Interpretable Machine Learning Methods Applied to Jet Background Subtraction in Heavy Ion Collisions
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916152719966208 |
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| author | Mengel, Tanner Steffanic, Patrick Hughes, Charles da Silva, Antonio Carlos Oliveira Nattrass, Christine |
| author_facet | Mengel, Tanner Steffanic, Patrick Hughes, Charles da Silva, Antonio Carlos Oliveira Nattrass, Christine |
| contents | Jet measurements in heavy ion collisions can provide constraints on the properties of the quark gluon plasma, but the kinematic reach is limited by a large, fluctuating background. We present a novel application of symbolic regression to extract a functional representation of a deep neural network trained to subtract the background for measurements of jets in relativistic heavy ion collisions. We show that the deep neural network is approximately the same as a method using the particle multiplicity in a jet. This demonstrates that interpretable machine learning methods can provide insight into underlying physical processes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_08275 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Interpretable Machine Learning Methods Applied to Jet Background Subtraction in Heavy Ion Collisions Mengel, Tanner Steffanic, Patrick Hughes, Charles da Silva, Antonio Carlos Oliveira Nattrass, Christine High Energy Physics - Experiment Nuclear Experiment Jet measurements in heavy ion collisions can provide constraints on the properties of the quark gluon plasma, but the kinematic reach is limited by a large, fluctuating background. We present a novel application of symbolic regression to extract a functional representation of a deep neural network trained to subtract the background for measurements of jets in relativistic heavy ion collisions. We show that the deep neural network is approximately the same as a method using the particle multiplicity in a jet. This demonstrates that interpretable machine learning methods can provide insight into underlying physical processes. |
| title | Interpretable Machine Learning Methods Applied to Jet Background Subtraction in Heavy Ion Collisions |
| topic | High Energy Physics - Experiment Nuclear Experiment |
| url | https://arxiv.org/abs/2303.08275 |