Back To The Roots: Tree-Based Algorithms for Weakly Supervised Anomaly Detection

Understand

Weakly supervised methods have emerged as a powerful tool for model-agnostic anomaly detection at the Large Hadron Collider (LHC).

• While these methods have shown remarkable performance on specific signatures such as di-jet resonances, their application in a more model-agnostic manner requires dealing with a larger number of potentially noisy input features.
• In this paper, we show that using boosted decision trees as classifiers in weakly supervised anomaly detection gives superior performance compared to deep neural networks.
• Boosted decision trees are well known for their effectiveness in tabular data analysis.