Accounting for Calibration Uncertainty in Detectors for High-Energy Astrophysics
- Author(s): Xu, Jin
- Advisor(s): Yu, Yaming
- van Dyk, David
- et al.
Systematic instrumental uncertainties in astronomical analyses have been generally ignored in data analysis due to the lack of robust principled methods, though the importance of incorporating instrumental calibration uncertainty is widely recognized by both users and instrument builders. Ignoring calibration uncertainty can cause bias in the estimation of source model parameters and can lead to underestimation of the variance of these estimates. Lee et al. (2011) introduced a so-called pragmatic Bayesian method to address this problem. The method is "pragmatic" in that it introduces an ad hoc technique that simplifies computation by assuming that the current data is not useful in narrowing the uncertainty for the calibration product, i.e., that the prior and posterior distributions for the calibration products are the same.
In the thesis, we focus on incorporating calibration uncertainty into a principled Bayesian X-ray spectral analysis, specifically we account for uncertainty in the so-called effective area curve and the photon redistribution matrix. X-ray spectral analysis models the distribution of the energies of X-ray photons emitted from an astronomical source. The effective area curve of an X-ray detector describes its sensitive as a function of the energy of incoming photons, and the photon redistribution matrix describes the probability distribution of the recorded (discrete) energy of a photon as a function of the true (discretized) energy. Starting with the effective area curve, we follow Lee et al. (2011) and use a principle component analysis (PCA) to efficiently represent the uncertainty. Here, however, we leverage this representation to enable a principled, fully Bayesian method to account for calibration uncertainty in high-energy spectral analysis. For the photon redistribution matrix, we first model each conditional distribution as a normal distribution and then apply PCA to the parameters describing the normal models. This results in an efficient low-dimensional summary of the uncertainty in the redistribution matrix. Our methods for both calibration products are compared with standard analysis techniques and the pragmatic Bayesian method of Lee et al. (2011). The advantage of the fully Bayesian method is that it allows the data to provide information not only for estimation of the source parameters but also for the calibration product; we demonstrate this for the effective area curve. In this way, our fully Bayesian approach can yield more accurate and efficient estimates of the source parameters, and valid estimates of their uncertainty. Moreover, the fully Bayesian approach is the only method that allows us to make a valid inference about the effective area curve itself, quantifying which possible curves are most consistent with the data.