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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/2406.01507 |
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| _version_ | 1866915036131229696 |
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| author | Pazos, Camila Aeron, Shuchin Beauchemin, Pierre-Hugues Croft, Vincent Huan, Zhengyan Klassen, Martin Wongjirad, Taritree |
| author_facet | Pazos, Camila Aeron, Shuchin Beauchemin, Pierre-Hugues Croft, Vincent Huan, Zhengyan Klassen, Martin Wongjirad, Taritree |
| contents | Correcting for detector effects in experimental data, particularly through unfolding, is critical for enabling precision measurements in high-energy physics. However, traditional unfolding methods face challenges in scalability, flexibility, and dependence on simulations. We introduce a novel approach to multidimensional object-wise unfolding using conditional Denoising Diffusion Probabilistic Models (cDDPM). Our method utilizes the cDDPM for a non-iterative, flexible posterior sampling approach, incorporating distribution moments as conditioning information, which exhibits a strong inductive bias that allows it to generalize to unseen physics processes without explicitly assuming the underlying distribution. Our results highlight the potential of this method as a step towards a "universal" unfolding tool that reduces dependence on truth-level assumptions, while enabling the unfolding of a wide range of measured distributions with improved adaptability and accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01507 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Universal Unfolding of Detector Effects in High-Energy Physics using Denoising Diffusion Probabilistic Models Pazos, Camila Aeron, Shuchin Beauchemin, Pierre-Hugues Croft, Vincent Huan, Zhengyan Klassen, Martin Wongjirad, Taritree Data Analysis, Statistics and Probability High Energy Physics - Experiment High Energy Physics - Phenomenology Correcting for detector effects in experimental data, particularly through unfolding, is critical for enabling precision measurements in high-energy physics. However, traditional unfolding methods face challenges in scalability, flexibility, and dependence on simulations. We introduce a novel approach to multidimensional object-wise unfolding using conditional Denoising Diffusion Probabilistic Models (cDDPM). Our method utilizes the cDDPM for a non-iterative, flexible posterior sampling approach, incorporating distribution moments as conditioning information, which exhibits a strong inductive bias that allows it to generalize to unseen physics processes without explicitly assuming the underlying distribution. Our results highlight the potential of this method as a step towards a "universal" unfolding tool that reduces dependence on truth-level assumptions, while enabling the unfolding of a wide range of measured distributions with improved adaptability and accuracy. |
| title | Towards Universal Unfolding of Detector Effects in High-Energy Physics using Denoising Diffusion Probabilistic Models |
| topic | Data Analysis, Statistics and Probability High Energy Physics - Experiment High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2406.01507 |