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Identifier 000456628
Title Searching for a signature of turnaround in velocity profiles of galaxy clusters with machine learning
Alternative Title Αναζητώντας την "υπογραφή" της ακτίνας αναστροφής στις ταχύτητες των γαλαξιών σε σμήνη με χρήση μηχανικής μάθησης
Author Τριανταφύλλου, Νικόλαος Π.
Thesis advisor Παυλίδου, Βασιλική
Reviewer Bonfini, Paolo
Κορκίδης, Γεώργιος
Abstract Context: ΛCDM is currently recognized as the standard model in cosmology, but recent tensions between different types of high-accuracy data indicate potential inconsistencies. This generates an urgent need for a new examination of this model using novel observables. Pavlidou et al. have recently illustrated that the turnaround density, the average density within the turnaround radius of galaxy clusters, can be utilized as a probe for this purpose. Aims: We search for an efficient way of measuring the turnaround radius of galaxy clusters on the plane of the sky. Methods: We use the MultiDark Planck 2 and Virgo simulations to acquire projections of galaxy clusters and calculate their turnaround radius based on their velocity profiles. We use these data to train (and test) a Convolutional Neural Network (CNN), involving several layers of filters, normalization and regularization techniques. Results: We find that: (a) the turnaround radius is correlated to the central mass of a galaxy cluster and that the mass distribution around the cluster is almost an irrelevant feature to the model’s prediction. (b) The velocity dispersion of the galaxies also contains information related to the turnaround radius. The model’s accuracy is not significantly affected by the absence of information inside the R200 of the central overdensity in each cluster.
Language English
Issue date 2023-07-28
Collection   School/Department--School of Sciences and Engineering--Department of Physics--Post-graduate theses
  Type of Work--Post-graduate theses
Permanent Link https://elocus.lib.uoc.gr//dlib/f/5/2/metadata-dlib-1687763058-714550-7476.tkl Bookmark and Share
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