Πτυχιακές εργασίες
Τρέχουσα Εγγραφή: 63 από 196
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Τίτλος |
Machine learning methods to detect the turnaround radius of galaxy clusters |
Συγγραφέας
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Τριανταφύλλου, Νικόλαος
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Σύμβουλος διατριβής
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Παυλίδου, Βασιλική
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Μέλος κριτικής επιτροπής
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Bonfini, Paolo
Κορκίδης, Γεώργιος
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Περίληψη |
ΛCDM is considered today the standard model of cosmology. In spite of ΛCDM’s success, recent findings seem to reveal tensions (Riess et al., 2018; Hildebrandt et al., 2017;
Joudaki et al., 2017) thus, a new probe of this model would be quite imperative. According to ΛCDM, the Universe contains dark energy in the form of a cosmological constant
Λ which is responsible for the evident accelerated expansion of the Universe. Current evidence of its existence is based on the relation of the present-day values of the cosmological
density parameters of matter (Ωm) and dark energy (ΩΛ). It has been recently shown
(Pavlidou, Korkidis, Tomaras, & Tanoglidis, 2020) that the turnaround density, and hence,
the turnaround radius of galaxy clusters at low redshifts could be a novel way to independently constrain ΩΛ. Scalable techniques to measure it effectively in a large number of
clusters at large distances have not been developed yet and machine learning is a natural
candidate. Consequently, the scope of this work is to investigate for a new way of measuring the turnaround radius of galaxy clusters, at the plane of the sky (i.e. observationally),
through machine learning methods and evaluate their accuracy.
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Γλώσσα |
Αγγλικά |
Ημερομηνία έκδοσης |
2022-11-25 |
Συλλογή
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Σχολή/Τμήμα--Σχολή Θετικών και Τεχνολογικών Επιστημών--Τμήμα Φυσικής--Πτυχιακές εργασίες
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Τύπος Εργασίας--Πτυχιακές εργασίες
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Μόνιμη Σύνδεση |
https://elocus.lib.uoc.gr//dlib/b/9/b/metadata-dlib-1664879064-557635-5942.tkl
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Εμφανίσεις |
651 |