Download Free Measurement Of The Average Very Forward Energy As A Function Of The Track Multiplicity At Central Pseudorapidities In Proton Proton Collisions At S Book in PDF and EPUB Free Download. You can read online Measurement Of The Average Very Forward Energy As A Function Of The Track Multiplicity At Central Pseudorapidities In Proton Proton Collisions At S and write the review.

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.
Many high-energy collider experiments (including the current Large Hadron Collider at CERN) involve the collision of hadrons. Hadrons are composite particles consisting of partons (quarks and gluons), and this means that in any hadron-hadron collision there will typically be multiple collisions of the constituents — i.e. multiple parton interactions (MPI). Understanding the nature of the MPI is important in terms of searching for new physics in the products of the scatters, and also in its own right to gain a greater understanding of hadron structure. This book aims at providing a pedagogical introduction and a comprehensive review of different research lines linked by an involvement of MPI phenomena. It is written by pioneers as well as young leading scientists, and reviews both experimental findings and theoretical developments, discussing also the remaining open issues.
This second open access volume of the handbook series deals with detectors, large experimental facilities and data handling, both for accelerator and non-accelerator based experiments. It also covers applications in medicine and life sciences. A joint CERN-Springer initiative, the "Particle Physics Reference Library" provides revised and updated contributions based on previously published material in the well-known Landolt-Boernstein series on particle physics, accelerators and detectors (volumes 21A, B1,B2,C), which took stock of the field approximately one decade ago. Central to this new initiative is publication under full open access
This will be a required acquisition text for academic libraries. More than ten years after its discovery, still relatively little is known about the top quark, the heaviest known elementary particle. This extensive survey summarizes and reviews top-quark physics based on the precision measurements at the Fermilab Tevatron Collider, as well as examining in detail the sensitivity of these experiments to new physics. Finally, the author provides an overview of top quark physics at the Large Hadron Collider.
^ 74 GeV and |y| 2.4; the b jets must contain a B hadron. The measurement has significant statistics up to p T ∼ O(TeV). Advanced methods of unfolding are performed to extract the signal. It is found that fixed-order calculations with underlying event describe the measurement well.
This 2002 monograph, now reissued as OA, explores the primordial state of hadronic matter called quark-gluon plasma.
This thesis describes the search for Dark Matter at the LHC in the mono-jet plus missing transverse momentum final state, using the full dataset recorded in 2012 by the ATLAS Experiment. It is the first time that the number of jets is not explicitly restricted to one or two, thus increasing the sensitivity to new signals. Instead, a balance between the most energetic jet and the missing transverse momentum is required, thus selecting mono-jet-like final states. Collider searches for Dark Matter have typically used signal models employing effective field theories (EFTs), even when comparing to results from direct and indirect detection experiments, where the difference in energy scale renders many such comparisons invalid. The thesis features the first robust and comprehensive treatment of the validity of EFTs in collider searches, and provides a means by which the different classifications of Dark Matter experiments can be compared on a sound and fair basis.