Gradient boosting MUST taggers for highly-boosted jets
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 () 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 -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.
Authors
- F. R. Joaquim (ORCID: https://orcid.org/0000-0002-6711-4606)
- J. A. Aguilar–Saavedra (ORCID: https://orcid.org/0000-0002-5475-8920)
- Rosa María Sandá Seoane (ORCID: https://orcid.org/0000-0001-5768-2952)
- E. Arganda (ORCID: https://orcid.org/0000-0001-6943-469X)
- J. F. Seabra
Institutions
- University of Lisbon (PT)
- Consejo Nacional de Investigaciones Científicas y Técnicas (AR)
- Universidade Politécnica de Lisboa (PT)
- Instituto de Física Teórica (ES)
- Universidad Autónoma de Madrid (ES)
- Universidad Nacional de La Plata (AR)
Publication Details
- Journal
- The European Physical Journal Plus
- Published
- 2024-11-24
- DOI
- https://doi.org/10.1140/epjp/s13360-024-05781-0
- Citations
- 3
- Primary Topic
- Advanced Data Storage Technologies
- Type
- article
- Field-Weighted Citation Impact
- 1.05
Funders
- CERN
- Comunidad de Madrid
- Fundação para a Ciência e a Tecnologia
- Consejo Superior de Investigaciones Científicas
- Agencia Estatal de Investigación