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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/2410.22074 |
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| _version_ | 1866909702875512832 |
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| author | Algren, Malte Golling, Tobias Pollard, Christopher Raine, John Andrew |
| author_facet | Algren, Malte Golling, Tobias Pollard, Christopher Raine, John Andrew |
| contents | In this paper, we present a novel method for pile-up removal of $pp$ interactions using variational inference with diffusion models, called vipr. Instead of using classification methods to identify which particles are from the primary collision, a generative model is trained to predict the constituents of the hard-scatter particle jets with pile-up removed. This results in an estimate of the full posterior over hard-scatter jet constituents, which has not yet been explored in the context of pile-up removal, yielding a clear advantage over existing methods especially in the presence of imperfect detector efficiency. We evaluate the performance of vipr in a sample of jets from simulated $t\bar{t}$ events overlain with pile-up contamination. vipr outperforms softdrop and has comparable performance to puppiml in predicting the substructure of the hard-scatter jets over a wide range of pile-up scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_22074 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Variational inference for pile-up removal at hadron colliders with diffusion models Algren, Malte Golling, Tobias Pollard, Christopher Raine, John Andrew High Energy Physics - Phenomenology Machine Learning In this paper, we present a novel method for pile-up removal of $pp$ interactions using variational inference with diffusion models, called vipr. Instead of using classification methods to identify which particles are from the primary collision, a generative model is trained to predict the constituents of the hard-scatter particle jets with pile-up removed. This results in an estimate of the full posterior over hard-scatter jet constituents, which has not yet been explored in the context of pile-up removal, yielding a clear advantage over existing methods especially in the presence of imperfect detector efficiency. We evaluate the performance of vipr in a sample of jets from simulated $t\bar{t}$ events overlain with pile-up contamination. vipr outperforms softdrop and has comparable performance to puppiml in predicting the substructure of the hard-scatter jets over a wide range of pile-up scenarios. |
| title | Variational inference for pile-up removal at hadron colliders with diffusion models |
| topic | High Energy Physics - Phenomenology Machine Learning |
| url | https://arxiv.org/abs/2410.22074 |