Speaker
Description
R-parity violating (RPV) SUSY introduces a wide variety of couplings, making it essential to search without limiting target channels and cover signatures as broadly as possible. Among such signatures, multijet final states offer high inclusivity and are especially well-suited for model-independent searches targeting RPV SUSY scenarios.
In this study, we develop a signal discrimination method based on Classification Without Labels (CWoLa), a weakly supervised learning framework. While CWoLa has been successfully applied to dijet resonance searches, extending it to multijet events presents unique challenges. These include the variable number of jets in each event and the broader, less distinct mass peaks caused by reduced mass reconstruction resolution, which prevent direct application of existing techniques.
In this presentation, we propose a model architecture and a loss function that utilizes attention mechanisms to achieve permutation invariance, enabling the model to handle events with varying jet multiplicities naturally. Additionally, we show tailored training sample construction strategy designed to mitigate the specific difficulties of multijet events.
| Would you like to be considered for an oral presentation? | Yes |
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