| Title: |
Gradient boosting MUST taggers for highly-boosted jets |
| Authors: |
Aguilar-Saavedra, J. A.; Arganda, E.; Joaquim, F.R.; Sandá Seoane, R.M.; Seabra, J.F. |
| Contributors: |
Departamento de Física Teórica; Facultad de Ciencias |
| Publisher Information: |
Springer; EDP Sciences |
| Publication Year: |
2025 |
| Collection: |
Universidad Autónoma de Madrid (UAM): Biblos-e Archivo |
| Subject Terms: |
Quantum chromodynamics; neural network; physics; Física |
| Description: |
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 ; This work is partially supported by the \u201CAtracci\u00F3n de Talento\u201D 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 Investigaci\u00F3n) 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. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
European Physical Journal Plus; https://doi.org/10.1140/epjp/s13360-024-05781-0; Gobierno de España. PID2019-110058GB; Gobierno de España. PID2022-142545NB; Gobierno de España. PID2021-124704NB; Gobierno de España. CEX2020-001007-S; European Physical Journal Plus 139.11 (2024): 1019; https://hdl.handle.net/10486/718767; 1019; 11; 139 |
| DOI: |
10.1140/epjp/s13360-024-05781-0 |
| Availability: |
https://hdl.handle.net/10486/718767; https://doi.org/10.1140/epjp/s13360-024-05781-0 |
| Rights: |
© The Author(s) 2024 ; http://creativecommons.org/licenses/by/4.0/ ; Reconocimiento ; open access |
| Accession Number: |
edsbas.29D9CAB6 |
| Database: |
BASE |