Robust resonant anomaly detection with NPLM

TL;DR

This paper explores the use of the New Physics Learning Machine (NPLM) as a robust alternative to traditional methods for detecting rare signals in particle physics, demonstrating improved performance and reduced variance.

Contribution

It introduces NPLM as a novel end-to-end anomaly detection method that outperforms BDT-based approaches, especially in low signal scenarios, and reduces hyperparameter sensitivity.

Findings

NPLM outperforms BDT in detection accuracy for rare signals.

NPLM reduces variance caused by hyperparameter choices.

NPLM enhances robustness in resonant anomaly detection.

Abstract

In this study, we investigate the application of the New Physics Learning Machine (NPLM) algorithm as an alternative to the standard CWoLa method with Boosted Decision Trees (BDTs), particularly for scenarios with rare signal events. NPLM offers an end-to-end approach to anomaly detection and hypothesis testing by utilizing an in-sample evaluation of a binary classifier to estimate a log-density ratio, which can improve detection performance without prior assumptions on the signal model. We examine two approaches: (1) a end-to-end NPLM application in cases with reliable background modelling and (2) an NPLM-based classifier used for signal selection when accurate background modelling is unavailable, with subsequent performance enhancement through a hyper-test on multiple values of the selection threshold. Our findings show that NPLM-based methods outperform BDT-based approaches in detection performance, particularly in low signal injection scenarios, while significantly reducing epistemic variance due to hyperparameter choices. This work highlights the potential of NPLM for robust resonant anomaly detection in particle physics, setting a foundation for future methods that enhance sensitivity and consistency under signal variability.