Speaker
Description
Searches for new resonances at hadron colliders are typically optimized for benchmark models and high transverse momentum final states in order to suppress large Standard Model backgrounds. However, potential signals of new physics may appear in less explored regions of phase space where traditional analyses may have reduced sensitivity. This project investigates the use weak supervision in resonant searches on the $\gamma \gamma + 1\tau$ final state in a phase space of reduced hadronic activity, with a lowered jet jet $p_T$ threshold of $10 \: GeV$, as opposed to a space of high $p_T$ objects. To identify potential signals without relying on event-level truth labels, a guided weakly supervised machine learning approach based on the is employed. In this method, a classifier is trained to distinguish between background and a mixed sample of signal and background, allowing discriminating features of potential new physics signals to be learned. A Graph Neural Network (GNN) is employed as the deep learning model, as its object-based representation and permutation-invariant architecture naturally accommodate the variable jet multiplicity and complex event topology characteristic of hadron collider final states. Crucially, the guidance for weak label construction is derived from the di-photon invariant mass regions, defined by a signal window of $144$-$156 \: GeV$ and sidebands spanning $132$-$168 \: GeV$, excluding the signal region. This study aims to assess the potential of weak supervision techniques to improve sensitivity to subtle signals that may reside in previously under-explored regions of phase space in collider data.
| Apply for student award at which level: | MSc |
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| Consent on use of personal information: Abstract Submission | Yes, I ACCEPT |