Science With The Cherenkov Telescope Array PDF Download
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Author: The Cta Consortium
Publisher: World Scientific
ISBN: 9813270101
Category : Science
Languages : en
Pages : 365
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Book Description
This book summarizes the science to be carried out by the upcoming Cherenkov Telescope Array, a major ground-based gamma-ray observatory that will be constructed over the next six to eight years. The major scientific themes, as well as core program of key science projects, have been developed by the CTA Consortium, a collaboration of scientists from many institutions worldwide.CTA will be the major facility in high-energy and very high-energy photon astronomy over the next decade and beyond. CTA will have capabilities well beyond past and present observatories. Thus, CTA's science program is expected to be rich and broad and will complement other major multiwavelength and multimessenger facilities. This book is intended to be the primary resource for the science case for CTA and it thus will be of great interest to the broader physics and astronomy communities. The electronic version (e-book) is available in open access.
Author: The Cta Consortium
Publisher: World Scientific
ISBN: 9813270101
Category : Science
Languages : en
Pages : 365
Get Book Here
Book Description
This book summarizes the science to be carried out by the upcoming Cherenkov Telescope Array, a major ground-based gamma-ray observatory that will be constructed over the next six to eight years. The major scientific themes, as well as core program of key science projects, have been developed by the CTA Consortium, a collaboration of scientists from many institutions worldwide.CTA will be the major facility in high-energy and very high-energy photon astronomy over the next decade and beyond. CTA will have capabilities well beyond past and present observatories. Thus, CTA's science program is expected to be rich and broad and will complement other major multiwavelength and multimessenger facilities. This book is intended to be the primary resource for the science case for CTA and it thus will be of great interest to the broader physics and astronomy communities. The electronic version (e-book) is available in open access.
Author: Miguel A. Sánchez-Conde
Publisher: MDPI
ISBN: 3039360442
Category : Mathematics
Languages : en
Pages : 220
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Book Description
An important, open research topic today is to understand the relevance that dark matter halo substructure may have for dark matter searches. In the standard cosmological model, halo substructure or subhalos are predicted to be largely abundant inside larger halos, for example, galaxies such as ours, and are thought to form first and later merge to form larger structures. Dwarf satellite galaxies—the most massive exponents of halo substructure in our own galaxy—are already known to be excellent targets for dark matter searches, and indeed, they are constantly scrutinized by current gamma-ray experiments in the search for dark matter signals. Lighter subhalos not massive enough to have a visible counterpart of stars and gas may be good targets as well, given their typical abundances and distances. In addition, the clumpy distribution of subhalos residing in larger halos may boost the dark matter signals considerably. In an era in which gamma-ray experiments possess, for the first time, the exciting potential to put to test the preferred dark matter particle theories, a profound knowledge of dark matter astrophysical targets and scenarios is mandatory should we aim for accurate predictions of dark matter-induced fluxes for investing significant telescope observing time on selected targets and for deriving robust conclusions from our dark matter search efforts. In this regard, a precise characterization of the statistical and structural properties of subhalos becomes critical. In this Special Issue, we aim to summarize where we stand today on our knowledge of the different aspects of the dark matter halo substructure; to identify what are the remaining big questions, and how we could address these; and, by doing so, to find new avenues for research.
Author: Houjun Mo
Publisher: Cambridge University Press
ISBN: 0521857937
Category : Science
Languages : en
Pages : 841
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Book Description
A coherent introduction for researchers in astronomy, particle physics, and cosmology on the formation and evolution of galaxies.
Author: Gianfranco Bertone
Publisher: Cambridge University Press
ISBN: 0521763681
Category : Science
Languages : en
Pages : 763
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Book Description
Describes the dark matter problem in particle physics, astrophysics and cosmology for graduate students and researchers.
Author: F.A. Aharonian
Publisher: AIP Conference Proceedings (Nu
ISBN:
Category : Science
Languages : en
Pages : 940
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Book Description
The fourth in a series of major international conferences in the field of Gamma-Ray Astronomy, attended by leading experts as well as young scientists from many universities and research centers. The symposium covered the basic observational and many theoretical topics related to ground and space-based Gamma-Ray Astronomy, Astroparticle Physics and Cosmology.
Author: Scott Dodelson
Publisher: Academic Press
ISBN: 0122191412
Category : Science
Languages : en
Pages : 462
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Book Description
An advanced text for senior undergraduates, graduate students and physical scientists in fields outside cosmology. This is a self-contained book focusing on the linear theory of the evolution of density perturbations in the universe, and the anisotropiesin the cosmic microwave background.
Author: Stefano Profumo
Publisher: Wspc (Europe)
ISBN: 9781786340009
Category : Science
Languages : en
Pages : 270
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Book Description
Particle dark matter: the name of the game -- The thermal relic paradigm: zeroth-order lessons from cosmology -- The thermal relic paradigm: a closer look -- The art of WIMP direct detection -- Indirect dark matter searches -- Searching for dark matter with particle colliders -- Axions and axion-like particles as dark matter -- Sterile neutrinos as dark matter particles -- Bestiarium: a short, biased compendium of notable dark matter particle candidates and models
Author: Jelena Aleksić
Publisher: Springer
ISBN: 3319231235
Category : Science
Languages : en
Pages : 213
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Book Description
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: Fulvio Melia
Publisher: Princeton University Press
ISBN: 9780691131290
Category : Science
Languages : en
Pages : 324
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Book Description
Publisher description
Author: Rainer Fischer
Publisher: A I P Press
ISBN:
Category : Mathematics
Languages : en
Pages : 632
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Book Description
All papers were peer reviewed. Bayesian Inference and Maximum Entropy Methods in Science and Engineering provide a framework for analyzing ill-conditioned data. Maximum Entropy is a theoretical method to draw conclusions when little information is available. Bayesian probability theory provides a formalism for scientific reasoning by analyzing noisy or imcomplete data using prior knowledge.
