Orateur
Description
In this study, we explore the capabilities of Non-Resonant Anomaly Detection techniques, Reweight and Generate, for identifying semi-visible jets (SVJ) within the Hidden Valley (HV) dark sector model. Using simulated events generated from PYTHIA, MadGraph5, and Delphes, we trained a Boosted Decision Tree (BDT) to select optimal features for distinguishing the signal from QCD backgrounds. With an AUC value of $0.998$, the features selected are $H_T$, MET, $m_{jj}$, and $N$-subjettiness ratios ($\tau_{21}$ and $\tau_{32}$). Our results confirm that both Reweight and Generate provide reliable background extrapolation, as validated by Wasserstein distance metrics. Additionally, the Reweight method improves the detection significance from $2.6\sigma$ to approximately $5\sigma$, demonstrating its potential for enhancing sensitivity in non-resonant searches.
