Searches for new physics with boosted top quarks in the MadAnalysis 5 and Rivet frameworks
arXiv:2303.03427 · doi:10.1140/epjc/s10052-023-11779-2
Abstract
High-momentum top quarks are a natural physical system in collider experiments for testing models of new physics, and jet substructure methods are key both to exploiting their largest decay mode and to assuaging resolution difficulties as the boosted system becomes increasingly collimated in the detector. To be used in new-physics interpretation studies, it is crucial that related methods get implemented in analysis frameworks allowing for the reinterpretation of the results of the LHC such as MadAnalysis 5 and Rivet. We describe the implementation of the HEPTopTagger algorithm in these two frameworks, and we exemplify the usage of the resulting functionalities to explore the sensitivity of boosted top reconstruction performance to new physics contributions from the Standard Model Effective Field Theory. The results of this study lead to important conclusions about the implicit assumption of Standard-Model-like top-quark decays in associated collider analyses, and for the prospects to constrain the Standard Model Effective Field Theory via kinematic observables built from boosted semileptonic top-antitop events selected using HEPTopTagger.
arXiv comment19 pages, 5 figures; version accepted by EPJC
References in corpus (57)
- The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
- The anti-k_t jet clustering algorithm
- An Introduction to PYTHIA 8.2
- FastJet user manual
- FeynRules 2.0 - A complete toolbox for tree-level phenomenology
- Parton distributions for the LHC Run II
- Dimension-Six Terms in the Standard Model Lagrangian
- LHAPDF6: parton density access in the LHC precision era
- Minimal Flavour Violation: an effective field theory approach
- UFO - The Universal FeynRules Output
- Better Jet Clustering Algorithms
- Jet substructure as a new Higgs search channel at the LHC
- Pileup subtraction using jet areas
- Identifying Boosted Objects with N-subjettiness
- The Catchment Area of Jets
- MadAnalysis 5, a user-friendly framework for collider phenomenology
- Jet Trimming
- Rivet user manual
- Towards an understanding of jet substructure
- Top-tagging: A Method for Identifying Boosted Hadronic Tops
- Energy Correlation Functions for Jet Substructure
- Maximizing Boosted Top Identification by Minimizing N-subjettiness
- Boosted objects: a probe of beyond the Standard Model physics
- Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning
- Recombination Algorithms and Jet Substructure: Pruning as a Tool for Heavy Particle Searches
- Robust Independent Validation of Experiment and Theory: Rivet version 3
- Techniques for improved heavy particle searches with jet substructure
- Jet Substructure at the Large Hadron Collider: Experimental Review
- Stop Reconstruction with Tagged Tops
- Jet-Images -- Deep Learning Edition
- Deep-learning Top Taggers or The End of QCD?
- Strategies to Identify Boosted Tops
- Jet Substructure Classification in High-Energy Physics with Deep Neural Networks
- Designing and recasting LHC analyses with MadAnalysis 5
- The Machine Learning Landscape of Top Taggers
- Top, Higgs, Diboson and Electroweak Fit to the Standard Model Effective Field Theory
- Power Counting to Better Jet Observables
- A comprehensive approach to new physics simulations
- A Monte Carlo global analysis of the Standard Model Effective Field Theory: the top quark sector
- O new physics, where art thou? A global search in the top sector
- Jet-Images: Computer Vision Inspired Techniques for Jet Tagging
- Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
- Confronting new physics theories to LHC data with MadAnalysis 5
- Constraining top quark effective theory in the LHC Run II era
- Finding top quarks with shower deconstruction
- Pulling Out All the Tops with Computer Vision and Deep Learning
- An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging
- Performance of top-quark and -boson tagging with ATLAS in Run 2 of the LHC
- Jets with Variable R
- Simplified fast detector simulation in MadAnalysis 5
- Pinning down top dipole moments with ultra-boosted tops
- The Cambridge Jet algorithm: features and applications
- Giving top quark effective operators a boost
- Combine and Conquer: Event Reconstruction with Bayesian Ensemble Neural Networks
- Fast simulation of detector effects in Rivet
- Telescoping Jets: Multiple Event Interpretations with Multiple R's
- On Statistical Aspects of Qjets