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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/2411.04526 |
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| _version_ | 1866916576903561216 |
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| author | Cornell, Alan S. Fuks, Benjamin Goodsell, Mark D. Ncube, Anele M. |
| author_facet | Cornell, Alan S. Fuks, Benjamin Goodsell, Mark D. Ncube, Anele M. |
| contents | We demonstrate that neural networks can be used to improve search strategies, over existing strategies, in LHC searches for light electroweak-charged scalars that decay to a muon and a heavy invisible fermion. We propose a new search involving a neural network discriminator as a final cut and show that different signal regions can be defined using networks trained on different subsets of signal samples (distinguishing low-mass and high-mass regions). We also present a workflow using publicly-available analysis tools, that can lead, from background and signal simulation, to network training, through to finding projections for limits using an analysis and ${\tt ONNX}$ libraries to interface network and recasting tools. We provide an estimate of the sensitivity of our search from Run 2 LHC data, and projections for higher luminosities, showing a clear advantage over previous methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_04526 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving smuon searches with Neural Networks Cornell, Alan S. Fuks, Benjamin Goodsell, Mark D. Ncube, Anele M. High Energy Physics - Phenomenology High Energy Physics - Experiment We demonstrate that neural networks can be used to improve search strategies, over existing strategies, in LHC searches for light electroweak-charged scalars that decay to a muon and a heavy invisible fermion. We propose a new search involving a neural network discriminator as a final cut and show that different signal regions can be defined using networks trained on different subsets of signal samples (distinguishing low-mass and high-mass regions). We also present a workflow using publicly-available analysis tools, that can lead, from background and signal simulation, to network training, through to finding projections for limits using an analysis and ${\tt ONNX}$ libraries to interface network and recasting tools. We provide an estimate of the sensitivity of our search from Run 2 LHC data, and projections for higher luminosities, showing a clear advantage over previous methods. |
| title | Improving smuon searches with Neural Networks |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2411.04526 |