Mass Loss in Population II Red Giants: an IRAC@Spitzer Survey of Galactic Globular Clusters

With the goal of studying ML along the RGB, mid-IR observations of a carefully selected sample of 17 Galactic globular clusters (GGCs) with different metallicity and horizontal branch (HB) morphology have been secured with IRAC on board Spitzer: a global sample counting about 8000 giant has been obtained. Suitable complementary photometry in the optical and near-IR has been also secured in order to properly characterize the stellar counterparts to the Spitzer sources and their photospheric parameters. Stars with color (i.e. dust) excess have been identified, their likely circumstellar emission quantified and modelled, and empirical estimates of mass loss rates and timescales obtained. We find that mass loss rates increases with increasing stellar luminosity and decreasing metallicity. For a given luminosity, we find that ML rates are systematically higher than the prediction by extrapolating the Reimers law. CMDs constructed from ground based near-IR and IRAC bands show that at a given luminosity some stars have dusty envelopes and others do not. From this, we deduce that the mass loss is episodic and is ``on'' for some fraction of the time. The total mass lost on the RGB can be easily computed by multiplying ML rates by the ML timescales and integrating over the evolutionary timescale. The average total mass lost moderately increases with increasing metallicity, and for a given metallicity is systematically higher in clusters with extended blue HB.

Employing Distances in Design-based Spatial Estimation

In the last couple of decades we assisted to a reappraisal of spatial design-based techniques. Usually the spatial information regarding the spatial location of the individuals of a population has been used to develop efficient sampling designs. This thesis aims at offering a new technique for both inference on individual values and global population values able to employ the spatial information available before sampling at estimation level by rewriting a deterministic interpolator under a design-based framework. The achieved point estimator of the individual values is treated both in the case of finite spatial populations and continuous spatial domains, while the theory on the estimator of the population global value covers the finite population case only. A fairly broad simulation study compares the results of the point estimator with the simple random sampling without replacement estimator in predictive form and the kriging, which is the benchmark technique for inference on spatial data. The Monte Carlo experiment is carried out on populations generated according to different superpopulation methods in order to manage different aspects of the spatial structure. The simulation outcomes point out that the proposed point estimator has almost the same behaviour as the kriging predictor regardless of the parameters adopted for generating the populations, especially for low sampling fractions. Moreover, the use of the spatial information improves substantially design-based spatial inference on individual values.