Optimized Dark Matter Searches in Deep Observations of Segue 1 with MAGIC als eBook von , Aleksic
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9783319231235 - Jelena Aleksi: Optimized Dark Matter Searches in Deep Observations of Segue 1 with MAGIC
Jelena Aleksi

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

Lieferung erfolgt aus/von: Deutschland DE NW EB DL

ISBN: 9783319231235 bzw. 3319231235, in Deutsch, Springer International Publishing, neu, E-Book, elektronischer Download.

Fr. 101.23 ( 103.52)¹
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Lieferung aus: Deutschland, Versandkostenfrei.
Optimized Dark Matter Searches in Deep Observations of Segue 1 with MAGIC: 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. Englisch, Ebook.
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9783319231235 - Jelena Aleksić: Optimized Dark Matter Searches in Deep Observations of Segue 1 with MAGIC (Springer Theses)
Jelena Aleksić

Optimized Dark Matter Searches in Deep Observations of Segue 1 with MAGIC (Springer Theses) (2015)

Lieferung erfolgt aus/von: Deutschland EN NW FE EB DL

ISBN: 9783319231235 bzw. 3319231235, in Englisch, 184 Seiten, Springer, neu, Erstausgabe, E-Book, elektronischer Download.

Lieferung aus: Deutschland, E-Book zum Download, Versandkostenfrei.
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. , Kindle Edition, Ausgabe: 1st ed. 2016, Format: Kindle eBook, Label: Springer, Springer, Produktgruppe: eBooks, Publiziert: 2015-11-06, Freigegeben: 2015-11-06, Studio: Springer.
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9783319231235 - Optimized Dark Matter Searches in Deep Observations of Segue 1 with MAGIC als eBook von Jelena Aleksi', Jelena Aleksic

Optimized Dark Matter Searches in Deep Observations of Segue 1 with MAGIC als eBook von Jelena Aleksi', Jelena Aleksic (2016)

Lieferung erfolgt aus/von: Vereinigtes Königreich Grossbritannien und Nordirland DE NW

ISBN: 9783319231235 bzw. 3319231235, in Deutsch, Springer International Publishing, neu.

Fr. 82.13 ( 83.99)¹
versandkostenfrei, unverbindlich
Lieferung aus: Vereinigtes Königreich Grossbritannien und Nordirland, Versandkostenfrei.
Optimized Dark Matter Searches in Deep Observations of Segue 1 with MAGIC ab 83.99 EURO 1st ed. 2016.
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