E-Locus - Ιδρυματικό Καταθετήριο Πανεπιστημίου Κρήτης - Machine learning methods to detect the turnaround radius of galaxy clusters

Αρχική Machine learning methods to detect the turnaround radius of galaxy clusters

Αποτελέσματα - Λεπτομέρειες

[Προσθήκη στο καλάθι]

Τίτλος

Machine learning methods to detect the turnaround radius of galaxy clusters

Συγγραφέας

Τριανταφύλλου, Νικόλαος

Σύμβουλος διατριβής

Παυλίδου, Βασιλική

Μέλος κριτικής επιτροπής

Bonfini, Paolo
Κορκίδης, Γεώργιος

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 (Ω_m) and dark energy (Ω_Λ). 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 Ω_Λ. 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.

Γλώσσα

Αγγλικά

Ημερομηνία έκδοσης

2022-11-25

Συλλογή

Σχολή/Τμήμα--Σχολή Θετικών και Τεχνολογικών Επιστημών--Τμήμα Φυσικής--Πτυχιακές εργασίες

Τύπος Εργασίας--Πτυχιακές εργασίες

Εμφανίσεις

693

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