Deep Image Reconstruction for Background Subtraction in Heavy-Ion Collisions
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918247250526208 |
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| author | Qureshi, Umar Sohail Elayavalli, Raghav Kunnawalkam |
| author_facet | Qureshi, Umar Sohail Elayavalli, Raghav Kunnawalkam |
| contents | Jet reconstruction in an ultra-relativistic heavy-ion collision suffers from a notoriously large, fluctuating thermal background. Traditional background subtraction methods struggle to remove this soft background while preserving the jet's hard substructure. In this Letter, we present DeepSub, the first machine learning-based approach for full-event background subtraction. DeepSub utilizes a model based on Swin Transformer layers to denoise jet images and disentangle hard jets from the heavy-ion background. DeepSub significantly outperforms existing subtraction techniques by reproducing key jet observables such as jet $p_\mathrm{T}$ and mass, and substructure observables such as girth and the energy correlation function, at the sub-percent to percent level. As such, DeepSub paves the way for precision heavy-ion measurements in hitherto inaccessible kinematic regimes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_14036 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Image Reconstruction for Background Subtraction in Heavy-Ion Collisions Qureshi, Umar Sohail Elayavalli, Raghav Kunnawalkam High Energy Physics - Phenomenology Nuclear Experiment Nuclear Theory Data Analysis, Statistics and Probability Jet reconstruction in an ultra-relativistic heavy-ion collision suffers from a notoriously large, fluctuating thermal background. Traditional background subtraction methods struggle to remove this soft background while preserving the jet's hard substructure. In this Letter, we present DeepSub, the first machine learning-based approach for full-event background subtraction. DeepSub utilizes a model based on Swin Transformer layers to denoise jet images and disentangle hard jets from the heavy-ion background. DeepSub significantly outperforms existing subtraction techniques by reproducing key jet observables such as jet $p_\mathrm{T}$ and mass, and substructure observables such as girth and the energy correlation function, at the sub-percent to percent level. As such, DeepSub paves the way for precision heavy-ion measurements in hitherto inaccessible kinematic regimes. |
| title | Deep Image Reconstruction for Background Subtraction in Heavy-Ion Collisions |
| topic | High Energy Physics - Phenomenology Nuclear Experiment Nuclear Theory Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2507.14036 |