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EA and RMSS would like to thank Diego Mourino for his collaboration with code. This work is partially supported by the "Atraccion de Talento" program (Modalidad 1) of the Comunidad de Madrid (Spain) under the grant number 2019-T1/TIC-14019 (EA, RMSS), by the Spanish Research Agency (Agencia Estatal de Investigacion) through the Grant IFT Centro de Excelencia Severo Ochoa No CEX2020-001007-S (JAAS, EA, RMSS) and by the grants PID2019-110058GB-C21, PID2022-142545NB-C21 (JAAS) and PID2021-124704NB-I00 (EA, RMSS) funded by MCIN/AEI/10.13039/501100011033. FRJ and JFS acknowledge financial support from Fundacao para a Ciencia e a Tecnologia (FCT, Portugal) through the projects CFTP-FCT Unit (UIDB/00777/2020, UIDP/00777/2020) and CERN/FIS-PAR/0019/2021, which are partially funded through POCTI (FEDER), COMPETE, QREN, and EU. The work of J.F.S. is supported by the FCT grant SFRH/BD/143891/ 2019.

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Gradient boosting MUST taggers for highly-boosted jets

Publicado en:European Physical Journal Plus. 139 (11): 1019- - 2024-11-24 139(11), DOI: 10.1140/epjp/s13360-024-05781-0

Autores: Aguilar-Saavedra, J.A.; Arganda, E.; Joaquim, F.R.; Seoane, R.M.S.; Seabra, J.F.

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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.

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