Abstract

We assess the performance of different jet-clustering algorithms, in the presence of different resolution parameters and reconstruction procedures, in resolving fully hadronic final states emerging from the chain decay of the discovered Higgs boson into pairs of new identical Higgs states, the latter in turn decaying into bottom-antibottom quark pairs. We show that, at the large hadron collider (LHC), both the efficiency of selecting the multi-jet final state and the ability to reconstruct from it the masses of the Higgs bosons (potentially) present in an event sample depend strongly on the choice of acceptance cuts, jet-clustering algorithm as well as its settings. Hence, we indicate the optimal choice of the latter for the purpose of establishing such a benchmark Beyond the SM (BSM) signal. We then repeat the exercise for a heavy Higgs boson cascading into two SM-like Higgs states, obtaining similar results.

Details

Title
Revisiting jet clustering algorithms for new Higgs Boson searches in hadronic final states
Author
Chakraborty, A 1 ; Dasmahapatra, S 2 ; Day-Hall, H A 3 ; Ford, B G 3 ; Jain, S 3 ; Moretti, S 4 ; Olaiya, E 5 ; Shepherd-Themistocleous, C H 5 

 SRM University AP, Department of Physics, School of Engineering and Sciences, Amaravati, India 
 University of Southampton, School of Electronics and Computer Science, Southampton, UK (GRID:grid.5491.9) (ISNI:0000 0004 1936 9297) 
 University of Southampton, School of Physics and Astronomy, Southampton, UK (GRID:grid.5491.9) (ISNI:0000 0004 1936 9297) 
 University of Southampton, School of Physics and Astronomy, Southampton, UK (GRID:grid.5491.9) (ISNI:0000 0004 1936 9297); Uppsala University, Department of Physics and Astronomy, Uppsala, Sweden (GRID:grid.8993.b) (ISNI:0000 0004 1936 9457) 
 Rutherford Appleton Laboratory, Particle Physics Department, Didcot, UK (GRID:grid.76978.37) (ISNI:0000 0001 2296 6998) 
Publication year
2022
Publication date
Apr 2022
Publisher
Springer Nature B.V.
ISSN
14346044
e-ISSN
14346052
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2652732409
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/. (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.