Alison Lister

Associate Professor

Research Classification

Research Interests

Large Hadron Collier (LHC)
ATLAS experiment
Search for physics beyond the standard model
top quarks
dark matter
Machine Learning
Long-lived particles

Relevant Thesis-Based Degree Programs

Research Options

I am available and interested in collaborations (e.g. clusters, grants).
I am interested in working with undergraduate students on research projects.

Research Methodology

LHC (Large Hadron Collider)
machine learning


Master's students
Doctoral students
Any time / year round
I support public scholarship, e.g. through the Public Scholars Initiative, and am available to supervise students and Postdocs interested in collaborating with external partners as part of their research.
I am open to hosting Visiting International Research Students (non-degree, up to 12 months).
I am interested in hiring Co-op students for research placements.

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Graduate Student Supervision

Doctoral Student Supervision

Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.

Displaced vertex search for heavy neutral leptons using the ATLAS detector (2022)

This dissertation presents a search for long-lived heavy neutral leptons (HNLs) in proton-proton collisions at the Large Hadron Collider (LHC). The Standard Model (SM) of particle physics is an extremely successful theory and many of its major predictions have been precisely confirmed. However, the existence of neutrinos, with small nonzero masses, suggests that the SM is incomplete. Introducing HNLs into the SM is a natural way to generate the light neutrino masses through a seesaw mechanism. Theories that postulate the existence of such particles can also explain the asymmetry between matter and anti-matter in our universe and models with at least three HNLs provide a dark matter candidate. This experimental search uses ATLAS data collected between 2015 and 2018 at a centre-of-mass energy of 13 TeV. A non-standard technique is used to search for a displaced vertex from particle trajectories produced in the HNL decay to leptons. The dominant background from uncorrelated leptons crossing in the ATLAS detector is estimated using an object shuffling method. The reconstructed HNL mass is used to discriminate between signal and background. No excess of events is observed and constraints on the strength of the interactions between HNLs and neutrinos are imposed in various scenarios.This dissertation also presents new methods to study the readout system and performance of a silicon strip tracking detector. The LHC is currently undergoing upgrades that will enable it to produce more than ten times the data that has already been collected. To meet the requirements of this challenging new environment, an all-silicon particle tracking system will be installed in ATLAS.

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Search for long-lived particles decaying into displaced hadronic jets in the ATLAS Calorimeter using novel machine learning algorithms (2022)

The Standard Model (SM) has been hugely successful at explaining our natural world at the smallest scales, including the fundamental particles and forces. However, experimental evidence, such as dark matter and baryon asymmetry point to the SM as being an incomplete model. One such candidate to extend the SM are hidden sectors, in which long-lived particles could be the missing link between the SM and a group of hidden particles. Firstly, work on performance expectations of the upgrade to the ATLAS Inner Detector to enable data taking for the next decade will be presented, showing increases in reconstruction efficiency at high momentum and particle density compared to the current detector. Then, a search for hidden sector particles in 2016 ATLAS data, totalling 33.0 fb⁻¹ from LHC proton-proton collisions at center-of-mass energy of 13 TeV, will be discussed. This search focuses on long-lived particles decaying back to SM particles in the ATLAS calorimeters. No significant excess was found, and limits were set on cross section times branching fraction as a function of proper decay length. For the 125 GeV mediator, a few cm to a few m are excluded, assuming a branching fraction of 10%. For higher mass mediators, up to 1 TeV, cross section times branching fraction of 0.1 pb are typically excluded between a few cm to a few m. Finally, a search for these same long-lived hidden sector particles was also performed for the full Run 2 ATLAS dataset, totalling 139.0 fb⁻¹. Novel machine learning techniques were used, such as an adversarial neural network which greatly reduced the impact of simulation mis-modelling. No significant excess was found, and for a 125 GeV Higgs Boson mediator, assuming a 10% branching ratio to long-lived particles, proper decay lengths between about 1 cm and a few tens of meters are excluded, improving 2016 results by about an order of magnitude. For higher mass mediators, cross section times branching fraction of 0.1 pb can be excluded for a proper decay length between 1 cm to a few tens of meters, improving 2016 limits by a factor of 2-3 depending on the model.

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Searches for new high-mass resonances in top-antitop and di-electron final states using the ATLAS detector (2021)

The standard model of particle physics (SM) describes all the fundamental particles and their interactions. It is a very successful theory; however, many experimental observations - such as the origin of neutrino mass, particle origin of dark matter, e.t.c. - are either not consistent or not explained by the SM. So, it is inevitable that there has to be a (physics) model beyond the SM which will consistently explain all these observations, not covered by the SM. Several of such extensions predict new heavy particles that can interact with SM particles. This dissertation presents searches for the resonant production of such high-mass particles in dielectron and top-antitop final states. These searches use proton-proton collision data at the center-of-mass energy of 13 TeV collected by the ATLAS detector at the Large Hadron Collider (LHC) between 2015 and 2018. Electrons are stable and easy to reconstruct, but top-quarks decay instantaneously. Two dominant top-decay final states, all-hadronic and semi-leptonic, are studied in this dissertation. The combined mass distributions of all the final-state particles are used to perform model-dependent and model-independent statistical searches. No evidence for the existence of new particles is found in any of the explored final states. Hence, upper limits on production cross-section times branching ratio and lower limits on the mass of heavy Z' particles, predicted by the BSM models, are placed at a 95% confidence level. The dilepton resonance search excludes Z' boson below 3.6 TeV. The resonance search in the boosted all-hadronic top-antitop final state excludes Z' bosons with a mass lower than 4.1 TeV. Whereas in the semi-leptonic search, the same signal is expected to be excluded up to 3.6 TeV.The dissertation also presents a new algorithm for splitting the merged charge clusters in the ATLAS pixel detector, based on a Mixture Density Network (MDN). The performance of this new algorithm is found to be better than the existing algorithm. As a result, the MDN-based algorithm is expected to be used as a default algorithm in ATLAS during the next data collection period, which will start in 2022.

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Searches for heavy vector-like quarks decaying to high transverse momentum W bosons and top- or bottom-quarks and weak mode identification with the ATLAS detector (2018)

The precise understanding of elementary particle properties and theory parameters predicted by the Standard Model of Particle Physics (SM) as well as the revelation of new physics phenomena beyond the scope of that successful theory are at the heart of modern fundamental particle physics research. The Large Hadron Collider (LHC) and modern particle detectors provide the key to probing nature at energy scales never achieved in an experimental controlled setup before. The assumption that the SM describes nature only up to a certain energy scale Λ can be relaxed if new particles are present. This helps in particular with the so called "fine-tuning" problem which requires large corrections -- in the SM -- to the bare mass of the Higgs boson in order to be consistent with the observed mass. A possible solution to this problem is the existence of partner particles of the heaviest known fundamental particle, the top-quark. The new partner particles are expected to be up to ten times heavier. Popular examples of theories predicting heavier top-quark partners are supersymmetric theories and theories that add an additional quark sector to the SM which might be a result of an additional spontaneously broken global symmetry. This dissertation documents two searches for heavy top-quark partners, namely vector-like quarks (VLQs), based on the proton proton pp collision data collected in 2015 and 2016, corresponding to an integrated luminosity of 36.1 fb-¹ at a center of mass energy of 13 TeV. It also elaborates on the work that contributed to a successful data taking campaign related to the alignment of the inner most part of the ATLAS detector with emphasis on the identification and mitigation of track parameter biases.No signs for VLQs were found. The strongest lower mass limits on the pair-produced VLQs decaying into W bosons and top- or bottom-quarks are set to 1.35 TeV at the 95% Confidence Interval exceeding the one TeV scale for the first time. In addition, the analyses were re-interpreted for other expected VLQ decay signatures.

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Master's Student Supervision

Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.

Improvement to the statistical sensitivity of top quark pair production in conjunction with additional heavy flavour jets through multivariate analysis (2016)

With the mass of the discovered Higgs-like boson being 125 GeV, this leads to a primary Higgs decay mode to two bottom (b) jets. A precise measurement of top-pair (tt̄) production in conjunction with two additional b-jets is essential to reduce the background uncertainty on the tt̄ + Higgs production cross-section, a direct probe of the Higgs to Yukawa coupling. This thesis attempts to improve on the statistical sensitivity of tt̄ production in conjunction with two additional heavy-flavour jets, using expected sensitivities from 20.3 fb-¹ of pp collision data at √s = 8TeV, collected by the ATLAS detector at the Large Hadron Collider in 2012. This thesis compares multiple multivariate analysis techniques, boosted decision trees and artificial neural networks, in both binary and multi-class classification cases. An overall improvement in precision was seen, from 19.7% uncertainty on the baseline tt̄ + bb̄ measurement based on a fit to the best single variable, to 16.1% uncertainty with the very best multi-class neural network algorithm. This represents a relative improvement of nearly 20% and could thus reduce luminosity needed for a precision measurement of this process.

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