2022/01/26 by J. A. Aguilar–Saavedra, J. A. Aguilar-Saavedra · 14 citations
Computer Science · Engineering · Physics and Astronomy · #Advanced Data Storage Technologies #Algorithms and Data Compression #Artificial intelligence #Computer science #Engineering #Jet (fluid) #Natural language processing #Particle physics #Particle physics theoretical and experimental studies #Parton #Pattern recognition (psychology) #Physics #Quantum chromodynamics #Substructure #hep-ph
paper · pdf · open access · doi:10.1140/epjc/s10052-022-10221-3
published in The European Physical Journal C 82(3) (Springer Science+Business Media) · LaTeX 11 pages, lots of plots
arxiv created 2022/01/26 · openalex publication_date 2022/03/01 · arxiv updated 2022/04/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We address the modeling dependence of jet taggers built using the method of Mass Unspecific Supervised Tagging, by using two different parton showering and hadronisation schemes. We find that the modeling dependence of the results - estimated by using different schemes in the design of the taggers and applying them to the same type of data - is rather small, even if the jet substructure varies significantly between the two schemes. These results add great value to the use of generic supervised taggers for new physics searches.