Graduate theses
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Current Record: 46 of 179
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Title |
Machine learning methods to detect the turnaround radius of galaxy clusters |
Author
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Τριανταφύλλου, Νικόλαος
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Thesis advisor
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Παυλίδου, Βασιλική
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Reviewer
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Bonfini, Paolo
Κορκίδης, Γεώργιος
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Abstract |
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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Language |
English |
Issue date |
2022-11-25 |
Collection
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School/Department--School of Sciences and Engineering--Department of Physics--Graduate theses
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Type of Work--Graduate theses
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Permanent Link |
https://elocus.lib.uoc.gr//dlib/b/9/b/metadata-dlib-1664879064-557635-5942.tkl
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Views |
565 |