Novel Networks in Collider Searches for New Physics
Date
2021
Authors
Mullin, Anna Jane
Editors
Advisors
White, Martin
Jackson, Paul
Jackson, Paul
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Thesis
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Abstract
Beyond-Standard Model (BSM) physics searches at the LHC are limited by the amount of
information available to distinguish a new physics process from its backgrounds. Analyses
apply a range of classification algorithms to obtain sensitivity to rare signals, but are challenged
to obtain enough information in a broad parameter space without relying on heavy
optimisation in narrow search regions. LHC event classification techniques become more
powerful when they can be applied broadly to diverse models, requiring a large number of
independent variables sensitive to anomalous signals. In our prototype ATLAS search, we
create new variables that target information not used in current methods. Whereas typical
variables treat events in isolation, we obtain further discrimination from the “similarity” between
event pairs by evaluating “distances” in a kinematic space. A map of event similarities
forms a graph network, which provides a convenient range of network variables able to
quantify local topologies. In networks constructed from nodes of LHC events, we aim to use
network variables to increase sensitivity to anomalous topologies local to BSM events. Our
proof-of-principle analysis reveals that BSM physics events may populate distinct distributions
compared with Standard Model events in several types of network variables, including
measures of local centrality and clustering, using supersymmetry searches as examples.
Graph network analysis may contribute power to existing methods of event classification and
increase sensitivity to anomalous signatures.
School/Discipline
School of Physical Sciences
Dissertation Note
Thesis (MPhil) -- University of Adelaide, School of Physical Sciences, 2021
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