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.
Our results show that they not only offer significantly faster training and
evaluation times, but they are also robust to a large number of noisy input
features. By using advanced gradient boosted decision trees in combination with
ensembling techniques and an extended set of features, we significantly improve
the performance of weakly supervised methods for anomaly detection at the LHC.
This advance is a crucial step towards a more model-agnostic search for new
physics.Comment: 11 pages, 9 figure