Constraints on the trilinear and quartic Higgs couplings from triple Higgs production at the LHC and beyond
arXiv:2312.04646 · doi:10.1140/epjc/s10052-024-12722-9
Abstract
Experimental information on the trilinear Higgs boson self-coupling and the quartic self-coupling will be crucial for gaining insight into the shape of the Higgs potential and the nature of the electroweak phase transition. While Higgs pair production processes provide access to , triple Higgs production processes, despite their small cross sections, will provide valuable complementary information on and first experimental constraints on . We investigate triple Higgs boson production at the HL-LHC, employing efficient Graph Neural Network methodologies to maximise the statistical yield. We show that it will be possible to establish bounds on the variation of both couplings from the HL-LHC analyses that significantly go beyond the constraints from perturbative unitarity. We also discuss the prospects for the analysis of triple Higgs production at future high-energy lepton colliders operating at the TeV scale.
15 pages, 11 figures; v2: accepted by EPJC; v3: corrected sign typo in Eq. (4);
References in corpus (22)
- The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
- An Introduction to PYTHIA 8.2
- Automatic spin-entangled decays of heavy resonances in Monte Carlo simulations
- A standard format for Les Houches Event Files
- Robust Independent Validation of Experiment and Theory: Rivet version 3
- What is the scale of new physics behind the -flavour anomalies?
- Probing the Higgs self coupling via single Higgs production at the LHC
- Anomaly detection with Convolutional Graph Neural Networks
- Indirect probes of the trilinear Higgs coupling: and
- Measuring the quartic Higgs self-coupling at a multi-TeV muon collider
- MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks
- Scrutinizing the Higgs quartic coupling at a future 100 TeV proton-proton collider with taus and b-jets
- New constraints on extended Higgs sectors from the trilinear Higgs coupling
- Interpretable machine learning in Physics
- Probing triple Higgs coupling with machine learning at the LHC
- Topological Reconstruction of Particle Physics Processes using Graph Neural Networks
- Triple Higgs Boson Production at the Large Hadron Collider with Two Real Singlet Scalars
- Fast simulation of detector effects in Rivet
- Improved Constraints on Effective Top Quark Interactions using Edge Convolution Networks
- Testing anomalous couplings and Higgs self-couplings via double and triple Higgs production at colliders
- A Detailed Study of Interpretability of Deep Neural Network based Top Taggers
- Machine learning the trilinear and light-quark Yukawa couplings from Higgs pair kinematic shapes
Cited by in corpus (8)
- HHH Whitepaper
- Multi-Higgs Boson Production with Anomalous Interactions at Current and Future Proton Colliders
- A search for triple Higgs boson production in the final state using collisions at TeV with the ATLAS detector
- Probing the Inert Doublet Model via Vector-Boson Fusion at a Muon Collider
- High Energy Vector Boson Scattering in Four-Body Final States to Probe Higgs Cubic, Quartic, and HEFT interactions
- Extracting Higgs Self-Coupling Constraints through Triple Higgs Boson Production at Future Hadron Colliders
- Foundations of automatic feature extraction at LHC--point clouds and graphs
- Constraining the Higgs potential using multi-Higgs production