Heavily obscured AGN: an ideal laboratory to study the early co-evolution of galaxies and black holes

Obscured AGN are a crucial ingredient to understand the full growth history of super massive black holes and the coevolution with their host galaxies, since they constitute the bulk of the BH accretion. In the distant Universe, many of them are hosted by submillimeter galaxies (SMGs), characterized by a high production of stars and a very fast consumption of gas. Therefore, the analysis of this class of objects is fundamental to investigate the role of the ISM in the early coevolution of galaxies and black holes. We present a multiwavelength study of a sample of six obscured X-ray selected AGN at z>2.5 in the CDF-S, detected in the far-IR/submm bands. We performed the X-ray spectral analysis based on the 7Ms Chandra dataset, which provides the best X-ray spectral information currently available for distant AGN. We were able to place constraints on the obscuring column densities and the intrinsic luminosities of our targets. Moreover, we built up the UV to FIR spectral energy distributions (SEDs) by combining the broad-band photometry from CANDELS and the Herschel catalogs, and analyzed them by means of an SED decomposition technique. Therefore, we derived important physical parameters of both the host galaxy and the AGN. In addition, we obtained, through an empirical calibration, the gas mass in the host galaxy and assessed the galaxy sizes in order to estimate the column density associated with the host ISM. The comparison of the ISM column densities with the values measured from the X-ray spectral analysis pointed out that the contribution of the host ISM to the obscuration of the AGN emission can be substantial, ranging from ~10% up to ~100% of the value derived from the X-ray spectra. The absorption may occur at different physical scales in these sources and, in particular, the medium in the host galaxy is an ingredient that should be taken into account, since it may have a relevant role in driving the early co-evolution of galaxies with their black holes.

Rheological Characterization of Bitumen and Asphalt including the effect of Added-recycled Material

This thesis evaluates the rheological behaviour of asphalt mixtures and the corresponding extracted binders from the mixtures containing different amounts of Reclaimed Asphalt (RA). Generally, the use of RA is limited to certain amounts. The study materials are Stone Mastic Asphalts including a control sample with 0% RA, and other samples with RA rates of 30%, 60% and 100%. Another set of studied mixtures are Asphalt Concretes (AC) types with again a control mix having 0% RA rate and the other mixtures designs containing 30%, 60% and 90% of reclaimed asphalt which also contain additives. In addition to the bitumen samples extracted from asphalt mixes, there are bitumen samples directly extracted from the original RA. To characterize the viscoelastic behaviour of the binders, Dynamic Shear Rheometer (DSR) tests were conducted on bitumen specimens. The resulting influence of the RA content in the bituminous binders are illustrated through master curves, black diagrams and Cole-Cole plots with regressing these experimental data by the application of the analogical 2S2P1D and the analytical CA model. The advantage of the CA model is in its limited number of parameters and thus is a simple model to use. The 2S2P1D model is an analogical rheological model for the prediction of the linear viscoelastic properties of both asphalt binders and mixtures. In order to study the influence of RA on mixtures, the Indirect Tensile Test (ITT) has been conducted. The master curves of different mixture samples are evaluated by regressing the test data points to a sigmoidal function and subsequently by comparing the master curves, the influence of RA materials is studied. The thesis also focusses on the applicability and also differences of CA model and 2S2P1D model for bitumen samples and the sigmoid function for the mixtures and presents the influence of the RA rate on the investigated model parameters.