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Optimized Dark Matter Searches in Deep Observations of Segue 1 with MAGIC
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Optimized Dark Matter Searches in Deep Observations of Segue 1 with MAGIC

Softcover reprint of the original 1st ed. 2016

Book Details

Format Paperback / Softback
Book Series Springer Theses
ISBN-10 3319370715
ISBN-13 9783319370712
Edition Softcover reprint of the original 1st ed. 2016
Publisher Springer International Publishing AG
Imprint Springer International Publishing AG
Country of Manufacture CH
Country of Publication GB
Publication Date Aug 23rd, 2016
Print length 184 Pages
Ksh 16,200.00
Werezi Extended Catalogue 0 in stock

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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.

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.


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