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Author: Nicolas Maximilian Köhler
Publisher: Springer Nature
ISBN: 3030259889
Category : Science
Languages : en
Pages : 264
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Book Description
Astrophysical observations implying the existence of Dark Matter and Dark Energy, which are not described by the Standard Model (SM) of particle physics, have led to extensions of the SM predicting new particles that could be directly produced at the Large Hadron Collider (LHC) at CERN. Based on 2015 and 2016 ATLAS proton-proton collision data, this thesis presents searches for the supersymmetric partner of the top quark, for Dark Matter, and for DarkEnergy, in signatures with jets and missing transverse energy. Muon detection is key to some of the most important LHC physics results, including the discovery of the Higgs boson and the measurement of its properties. The efficiency with which muons can be detected with the ATLAS detector is measured using Z boson decays. The performance of high-precision Monitored Drift Tube muon chambers under background rates similar to the ones expected for the High Luminosity-LHC is studied.
Author: Nicolas Maximilian Köhler
Publisher: Springer Nature
ISBN: 3030259889
Category : Science
Languages : en
Pages : 264
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Book Description
Astrophysical observations implying the existence of Dark Matter and Dark Energy, which are not described by the Standard Model (SM) of particle physics, have led to extensions of the SM predicting new particles that could be directly produced at the Large Hadron Collider (LHC) at CERN. Based on 2015 and 2016 ATLAS proton-proton collision data, this thesis presents searches for the supersymmetric partner of the top quark, for Dark Matter, and for DarkEnergy, in signatures with jets and missing transverse energy. Muon detection is key to some of the most important LHC physics results, including the discovery of the Higgs boson and the measurement of its properties. The efficiency with which muons can be detected with the ATLAS detector is measured using Z boson decays. The performance of high-precision Monitored Drift Tube muon chambers under background rates similar to the ones expected for the High Luminosity-LHC is studied.
Author: Marc Christopher Thomas
Publisher: Springer
ISBN: 3319434527
Category : Science
Languages : en
Pages : 113
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Book Description
This thesis studies collider phenomenology of physics beyond the Standard Model at the Large Hadron Collider (LHC). It also explores in detail advanced topics related to Higgs boson and supersymmetry – one of the most exciting and well-motivated streams in particle physics. In particular, it finds a very large enhancement of multiple Higgs boson production in vector-boson scattering when Higgs couplings to gauge bosons differ from those predicted by the Standard Model. The thesis demonstrates that due to the loss of unitarity, the very large enhancement for triple Higgs boson production takes place. This is a truly novel finding. The thesis also studies the effects of supersymmetric partners of top and bottom quarks on the Higgs production and decay at the LHC, pointing for the first time to non-universal alterations for two main production processes of the Higgs boson at the LHC–vector boson fusion and gluon–gluon fusion. Continuing the exploration of Higgs boson and supersymmetry at the LHC, the thesis extends existing experimental analysis and shows that for a single decay channel the mass of the top quark superpartner below 175 GeV can be completely excluded, which in turn excludes electroweak baryogenesis in the Minimal Supersymmetric Model. This is a major new finding for the HEP community. This thesis is very clearly written and the introduction and conclusions are accessible to a wide spectrum of readers.
Author: Daiki Yamaguchi
Publisher: Springer Nature
ISBN: 9811509328
Category : Science
Languages : en
Pages : 288
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Book Description
This book reports on the search for a new heavy particle, the Vector-Like Top quark (VLT), in the Large Hadron Collider (LHC) at CERN. The signal process is the pair production of VLT decaying into a Higgs boson and top quark (TT→Ht+X, X=Ht, Wb, Zt). The signal events result in top–antitop quarks final states with additional heavy flavour jets. The book summarises the analysis of the data collected with the ATLAS detector in 2015 and 2016. In order to better differentiate between signals and backgrounds, exclusive taggers of top quark and Higgs boson were developed and optimised for VLT signals. These efforts improved the sensitivity by roughly 30%, compared to the previous analysis. The analysis outcomes yield the strongest constraints on parameter space in various BSM theoretical models. In addition, the book addresses detector operation and the evaluation of tracking performance. These efforts are essential to properly collecting dense events and improving the accuracy of the reconstructed objects that are used for particle identification. As such, they represent a valuable contribution to data analysis in extremely dense environments.
Author: J. Thanh Van Tran
Publisher: Atlantica Séguier Frontières
ISBN: 9782863320556
Category : Dark matter (Astronomy)
Languages : en
Pages : 616
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Book Description
Author: Takuya Nobe
Publisher: Springer
ISBN: 9811000034
Category : Science
Languages : en
Pages : 229
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Book Description
This book reports a search for theoretically natural supersymmetry (SUSY) at the Large Hadron Collider (LHC). The data collected with the ATLAS detector in 2012 corresponding to 20 /fb of an integrated luminosity have been analyzed for stop pair production in proton–proton collisions at a center-of-mass energy of 8 TeV at the Large Hadron Collider (LHC) in the scenario of the higgsino-like neutralino. The author focuses on stop decaying into a bottom quark and chargino. In the scenario of the higgsino-like neutralino, the mass difference between charginos and neutralinos (Δm) is expected to be small, and observable final-state particles are likely to have low-momentum (soft). The author develops a dedicated analysis with a soft lepton as a probe of particles from chargino decay, which suppresses the large amount of backgrounds. As a result of the analysis, no significant SUSY signal is observed. The 95% confidence-level exclusion limits are set to masses of stop and neutralino assuming Δm = 20 GeV. The region with ΔM (the mass difference between stop and neutralino) 70 GeV is excluded for the first time at stop mass of less than 210 GeV. The author also excludes the signals with ΔM 120 GeV up to 600 GeV of stop mass with neutralino mass of less than 280 GeV. The author clearly shows very few remaining parameter spaces for light stop (e.g., topology of stop decay is extremely similar to the SM top quark) by combining his results and previous ATLAS analyses. His results provide a strong constraint to searches for new physics in the future.
Author: Javier Montejo Berlingen
Publisher: Springer
ISBN: 3319410512
Category : Science
Languages : en
Pages : 288
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Book Description
This doctoral thesis focuses on the search for new phenomena in top-antitop quark (tt) final states with additional b-quark jets at the LHC. It uses the full Run 1 dataset collected by the ATLAS experiment in proton-proton collisions at √s=8 TeV. The final state of interest consists of an isolated lepton, a neutrino and at least six jets with at least four b-tagged jets, a challenging experimental signature owing to the large background from tt+heavy-flavor production. This final state is characteristic of ttH production, with the Higgs boson decaying into bb, a process that allows direct probing of the top-Higgs Yukawa coupling. This signature is also present in many extensions of the Standard Model that have been proposed as solutions to the hierarchy problem, such as supersymmetry or composite Higgs models, which predict the pair production of bosonic or fermionic top quark partners, or the anomalous production of four-top-quark events. All these physics processes have been searched for using an ambitious search strategy that has been developed on the basis of a combination of state-of-art theoretical predictions and a sophisticated statistical analysis to constrain in-situ the large background uncertainties. As a result, the most restrictive bounds to date on the above physics processes have been obtained.
Author: Katharina Morik
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 311078596X
Category : Science
Languages : en
Pages : 364
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Book Description
Machine learning is part of Artificial Intelligence since its beginning. Certainly, not learning would only allow the perfect being to show intelligent behavior. All others, be it humans or machines, need to learn in order to enhance their capabilities. In the eighties of the last century, learning from examples and modeling human learning strategies have been investigated in concert. The formal statistical basis of many learning methods has been put forward later on and is still an integral part of machine learning. Neural networks have always been in the toolbox of methods. Integrating all the pre-processing, exploitation of kernel functions, and transformation steps of a machine learning process into the architecture of a deep neural network increased the performance of this model type considerably. Modern machine learning is challenged on the one hand by the amount of data and on the other hand by the demand of real-time inference. This leads to an interest in computing architectures and modern processors. For a long time, the machine learning research could take the von-Neumann architecture for granted. All algorithms were designed for the classical CPU. Issues of implementation on a particular architecture have been ignored. This is no longer possible. The time for independently investigating machine learning and computational architecture is over. Computing architecture has experienced a similarly rampant development from mainframe or personal computers in the last century to now very large compute clusters on the one hand and ubiquitous computing of embedded systems in the Internet of Things on the other hand. Cyber-physical systems’ sensors produce a huge amount of streaming data which need to be stored and analyzed. Their actuators need to react in real-time. This clearly establishes a close connection with machine learning. Cyber-physical systems and systems in the Internet of Things consist of diverse components, heterogeneous both in hard- and software. Modern multi-core systems, graphic processors, memory technologies and hardware-software codesign offer opportunities for better implementations of machine learning models. Machine learning and embedded systems together now form a field of research which tackles leading edge problems in machine learning, algorithm engineering, and embedded systems. Machine learning today needs to make the resource demands of learning and inference meet the resource constraints of used computer architecture and platforms. A large variety of algorithms for the same learning method and, moreover, diverse implementations of an algorithm for particular computing architectures optimize learning with respect to resource efficiency while keeping some guarantees of accuracy. The trade-off between a decreased energy consumption and an increased error rate, to just give an example, needs to be theoretically shown for training a model and the model inference. Pruning and quantization are ways of reducing the resource requirements by either compressing or approximating the model. In addition to memory and energy consumption, timeliness is an important issue, since many embedded systems are integrated into large products that interact with the physical world. If the results are delivered too late, they may have become useless. As a result, real-time guarantees are needed for such systems. To efficiently utilize the available resources, e.g., processing power, memory, and accelerators, with respect to response time, energy consumption, and power dissipation, different scheduling algorithms and resource management strategies need to be developed. This book series addresses machine learning under resource constraints as well as the application of the described methods in various domains of science and engineering. Turning big data into smart data requires many steps of data analysis: methods for extracting and selecting features, filtering and cleaning the data, joining heterogeneous sources, aggregating the data, and learning predictions need to scale up. The algorithms are challenged on the one hand by high-throughput data, gigantic data sets like in astrophysics, on the other hand by high dimensions like in genetic data. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are applied to program executions in order to save resources. The three books will have the following subtopics: Volume 1: Machine Learning under Resource Constraints - Fundamentals Volume 2: Machine Learning and Physics under Resource Constraints - Discovery Volume 3: Machine Learning under Resource Constraints - Applications Volume 2 is about machine learning for knowledge discovery in particle and astroparticle physics. Their instruments, e.g., particle accelerators or telescopes, gather petabytes of data. Here, machine learning is necessary not only to process the vast amounts of data and to detect the relevant examples efficiently, but also as part of the knowledge discovery process itself. The physical knowledge is encoded in simulations that are used to train the machine learning models. At the same time, the interpretation of the learned models serves to expand the physical knowledge. This results in a cycle of theory enhancement supported by machine learning.
Author: Roman Anatolievich Ryutin
Publisher: World Scientific
ISBN: 9814689319
Category : Science
Languages : en
Pages : 380
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Book Description
This unique volume captures the content of the XXXth International Workshop on High Energy Physics. The scope of this volume is much wider than just high-energy physics; it actually concerns and includes materials from all the most fundamental areas of modern physics research: high-energy physics proper, gravitation and cosmology. Presentations embrace both theory and experiment.
Author: Hannsjörg Artur Weber
Publisher: Springer
ISBN: 3319199560
Category : Science
Languages : en
Pages : 217
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Book Description
The project reported here was a search for new super symmetric particles in proton-proton collisions at the LHC. It has produced some of the world’s best exclusion limits on such new particles. Furthermore, dedicated simulation studies and data analyses have also yielded essential input to the upgrade activities of the CMS collaboration, both for the Phase-1 pixel detector upgrade and for the R&D studies in pursuit of a Phase-2 end cap calorimeter upgrade.
Author: Johanna Gramling
Publisher: Springer
ISBN: 3319950169
Category : Science
Languages : en
Pages : 290
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Book Description
This book discusses searches for Dark Matter at the CERN’s LHC, the world’s most powerful accelerator. It introduces the relevant theoretical framework and includes an in-depth discussion of the Effective Field Theory approach to Dark Matter production and its validity, as well as an overview of the formalism of Simplified Dark Matter models. Despite overwhelming astrophysical evidence for Dark Matter and numerous experimental efforts to detect it, the nature of Dark Matter still remains a mystery and has become one of the hottest research topics in fundamental physics. Two searches for Dark Matter are presented, performed on data collected with the ATLAS experiment. They analyze missing-energy final states with a jet or with top quarks. The analyses are explained in detail, and the outcomes and their interpretations are discussed, also in view of the precedent analysis of theoretical approaches. Given its depth of coverage, the book represents an excellent reference guide for all physicists interested in understanding the theoretical and experimental considerations relevant to Dark Matter searches at the LHC.
