Measuring energy spectra of TeV gamma-ray emission from the Cygnus region of our galaxy with Milagro

High energy gamma rays can provide fundamental clues to the origins of cosmic rays. In this thesis, TeV gamma-ray emission from the Cygnus region is studied. Previously the Milagro experiment detected five TeV gamma-ray sources in this region and a significant excess of TeV gamma rays whose origin is still unclear. To better understand the diffuse excess the separation of sources and diffuse emission is studied using the latest and most sensitive data set of the Milagro experiment. In addition, a newly developed technique is applied that allows the energy spectrum of the TeV gamma rays to be reconstructed using Milagro data. No conclusive statement can be made about the spectrum of the diffuse emission from the Cygnus region because of its low significance of 2.2 σ above the background in the studied data sample. The entire Cygnus region emission is best fit with a power law with a spectral index of α=2.40 (68% confidence interval: 1.35-2.92) and a exponential cutoff energy of 31.6 TeV (10.0-251.2 TeV). In the case of a simple power law assumption without a cutoff energy the best fit yields a spectral index of α=2.97 (68% confidence interval: 2.83-3.10). Neither of these best fits are in good agreement with the data. The best spectral fit to the TeV emission from MGRO J2019+37, the brightest source in the Cygnus region, yields a spectral index of α=2.30 (68% confidence interval: 1.40-2.70) with a cutoff energy of 50.1 TeV (68% confidence interval: 17.8-251.2 TeV) and a spectral index of α=2.75 (68% confidence interval: 2.65-2.85) when no exponential cutoff energy is assumed. According to the present analysis, MGRO J2019+37 contributes 25% to the differential flux from the entire Cygnus at 15 TeV.

STATISTICAL METHODS FOR RARE AND COMMON VARIANT ASSOCIATION STUDIES

Complex human diseases are a major challenge for biological research. The goal of my research is to develop effective methods for biostatistics in order to create more opportunities for the prevention and cure of human diseases. This dissertation proposes statistical technologies that have the ability of being adapted to sequencing data in family-based designs, and that account for joint effects as well as gene-gene and gene-environment interactions in the GWA studies. The framework includes statistical methods for rare and common variant association studies. Although next-generation DNA sequencing technologies have made rare variant association studies feasible, the development of powerful statistical methods for rare variant association studies is still underway. Chapter 2 demonstrates two adaptive weighting methods for rare variant association studies based on family data for quantitative traits. The results show that both proposed methods are robust to population stratification, robust to the direction and magnitude of the effects of causal variants, and more powerful than the methods using weights suggested by Madsen and Browning [2009]. In Chapter 3, I extended the previously proposed test for Testing the effect of an Optimally Weighted combination of variants (TOW) [Sha et al., 2012] for unrelated individuals to TOW &ndash F, TOW for Family &ndash based design. Simulation results show that TOW &ndash F can control for population stratification in wide range of population structures including spatially structured populations, is robust to the directions of effect of causal variants, and is relatively robust to percentage of neutral variants. In GWA studies, this dissertation consists of a two &ndash locus joint effect analysis and a two-stage approach accounting for gene &ndash gene and gene &ndash environment interaction. Chapter 4 proposes a novel two &ndash stage approach, which is promising to identify joint effects, especially for monotonic models. The proposed approach outperforms a single &ndash marker method and a regular two &ndash stage analysis based on the two &ndash locus genotypic test. In Chapter 5, I proposed a gene &ndash based two &ndash stage approach to identify gene &ndash gene and gene &ndash environment interactions in GWA studies which can include rare variants. The two &ndash stage approach is applied to the GAW 17 dataset to identify the interaction between KDR gene and smoking status.