FREE Virtual Study Fair | 1 - 2 March | REGISTER NOW FREE Virtual Study Fair | 1 - 2 March | REGISTER NOW

High Precision Search for New Physics at the Large Hadron Collider using Machine Learning Unfolding


   School of Physical and Chemical Sciences

This project is no longer listed on FindAPhD.com and may not be available.

Click here to search FindAPhD.com for PhD studentship opportunities
  Prof Eram Rizvi  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

The search for new physics is intensifying globally. Recent publications from a range of experiments have indicated increasing tension with the predictions of the Standard Model of Particle Physics, including measurements of the W particle mass, lepton flavour violation, and the magnetic moment of the muon. However no “smoking-gun” signatures of new particles have been found. It is increasingly likely that any new particles have masses beyond the direct reach of the LHC, and can only be indirectly observed through lower energy quantum fluctuations of well-known particle interactions.

In this project a highly precise series of measurements of the interactions and decays of the W and Z bosons will be performed. These interactions are targeted for their clean and simple detector signatures, leading to high experimental accuracy. Correspondingly, the precision of theoretical predictions for these two processes is the highest the global theory community have achieved. Therefore this is an excellent testing ground to search for subtle deviations between measurement and theory as indirect signals of new physics. This is an area where Prof. Rizvi has considerable expertise and international recognition for precision measurements. In 2019 he held an Associateship with the University of Durham to collaborate with the renowned IPPP theory group on producing bespoke state-of-the-art predictions.

The ATLAS detector is one of the most complex experimental apparatuses ever built comprising of ~100 million electronic channels and over 100 detector technologies recording collision data at 40 MHz. 

The response of the ATLAS detector is accurately simulated using data-intensive techniques and then used to correct the data for distortions, biases and miscalibrations. This detector response inversion allows us to accurately measure the true underlying physics. In this project we will develop a new robust statistical procedure to invert those distortions which will include testing novel and bespoke machine learning classification approaches. The methods will be developed to propagate all measurement uncertainties and will be applied in the search for Lepton Flavour Violation at the highest collision energies achievable.

The project will be co-supervised by Dr Alex Shestopaloff (SMS) an expert in statistics, Bayesian inference and machine learning.

Supervisor Contact Details:

For informal enquiries about this position, please contact: Prof Eram Rizvi:

E-mail: [Email Address Removed]

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: 31st of January 2023

The minimum requirement for this studentship opportunity is a good Honours degree (minimum 2(i) honours or equivalent) or MSc/MRes in a relevant discipline.

If English is not your first language you will require a valid English certificate equivalent to IELTS 6.5+ overall with a minimum score of 6.0 in Writing and 5.5 in all sections (Reading, Listening, Speaking).


Funding Notes

-Available to Home and international applicants.
-The studentship arrangement will cover home tuition fees and provide an annual stipend for up to three years (Currently set for 2022/23 as £19,688).
-International students note that this studentship only covers home tuition fees and students will need to cover the difference in fees between the home and overseas basic rate from external sources. 
PhD saved successfully
View saved PhDs