Gravitational lensing

• Verfasst von: Merten, Julian
• Titel: Gravitational lensing
• Titelzusatz: an advanced method to recover the mass distribution of galaxy clusters
• Verf.angabe: Julian Merten
• Jahr: 2010
• Umfang: Online-Ressource (xvi, 168 S.)
• Angaben zum Titel: Übers. des Hauptsacht.: Über eine effiziente Methode zur Massenprofilrekonstruktion von Galaxienhaufen
• Hochschulschrift: Heidelberg, Univ., Diss., 2010
• DOI: doi:10.11588/heidok.00010757
• URN: urn:nbn:de:bsz:16-opus-107572
• Schlagwörter: (s)Kosmologie / (s)Gravitationslinse / (s)Galaxienhaufen / (s)Parallelisierung / (s)Dunkle Materie
• Schlagwörter: (s)Kosmologie / (s)Gravitationslinse / (s)Galaxienhaufen / (s)Parallelisierung / (s)Dunkle Materie
• Datenträger: Online-Ressource
• Dokumenttyp: Hochschulschrift
• Sprache: eng ger
• Reproduktion: Druckausg.: Merten, Julian: Gravitational lensing201020102010. - XVI, 168 S
• K10plus-PPN: 630483582
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

Abstract

This work shall be on clusters of galaxies. Those most massive, gravitationally bound objects in the observable Universe represent the high-mass tail of the mass function, rendering them as objects of interest for cosmology. Not only that they allow for the derivation of several cosmological parameters, but they are also ideal cosmic laboratories. Direct comparisons between numerical simulations and observations are particularly appealing in the case of clusters, as we will show. Maybe the most promising method to derive cluster properties from observations is gravitational lensing. Light rays of distant background sources are bent on the way to the observer, due to the high mass concentrations of clusters, and thereby carry important information about the deflector. In this work we develop an advanced, nonparametric method to recover the mass distribution of galaxy clusters by combining weak and strong gravitational lensing. The underlying numerical algorithm makes use of modern concepts of high-performance computing and is fully parallelised ...