Research Group: Particle Physics Research Centre Number of Students: 1 Length of Study in Years: 4 years Full-time Project: yes
Funding is provided via the China Scholarship Council.
The Large Hadron Collider is the world’s highest energy collider reaching collisions at 13 TeV. The high energy allows us to search this unexplored region for signals of new physics. In this project a novel machine learning approach will be used to search for new physics effects beyond the 13 TeV. The project will make a high precision measurement the rate of quark and anti-quark annihilation to form a Z boson decaying to leptons as a function of the collision energy. The data will use the largest LHC data set (Run-3) which will complete data taking in spring 2026.
These measurements will be analysed using advanced statistical machine learning methods in the Effective Field Theory (EFT) formalism, and state-of-the-art theoretical predictions. In addition the impact of the parton density functions will be fully included in the analysis for the first time.
This project builds on earlier measurements made by Prof Rizvi (JHEP 1608 (2016) 009, and two analyses currently in preparation) and will extend the energy range and the physics scope of the EFT determination. The project will either succeed in finding a signal for new physics, or set the world’s best constraint on some of the EFT operators. The result will lead to a high impact journal publication that will not be superseded for over a decade.
A comprehensive training programme is offered involving 150 hours of lecture courses in particle physics and detection methods, a 2-weeek residential summer school, and attendance at an international conference to present your work towards the end of the PhD. Students may also participate in the DISCnet training programme in machine learning, or have the opportunity to spend 6-12 months based at CERN, in Geneva depending on funding.
The project will be supervised by Prof Rizvi who has an established expertise in machine learning and operating the Level-1 Calorimeter Trigger. He leads the ATLAS group at Queen Mary University of London, and has successfully supervised 15 PhD students, and currently leads two Centres for Doctoral Training in Data-Centric Engineering and in Data-Intensive Science for Particle Physics. He is a Fellow of the Alan Turing Institute – the national centre for Artificial Intelligence.
Supervisor Contact Details:
For informal enquiries about this position, please contact: Prof Eram Rizvi:
E-mail: e.rizvi@qmul.ac.uk
Application Method:
To apply for this studentship please select September entry (Full Time) and follow the instructions detailed on the following webpage:
https://www.qmul.ac.uk/postgraduate/research/subjects/physics.html
Deadline for applications: 29th of January 2025
SPCS Academics: Prof. Eram Rizvi