https://doi.org/10.1140/epjp/s13360-024-05781-0
Regular Article
Gradient boosting MUST taggers for highly-boosted jets
1
Instituto de Física Teórica IFT UAM-CSIC, C/ Nicolás Cabrera 13-15, Campus de Cantoblanco, 28049, Madrid, Spain
2
IFLP, CONICET - Dpto. de Física, Universidad Nacional de La Plata, C.C. 67, 1900, La Plata, Argentina
3
Departamento de Física and CFTP, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais,1, 1049-001, Lisbon, Portugal
4
Departamento de Física Teórica, Universidad Autónoma de Madrid, 28049, Madrid, Spain
Received:
27
February
2024
Accepted:
25
October
2024
Published online:
24
November
2024
The Mass Unspecific Supervised Tagging (MUST) 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.
© The Author(s) 2024
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