Boosting sensitivity to new physics with unsupervised anomaly detection in dijet resonance search

Enhancing dijet resonance searches, crucial for uncovering new physics at hadron colliders, poses challenges with increasing luminosities. Traditional methods struggle to capture complex backgrounds accurately. We propose an innovative approach utilizing unsupervised anomaly detection. By filtering out background-related events, this technique enhances sensitivity to potential signals. Simulations demonstrate improved performance over conventional methods. Our findings open doors for more effective searches for new physics in high-energy collider experiments.