2011/12/01 by Natascia Vignaroli, Vignaroli, Natascia · 1 citation
Physics and Astronomy · #FOS: Physical sciences #High Energy Physics - Phenomenology (hep-ph) #High-Energy Particle Collisions Research #Particle physics theoretical and experimental studies #Quantum Chromodynamics and Particle Interactions
paper · pdf · doi:10.48550/arxiv.1112.0218
openalex publication_date 2011/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this project we study the phenomenology of models where the Higgs is a\nbound state of a strongly interacting dynamics at the TeV scale and we assess\nthe LHC potential to discover new heavy colored states. In the first part of\nthe thesis we analyze the bounds on the spectrum of Composite Higgs Models\n(CHM) that come from flavor observables. In the second part we propose a novel\nstrategy to discover a heavy gluon (G^*) and heavy fermions at the LHC. We do\nso by means of simple two-site effective Lagrangians, which could also be used\nin further phenomenological studies on CHM. From the analysis of the bounds on\nthe CHM spectrum, we derive an important constraint on the masses of the heavy\nfermions which does not depend on the flavor structure of the sector beyond the\nSM. This bound is obtained from the infrared contribution to b \→ s\γ\ninduced by the flavor-conserving effective vertex WtRbR. We find that the\npresence of a custodial symmetry can play a role in protecting this effective\ncoupling. Studying the heavy colored vectors phenomenology, we find that heavy\ncomposite fermions have a great impact on the phenomenology of heavy composite\ngluon at the LHC. If the composite gluon is heavier than the composite\nfermions, as flavor observables seem to suggest, the search in the channels\nwhere G^* decays into one heavy fermion plus its Standard Model partner is\nvery promising, with the possibility for both the G^* and heavy fermions to\nbe discovered at the early stage of the LHC. These channels offer also the\npossibility to extract important information on model's parameters, such as the\ntop degree of compositeness.\n