The Large Hadron Collider (LHC) situated at CERN in Geneva, Switzerland is the highest energy particle collider ever build. It produces both collisions between protons and between heavy ions. The LHC has so far discovered a Higgs boson and produced a plethora of searches for beyond Standard Model physics, which have transformed our understanding of how new physics may be realized. Data-taking will continue and the forthcoming data will provide more opportunities to explore new physics.
Axions are particles that were postulated to solve the “strong CP problem”. However, axion-like particles (ALPs) appear in many beyond Standard Model theories. Their signature couplings are to photons and their mass is unconstrained. The LHC is currently breaking new ground in acessing previously unexplored ALP models using both proton—proton and Pb—Pb collisions.
This project is about searching with ALPs using ultra-peripheral Pb—Pb collision (UPC) data recorded with the ATLAS detector at the LHC. In these collisions2, the heavy ions interact electromagnetically without breaking up. They are ideal for the study of photon—photon produced resonances, like ALPs produced by the interaction of two photons. The ALPs then decay to photon—photon pair that can be recorded in the detector. Photon reconstruction and identification in ATLAS has not been optimized for the UPC environment and this opens an excellent opportunity to develop new techniques. A major improvement in the sensitivity of this analysis is expected by studying a photon reconstruction and identification using image analysis with deep neural networks, which will allow us to reconstruct photons with lower transverse momentum and identify them better against backgrounds from electrons or neutral pions than it is now possible using the current algorithms. These techniques combined with the forthcoming LHC heavy ion datasets will enable us to achieve the best possible sensitivity to ALPs leading to a discovery or a most stringent upper limit on their production.
Throughout the project you will have targeted training in data science provided by the University of Liverpool with the Centre for Doctoral Training LIV.INNO. You will also be given the opportunity to carry out an industry placement of six months to broaden your wider research and career skills.
This project will be carried out over 48 months based at the University of Liverpool but you have the opportunity to spend up to year in CERN. Whilst in the UK, a standard RKUK PhD stipend will be paid, during the time at CERN. A mandatory 6-months industry placement forms part of the project.
Applicants are required to have a 2:1 Master degree or an equivalent 4-year degree in Physics, Engineering or Maths and some background in particle physics. The position under LIVINNO, an STFC and University of Liverpool partnership for doctoral training and it is open for both UK and international applicants, although quota apply for the offers we can make to international students.
https://www.liverpool.ac.uk/study/postgraduate-research/how-to-apply/. Please ensure you state Dr Rompotis and Prof D’Onofrio as the proposed supervisors on your application form and quote studentship reference: PPPR052.