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- Albertsson, Kim
(författare)
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Machine Learning in High-Energy Physics: Displaced Event Detection and Developments in ROOT/TMVA
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Many proposed extensions to the Standard Model of particle physics predict long-lived particles, which can decay at a significant distance from the primary interaction point. Such events produce displaced vertices with distinct detector signatures when compared to standard model processes. The Large Hadron Collider (LHC) operates at a collision rate where it is not feasible to record all generated data—a problem that will be exac-erbated in the coming high-luminosity upgrade—necessitating an online trigger system to decide which events to keep based on partial information. However, the trigger is not directly sensitive to signatures with displaced vertices from Long-lived particles (LLPs). Current LLP detection approaches require a computationally expensive reconstruction step, or rely on auxiliary signatures such as energetic particles or missing energy. An improved trigger sensitivity increases the reach of searches for extensions to the standard model.This thesis explores the possibility to apply machine learning methods directly on low-level tracking features, such as detector hits and hit-pairs to identify displaced high-mass decays while avoiding a full vertex and track reconstruction step.A dataset is developed where modelled displaced signatures from novel and known physics processes are mixed in a custom simulation environment, which models the in-ner detector of a general purpose particle detector. Two machine learning models are evaluated using the dataset: a multi-layer dense Artificial Neural Network (ANN), and a Graph Neural Network (GNN). Two case studies suggest that dense ANNs have difficulty capturing relational information in low-level data, while GNNs can feasibily discriminate heavy displaced decay signatures from a Standard Model background. Furthermore it was found that GNNs can perform at a background rejection factor of 103 and a signal efficiency of 20% in collision environments with moderate levels of pile-up interactions, i.e. low-energy particle collisions simultaneous with the primary hard scatter. Further work is required to integrate the approach into a trigger environment. In particular, detector material and measurement resolution effects should be included in the simulation, which should be scaled to model the High-Luminosity Large Hadron Collider (HL-LHC) with its more complicated geometry and its high levels of pile-up.In parallel, the machine learning landscape is quickly evolving and concentrating into large software frameworks with expanding scope, while the High-Energy Physics (HEP) community maintains its own set of tools and frameworks, one example being the Toolkit for Multivariate Analysis (TMVA) which is part of the ROOT framework. This thesis discusses the long- and short-term evolution of these tools, both current trends and some relations to parallel developments in Industry 4.0.
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- Alimena, Juliette, et al.
(författare)
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Searching for long-lived particles beyond the Standard Model at the Large Hadron Collider
- 2020
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Ingår i: Journal of Physics G. - : IOP Publishing. - 0954-3899 .- 1361-6471. ; 47:9
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Tidskriftsartikel (refereegranskat)abstract
- Particles beyond the Standard Model (SM) can generically have lifetimes that are long compared to SM particles at the weak scale. When produced at experiments such as the Large Hadron Collider (LHC) at CERN, these long-lived particles (LLPs) can decay far from the interaction vertex of the primary proton-proton collision. Such LLP signatures are distinct from those of promptly decaying particles that are targeted by the majority of searches for new physics at the LHC, often requiring customized techniques to identify, for example, significantly displaced decay vertices, tracks with atypical properties, and short track segments. Given their non-standard nature, a comprehensive overview of LLP signatures at the LHC is beneficial to ensure that possible avenues of the discovery of new physics are not overlooked. Here we report on the joint work of a community of theorists and experimentalists with the ATLAS, CMS, and LHCb experiments-as well as those working on dedicated experiments such as MoEDAL, milliQan, MATHUSLA, CODEX-b, and FASER-to survey the current state of LLP searches at the LHC, and to chart a path for the development of LLP searches into the future, both in the upcoming Run 3 and at the high-luminosity LHC. The work is organized around the current and future potential capabilities of LHC experiments to generally discover new LLPs, and takes a signature-based approach to surveying classes of models that give rise to LLPs rather than emphasizing any particular theory motivation. We develop a set of simplified models; assess the coverage of current searches; document known, often unexpected backgrounds; explore the capabilities of proposed detector upgrades; provide recommendations for the presentation of search results; and look towards the newest frontiers, namely high-multiplicity 'dark showers', highlighting opportunities for expanding the LHC reach for these signals.
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| 5. |
- Aad, G., et al.
(författare)
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- 2011
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swepub:Mat__t (refereegranskat)
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| 6. |
- Aad, G., et al.
(författare)
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- 2011
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Tidskriftsartikel (refereegranskat)
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| 7. |
- Aad, G., et al.
(författare)
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- 2011
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swepub:Mat__t (refereegranskat)
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| 8. |
- Aad, G., et al.
(författare)
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- 2013
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swepub:Mat__t (refereegranskat)
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| 9. |
- Aad, G., et al.
(författare)
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- 2012
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swepub:Mat__t (refereegranskat)
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| 10. |
- Aad, G., et al.
(författare)
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- 2012
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swepub:Mat__t (refereegranskat)
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