ATLAS Upgrade

Lead Research Organisation: Queen Mary, University of London
Department Name: Physics

Abstract

The objective of this project is to construct elements of the ATLAS Phase-II Upgrade for operation at the High-Luminosity Large Hadron Collider (HL-LHC). The increased luminosity delivered, around a factor of 10 more than the will be delivered by 2023, by the HL-LHC to ATLAS will significantly enhance the physics programme. The Large dataset collected will allow ATLAS to make precision studies of the Higgs sector, the Standard Model of particle physics, and to extend the mass scales accessible to searches for signatures of new physics well into the TeV region. This will allow ATLAS to address many of the fundamental questions in particle physics:

Is the Higgs mechanism for generating fermion and weak gauge boson masses linked, as is implied by the Standard Model?

Does the Higgs mechanism limit the cross-section of vector boson scattering in the Standard Model at high energies, or is there new physics involved in electroweak symmetry breaking?

Why is the Higgs boson much lighter than the Planck Mass (the hierarchy problem)?

Is there a weakly interacting particle with mass of the order of the electroweak scale that could explain dark matter?

Is supersymmetry (SUSY), a symmetry between bosons and fermions, realised in nature?

Planned Impact

See Glasgow for main submission

Publications

10 25 50
 
Description Consolidated Grant 2019
Amount £2,102,510 (GBP)
Funding ID ST/S00095X/1 
Organisation Science and Technologies Facilities Council (STFC) 
Sector Public
Country United Kingdom
Start 09/2019 
End 09/2022
 
Description ATLAS Collaboration 
Organisation European Organization for Nuclear Research (CERN)
Country Switzerland 
Sector Academic/University 
PI Contribution The ATLAS Collaboration at CERN, including R&D toward upgrades of the CERN LHC Facility
Collaborator Contribution International laboratory hosting an experiment
Impact These include the discovery of the Higgs boson, many publications and preliminary results, machine learning technology integrated into teaching resources for undergraduate and graduate students.