Alison Lister

Associate Professor

Research Classification

Elementary Particles

Research Interests

Large Hadron Collier (LHC)
ATLAS experiment
Search for physics beyond the standard model
top quarks
dark matter
Detector development

Relevant Degree Programs

 

Research Methodology

LHC (Large Hadron Collider)
machine learning

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Master's students
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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.
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Graduate Student Supervision

Doctoral Student Supervision (Jan 2008 - Mar 2019)
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 (2010-2017)
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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Publications

Current Students & Alumni

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