstructed particles). By combining detector simulations and reconstruction into one step, we aim to minimize resource utilization and enable fast surrogate models suitable for application both inside and outside large collaborations. We demonstrate this approach using a publicly available dataset of jets passed through the full simulation and reconstruction pipeline of the CMS experiment. We show that Parnassus accurately mimics the CMS particle flow algorithm on the (statistically) same events it was trained on and can generalize to jet momentum and type outside of the training distribution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01620 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Parnassus: An Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction Dreyer, Etienne Gross, Eilam Kobylianskii, Dmitrii Mikuni, Vinicius Nachman, Benjamin Soybelman, Nathalie Data Analysis, Statistics and Probability High Energy Physics - Experiment High Energy Physics - Phenomenology Detector simulation and reconstruction are a significant computational bottleneck in particle physics. We develop Particle-flow Neural Assisted Simulations (Parnassus) to address this challenge. Our deep learning model takes as input a point cloud (particles impinging on a detector) and produces a point cloud (reconstructed particles). By combining detector simulations and reconstruction into one step, we aim to minimize resource utilization and enable fast surrogate models suitable for application both inside and outside large collaborations. We demonstrate this approach using a publicly available dataset of jets passed through the full simulation and reconstruction pipeline of the CMS experiment. We show that Parnassus accurately mimics the CMS particle flow algorithm on the (statistically) same events it was trained on and can generalize to jet momentum and type outside of the training distribution. |
| title | Parnassus: An Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction |
| topic | Data Analysis, Statistics and Probability High Energy Physics - Experiment High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2406.01620 |