Uncovering doubly charged scalars with dominant three-body decays using machine learning
We propose a deep learning-based search strategy for pair production of doubly charged scalars undergoing three-body decays to \(W^+ t\bar b\) in the same-sign lepton plus multi-jet final state. This process is motivated by composite Higgs models with an underlying fermionic UV theory. We demonstrat...
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| creator | Flacke, Thomas Jeong Han Kim Kunkel, Manuel Ko, Pyungwon Pi, Jun Seung Porod, Werner Schwarze, Leonard |
| description | We propose a deep learning-based search strategy for pair production of doubly charged scalars undergoing three-body decays to \(W^+ t\bar b\) in the same-sign lepton plus multi-jet final state. This process is motivated by composite Higgs models with an underlying fermionic UV theory. We demonstrate that for such busy final states, jet image classification with convolutional neural networks outperforms standard fully connected networks acting on reconstructed kinematic variables. We derive the expected discovery reach and exclusion limit at the high-luminosity LHC. |
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| subjects | Artificial neural networks Deep learning Image classification Kinematics Leptons Luminosity Machine learning Pair production Scalars |
| title | Uncovering doubly charged scalars with dominant three-body decays using machine learning |
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