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Grant support

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.

Impact on the Sustainable Development Goals (SDGs)

Analysis of institutional authors

Aguilar-Saavedra, J ACorresponding AuthorArganda, EAuthorSeoane, R M SandaAuthor

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December 9, 2024
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Article
Hybrid Gold

Gradient boosting MUST taggers for highly-boosted jets

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

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

Affiliations

UAM, CSIC, Inst Fis Teor IFT, C Nicolas Cabrera 13-15,Campus Cantoblanco, Madrid 28049, Spain - Author
Univ Autonoma Madrid, Dept Fis Teor, Madrid 28049, Spain - Author
Univ Lisbon, CFTP, Inst Super Tecn, Ave Rovisco Pais 1, P-1049001 Lisbon, Portugal - Author
Univ Lisbon, Dept Fis, Inst Super Tecn, Ave Rovisco Pais 1, P-1049001 Lisbon, Portugal - Author
Univ Nacl La Plata, Dept Fis, IFLP, CONICET, CC 67, RA-1900 La Plata, Argentina - Author
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Abstract

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.

Keywords

Reduced inequalities

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal European Physical Journal Plus due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 32/112, thus managing to position itself as a Q2 (Segundo Cuartil), in the category Physics, Multidisciplinary. Notably, the journal is positioned en el Cuartil Q2 para la agencia Scopus (SJR) en la categoría Fluid Flow and Transfer Processes.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-08-29:

  • Open Alex: 1

Impact and social visibility

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://repositorio.uam.es/handle/10486/718767
Continuing with the social impact of the work, it is important to emphasize that, due to its content, it can be assigned to the area of interest of ODS 10 - Reduce inequality within and among countries, with a probability of 71% according to the mBERT algorithm developed by Aurora University.

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Argentina; Portugal.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (AGUILAR SAAVEDRA, JUAN ANTONIO) and Last Author (Seabra, J F).

the author responsible for correspondence tasks has been AGUILAR SAAVEDRA, JUAN ANTONIO.