Deep learning searches for vector-like leptons at the LHC and electron/muon colliders

Abstract

The discovery potential of both singlet and doublet vector-like leptons (VLLs) at the Large Hadron Collider (LHC) as well as at the not-so-far future muon and electron machines is explored. The focus is on a single production channel for LHC direct searches while double production signatures are proposed for the leptonic colliders. A Deep Learning algorithm to determine the discovery (or exclusion) statistical significance at the LHC is employed. While doublet VLLs can be probed up to masses of 1 TeV, their singlet counterparts have very low cross sections and can hardly be tested beyond a few hundreds of GeV at the LHC. This motivates a physics-case analysis in the context of leptonic colliders where one obtains larger cross sections in VLL double production channels, allowing to probe higher mass regimes otherwise inaccessible even to the LHC high-luminosity upgrade.

Collider signatures of a scalar leptoquark and vectorlike lepton in light of muon anomaly

“…We note that there have been recent works on leptoquark [43][44][45][46][47] and VLL [24,48] searches in a muon collider. In our study, we perform a search for leptoquarks in the 2µ + 2b + / E T final state, extending our earlier study in the context of LHC in Ref.…”

Calibração de Energia para a Seleção Online de Elétrons com Alta Taxa e Utilizando um Conjunto de árvores de Decisão com Reforço por Gradiente e Informação Especialista de Calorimetria

“…Os sistemas de filtragem online são projetados para preservar a informac ¸ão dos eventos raros e de interesse e rejeitar o ruído de fundo, ainda que em algumas situac ¸ões as assinaturas das diferentes classes sejam semelhantes. A fim de lidar com essa dificuldade, técnicas de aprendizado de máquina têm sido empregadas em várias aplicac ¸ões relacionadas à detecc ¸ão de partículas [3], [4], reconstruc ¸ão de eventos [5] e filtragem online [6]. Essas técnicas permitem que os sistemas de classificac ¸ão identifiquem padrões e características relevantes nos sinais coletados, melhorando assim a capacidade de separar os eventos de interesse do ruído de fundo.…”