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Author: Katharina Morik Publisher: Walter de Gruyter GmbH & Co KG ISBN: 3110786133 Category : Science Languages : en Pages : 406
Book Description
Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. 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. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 2 covers machine learning for knowledge discovery in particle and astroparticle physics. Their instruments, e.g., particle detectors 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: Katharina Morik Publisher: Walter de Gruyter GmbH & Co KG ISBN: 3110786133 Category : Science Languages : en Pages : 406
Book Description
Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. 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. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 2 covers machine learning for knowledge discovery in particle and astroparticle physics. Their instruments, e.g., particle detectors 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: Leon M. Lederman Publisher: Houghton Mifflin Harcourt ISBN: 9780618711680 Category : Science Languages : en Pages : 452
Book Description
A fascinating tour of particle physics from Nobel Prize winner Leon Lederman. At the root of particle physics is an invincible sense of curiosity. Leon Lederman embraces this spirit of inquiry as he moves from the Greeks' earliest scientific observations to Einstein and beyond to chart this unique arm of scientific study. His survey concludes with the Higgs boson, nicknamed the God Particle, which scientists hypothesize will help unlock the last secrets of the subatomic universe, quarks and all--it's the dogged pursuit of this almost mystical entity that inspires Lederman's witty and accessible history.
Author: Lyndon R. Evans Publisher: EPFL Press ISBN: 9782940222346 Category : Hadron colliders Languages : en Pages : 264
Book Description
Describes the technology and engineering of the Large Hadron collider (LHC), one of the greatest scientific marvels of this young 21st century. This book traces the feat of its construction, written by the head scientists involved, placed into the context of the scientific goals and principles.
Author: Cristina Martin Perez Publisher: Springer Nature ISBN: 3030902064 Category : Science Languages : en Pages : 291
Book Description
In this work, the interaction between the Higgs boson and the top quark is studied with the proton-proton collisions at 13 TeV provided by the LHC at the CMS detector at CERN (Geneva). At the LHC, these particles are produced simultaneously via the associate production of the Higgs boson with one top quark (tH process) or two top quarks (ttH process). Compared to many other possible outcomes of the proton-proton interactions, these processes are very rare, as the top quark and the Higgs boson are the heaviest elementary particles known. Hence, identifying them constitutes a significant experimental challenge. A high particle selection efficiency in the CMS detector is therefore crucial. At the core of this selection stands the Level-1 (L1) trigger system, a system that filters collision events to retain only those with potential interest for physics analysis. The selection of hadronically decaying τ leptons, expected from the Higgs boson decays, is especially demanding due to the large background arising from the QCD interactions. The first part of this thesis presents the optimization of the L1 τ algorithm in Run 2 (2016-2018) and Run 3 (2022-2024) of the LHC. It includes the development of a novel trigger concept for the High-Luminosity LHC, foreseen to start in 2027 and to deliver 5 times the current instantaneous luminosity. To this end, sophisticated algorithms based on machine learning approaches are used, facilitated by the increasingly modern technology and powerful computation of the trigger system. The second part of the work presents the search of the tH and ttH processes with the subsequent decays of the Higgs boson to pairs of τ lepton, W bosons or Z bosons, making use of the data recorded during Run 2. The presence of multiple particles in the final state, along with the low cross section of the processes, makes the search an ideal use case for multivariant discriminants that enhance the selectivity of the signals and reject the overwhelming background contributions. The discriminants presented are built using state-of-the-art machine learning techniques, able to capture the correlations amongst the processes involved, as well as the so-called Matrix Element Method (MEM), which combines the theoretical description of the processes with the detector resolution effects. The level of sophistication of the methods used, along with the unprecedented amount of collision data analyzed, result in the most stringent measurements of the tH and ttH cross sections up to date.
Author: John F. Gunion Publisher: CRC Press ISBN: 0429976070 Category : Science Languages : en Pages : 333
Book Description
The Higgs Hunter's Guide is a definitive and comprehensive guide to the physics of Higgs bosons. In particular, it discusses the extended Higgs sectors required by those recent theoretical approaches that go beyond the Standard Model, including supersymmetry and superstring-inspired models.
Author: Katharina Morik
Author: Katharina Morik
Author: Leon M. Lederman
Author: 
Author: 
Author: Lyndon R. Evans
Author: Cristina Martin Perez
Author: 
Author: John F. Gunion
Author: Lyndon Evans
Author: European Organization for Nuclear Research