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Durham e-Theses
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The nature of early-type galaxies in hierarchical models

Almeida, Cesário Manuel de Deus Lavaredas de (2008) The nature of early-type galaxies in hierarchical models. Doctoral thesis, Durham University.



In this Thesis we describe the properties of early-type galaxies in the context of hierarchical galaxy formation. We use two variants of the GALFORM model originally introduced by Cole et al.: the Baugh et al. and the Bower et al. models. We test the prescription defined by Cole et al. to calculate the sizes of bulges, by comparing GALFORM predictions with local observational data. We find that the model reproduces successfully several tight correlations observed for early-type galaxies: the relation between velocity dispersion and luminosity, the velocity dispersion-age relation and the Fundamental Plane. However, there is an important disagreement between the models and observations: in the model, the radii of the luminous spheroids are smaller than expected. We analyse how the physical ingredients involved in the calculation of the sizes influence these results. We explore the physics of massive galaxy formation in the models, by predicting the abundance, properties and clustering of luminous red galaxies (LRGs). Without adjusting any parameters in the two models, we find a good agreement between the GALFORM model and observations. We find that model LRGs are mainly elliptical galaxies, with stellar masses around 2 x 10(^11) h-(^1) M(_ʘ) and velocity dispersions of 250 kms-(^1). The models predict the correlation function of LRGs to be a power law down to small scales, which is in excellent agreement with the observational estimates. Finally, we predict the abundance, colour and clustering of submillimeter galaxies (SMGs), which are thought to be the progenitors of local massive early-type galaxies. At the wavelengths where these galaxies are detected, 850 um, the predictions using the standard GALFORM model are inaccurate, hence the time consuming GRASIL code is used in addition to GALFORM. We develop a new method, based on artificial neural networks, to rapidly generate galaxy spectra from a small set of properties.

Item Type:Thesis (Doctoral)
Award:Doctor of Philosophy
Thesis Date:2008
Copyright:Copyright of this thesis is held by the author
Deposited On:08 Sep 2011 18:27

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