In November 2012, the European Space Agency (ESA) is planning to launch Gaia, a mission designed to measure with microarcsecond accuracy the astrometric properties of over a billion stars. Microarcsecond astrometry requires extremely accurate positional measurements of individual stellar transits on the focal plane, which can be disrupted by radiation-induced Charge Transfer Inefficiency (CTI). Gaia will suffer radiation damage, impacting on the science performance, which has led to a series of Radiation Campaigns (RCs) being carried out by industry to investigate these issues. The goal of this thesis is to rigorously assess these campaigns and facilitate how to deal with CTI in the data processing. We begin in Chapter 1 by giving an overview of astrometry and photometry, introducing the concept of stellar parallax, and establishing why observing from space is paramount for performing global, absolute astrometry. As demonstrated by Hipparcos, the concept is sound. After reviewing the Gaia payload and discussing how astrometric and photometric parameters are determined in practice, we introduce the issue of radiation-induced CTI and how it may be dealt with. The on board mitigating strategies are investigated in detail in Chapter 2. Here we analyse the effects of radiation damage as a function of magnitude with and without a diffuse optical background, charge injection and the use of gates, and also discover a number of calibration issues. Some of these issues are expected to be removed during flight testing, others will have to be dealt with as part of the data processing, e.g. CCD stitches and the charge injection tail. In Chapter 3 we turn to look at the physical properties of a Gaia CCD. Using data from RC2 we probe the density of traps (i.e. damaged sites) in each pixel and, for the first time, measure the Full Well Capacity of the Supplementary Buried Channel, a part of every Gaia pixel that constrains the passage of faint signals away from the bulk of traps throughout the rest of the pixel. The Data Processing and Analysis Consortium (DPAC) is currently adopting a 'forward modelling' approach to calibrate radiation damage in the data processing. This incorporates a Charge Distortion Model (CDM), which is investigated in Chapter 4. We find that although the CDM performs well there are a number of degeneracies in the model parameters, which may be probed further by better experimental data and a more realistic model. Another way of assessing the performance of a CDM is explored in Chapter 5. Using a Monte Carlo approach we test how well the CDM can extract accurate image parameters. It is found that the CDM must be highly robust to achieve a moderate degree of accuracyand that the fitting is limited by assigning finite window sizes to the image shapes. Finally, in Chapter 6 we summarise our findings on the campaign analyses, the on-board mitigating strategies and on how well we are currently able to handle radiation damage in the data processing.

The problem of statistical calibration of a measuring instrument can be framed both in a statistical context as well as in an engineering context. In the first, the problem is dealt with by distinguishing between the "classical" approach and the "inverse" regression approach. Both of these models are static models and are used to estimate "exact" measurements from measurements that are affected by error. In the engineering context, the variables of interest are considered to be taken at the time at which you observe the measurement. The Bayesian time series analysis method of Dynamic Linear Models (DLM) can be used to monitor the evolution of the measures, thus introducing a dynamic approach to statistical calibration. The research presented employs the use of Bayesian methodology to perform statistical calibration. The DLM framework is used to capture the time-varying parameters that may be changing or drifting over time. Dynamic based approaches to the linear, nonlinear, and multivariate calibration problem are presented in this dissertation. Simulation studies are conducted where the dynamic models are compared to some well known "static'" calibration approaches in the literature from both the frequentist and Bayesian perspectives. Applications to microwave radiometry are given.