Gradient Boosting MUST taggers for highly-boosted jets

TL;DR

This paper introduces the use of XGBoost classifiers for the MUST jet tagging method, demonstrating that they are faster and easier to optimize than neural networks while maintaining similar performance in discriminating complex jet signals.

Contribution

The paper replaces neural networks with XGBoost classifiers in the MUST jet tagging framework, showing improved efficiency and comparable accuracy for generic and specific multi-pronged jet identification.

Findings

XGBoost-based taggers are faster and easier to optimize than neural network-based ones.

XGBoost taggers perform similarly to neural networks on unseen signals.

The method effectively discriminates multi-pronged signals from QCD background.

Abstract

The MUST (Mass Unspecific Supervised Tagging) method has proven to be successful in implementing generic jet taggers capable of discriminating various signals over a wide range of jet masses. We implement the MUST concept by using eXtreme Gradient Boosting (XGBoost) classifiers instead of neural networks (NNs) as previously done. We build both fully-generic and specific multi-pronged taggers, to identify 2, 3, and/or 4-pronged signals from SM QCD background. We show that XGBoost-based taggers are not only easier to optimize and much faster than those based in NNs, but also show quite similar performance, even when testing with signals not used in training. Therefore, they provide a quite efficient alternative machine-learning implementation for generic jet taggers.