Searching for New Physics with neutrinos in a novel pixel detector

The UKRI AI Centre for Doctoral Training (CDT) in Decision Making for Complex Systems is a joint CDT between The University of Manchester and the University of Cambridge. The CDT provides funding for four years of advanced studies towards a PhD. The first year consists of a taught program at Manchester that will cover the fundamentals of Machine Learning. This year is followed by three years of research at either at Manchester or Cambridge. Please note the research element of the PhD will take place at the host institution of the supervisor listed for each project.

The elusive neutrinos may hold the key to answering many fundamental questions in physics such as the matter/antimatter asymmetry in the Universe. Noble element detectors have transformed the field of neutrino physics by offering high granularity electronic images of neutrino events. The 2D images produced by these detectors provide incredibly detailed information about the rare neutrino interactions. However, a big challenge remains in extracting useful information for physics analyses with traditional algorithms for 2D images: the “reconstruction” of the neutrino interactions is the single limiting factor that hampers event selection efficiencies and background rejection.  

Our group in Manchester is posed to tackle this challenge from two complementary avenues. From a hardware perspective, we are developing a novel pixel technology with a powerful light collection system for future noble element detectors. This technology will offer intrinsic 3D imaging capabilities, thus reducing the ambiguities inherent to traditional 2D images, and providing higher quality information as a starting point for event reconstruction. 

From a software perspective, we are developing new machine learning/artificial intelligence algorithms for 3D imaging that will substitute traditional event reconstruction. 

The use of machine learning to reconstruct, select and identify events has been proven very promising to increase the analysis performances in pixelated readouts compared to traditional 2D readout. For example, [1] reports a notable gain in recognition performance of all type of events relevant to the matter-anti matter asymmetry analysis – notably, a gain of 17% in signal efficiency and 12% in signal purity. Such improvements directly translate into a significant boost in sensitivity to physics beyond the standard model. 

In this project, the student will study the physics capabilities of a pixelated noble element detector for neutrino physics. Given the nature of the detector images, the studies will be performed using pattern recognition algorithms. As a first step, new reconstruction algorithms, using convolutional neural networks will be developed and adapted to the 3D pixel images. Long-short term memory netwroks will also be tested to see if further background reduction can be achieved given the temporal component of the pixel readout. The algorithms development will leverage real data from the MicroBooNE and NEXT detectors (both noble element detectors). This first stage of the project will lead to publication on the performance of these algorithms. The second step will be to use the newly developed algorithms to study simulated events in a future large pixel detector. We will focus our study on astrophysical neutrinos, such as neutrinos coming from supernovae and the sun. The goal is to demonstrate that the intrinsic 3D pixel readout offers enhanced performances. We expect this work to lead to a second publication on the physics potential of a pixel detector to study astrophysical neutrinos. The final step of this project will include the scintillation light information to the 3D images to increase the energy resolution and to lower the event detection threshold. The light readout also produces patterns that can be more efficiently reconstructed using neural networks. The new algorithms dedicated to the reconstruction of the light will complement the ones for 3D images. This work will lead to a novel, 4D event reconstruction method that is expected to make significant advances in the physics reach of a future pixelated detector with a powerful light detection system.

Entry requirements

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline.

How to Apply

As the CDT has only recently been awarded we strongly encourage you to contact the supervisor of the project you are interested in with your CV and supporting documents. You will have a chance to meet with prospective supervisors prior to submitting an application – further details will be provided.

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact.

We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).

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