[PDF] A New Unfolding Method For The Magic Telescope eBook

Author : David Carreto Fidalgo
Publisher : Springer
Page : 208 pages
File Size : 39,45 MB
Release : 2019-07-17
Category : Science
ISBN : 3030241947

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This book reports on the extraordinary observation of TeV gamma rays from the Crab Pulsar, the most energetic light ever detected from this type of object. It presents detailed information on the painstaking analysis of the unprecedentedly large dataset from the MAGIC telescopes, and comprehensively discusses the implications of pulsed TeV gamma rays for state-of-the-art pulsar emission models. Using these results, the book subsequently explores new testing methodologies for Lorentz Invariance Violation, in terms of a wavelength-dependent speed of light. The book also covers an updated search for Very-High-Energy (VHE), >100 GeV, emissions from millisecond pulsars using the Large Area Telescope on board the Fermi satellite, as well as a study on the promising Pulsar Wind Nebula candidate PSR J0631. The observation of VHE gamma rays is essential to studying the non-thermal sources of radiation in our Universe. Rotating neutron stars, also known as pulsars, are an extreme source class known to emit VHE gamma rays. However, to date only two pulsars have been detected with emissions above 100 GeV, and our understanding of their emission mechanism is still lacking.

Author : Maurice M. Shapiro
Publisher : Springer Science & Business Media
Page : 412 pages
File Size : 24,15 MB
Release : 2006-01-26
Category : Science
ISBN : 1402037481

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“Neutrinos and Explosive Events in the Universe” brought together experts from diverse disciplines to offer a detailed view of the exciting new work in this part of High Energy Astrophysics. Sponsored by NATO as an Advanced Study Institute, and coordinated under the auspices of the International School of Cosmic Ray Astrophysics (14th biennial course), the ASI featured a full program of lectures and discussion in the ambiance of the Ettore Majorana Centre in Erice, Italy, including visits to the local Dirac and Chalonge museum collections as well as a view of the cultural heritage of southern Sicily. Enri- ment presentations on results from the Spitzer Infrared Space Telescope and the Origin of Complexity complemented the program. This course was the best attended in the almost 30 year history of the School with 121 participants from 22 countries. The program provided a rich ex- rience, both introductory and advanced, to fascinating areas of observational Astrophysics Neutrino Astronomy, High Energy Gamma Ray Astronomy, P- ticle Astrophysics and the objects most likely responsible for the signals - plosions and related phenomena, ranging from Supernovae to Black Holes to the Big Bang. Contained in this NATO Science Series volume is a summative formulation of the physics and astrophysics of this newly emerging research area that already has been, and will continue to be, an important contributor to understanding our high energy universe.

Author : Jelena Aleksić
Publisher : Springer
Page : 213 pages
File Size : 29,74 MB
Release : 2015-11-06
Category : Science
ISBN : 3319231235

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This thesis presents the results of indirect dark matter searches in the gamma-ray sky of the near Universe, as seen by the MAGIC Telescopes. The author has proposed and led the 160 hours long observations of the dwarf spheroidal galaxy Segue 1, which is the deepest survey of any such object by any Cherenkov telescope so far. Furthermore, she developed and completely characterized a new method, dubbed “Full Likelihood”, that optimizes the sensitivity of Cherenkov instruments for detection of gamma-ray signals of dark matter origin. Compared to the standard analysis techniques, this novel approach introduces a sensitivity improvement of a factor of two (i.e. it requires 4 times less observation time to achieve the same result). In addition, it allows a straightforward merger of results from different targets and/or detectors. By selecting the optimal observational target and combining its very deep exposure with the Full Likelihood analysis of the acquired data, the author has improved the existing MAGIC bounds to the dark matter properties by more than one order of magnitude. Furthermore, for particles more massive than a few hundred GeV, those are the strongest constraints from dwarf galaxies achieved by any gamma-ray instrument, both ground-based or space-borne alike.

Author : Katharina Morik
Publisher : Walter de Gruyter GmbH & Co KG
Page : 364 pages
File Size : 37,67 MB
Release : 2022-12-31
Category : Science
ISBN : 311078596X

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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 : Rubén López Coto
Publisher : Springer
Page : 241 pages
File Size : 16,26 MB
Release : 2016-09-29
Category : Science
ISBN : 3319447513

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This thesis is a comprehensive work that addresses many of the open questions currently being discusssed in the very-high-energy (VHE) gamma-ray community. It presents a detailed description of the MAGIC telescope together with a glimpse of the future Cherenkov Telescope Array (CTA). One section is devoted to the design, development and characterization of trigger systems for current and future imaging atmospheric Cherenkov telescopes. The book also features a state-of-the-art description of pulsar wind nebula (PWN) systems, the study of the multi-TeV spectrum of the Crab nebula, as well as the discovery of VHE gamma rays at the multiwavelength PWN 3C 58, which were sought at these wavelengths for more than twenty years. It also includes the contextualization of this discovery amongst the current population of VHE gamma-ray PWNe. Cataclysmic variable stars represent a new source of gamma ray energies, and are also addressed here. In closing, the thesis reports on the systematic search for VHE gamma-ray emissions of AE Aquarii in a multiwavelength context and the search for VHE gamma-ray variability of novae during outbursts at different wavelengths.

Author : Jostein Gaarder
Publisher : Farrar, Straus and Giroux
Page : 735 pages
File Size : 25,19 MB
Release : 2007-03-20
Category : Fiction
ISBN : 1466804270

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A page-turning novel that is also an exploration of the great philosophical concepts of Western thought, Jostein Gaarder's Sophie's World has fired the imagination of readers all over the world, with more than twenty million copies in print. One day fourteen-year-old Sophie Amundsen comes home from school to find in her mailbox two notes, with one question on each: "Who are you?" and "Where does the world come from?" From that irresistible beginning, Sophie becomes obsessed with questions that take her far beyond what she knows of her Norwegian village. Through those letters, she enrolls in a kind of correspondence course, covering Socrates to Sartre, with a mysterious philosopher, while receiving letters addressed to another girl. Who is Hilde? And why does her mail keep turning up? To unravel this riddle, Sophie must use the philosophy she is learning—but the truth turns out to be far more complicated than she could have imagined.