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Title Machine learning methods to detect the turnaround radius of galaxy clusters
Author Τριανταφύλλου, Νικόλαος
Thesis advisor Παυλίδου, Βασιλική
Reviewer Bonfini, Paolo
Κορκίδης, Γεώργιος
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.
Language English
Issue date 2022-11-25
Collection   School/Department--School of Sciences and Engineering--Department of Physics--Graduate theses
  Type of Work--Graduate theses
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