Unmasking the gamma-ray sky: comprehensive and reproducible analysis for Cherenkov telescopes - Brügge, Kai (Technische Universität Dortmund, 2019)
Imaging atmospheric Cherenkov telescopes (IACT) observe the sky in the highest energy ranges. From the remnants of cataclysmic supernovae to jets powered by supermassive blackholes in the center of distant galaxies, IACTs can capture the light emerging from the most extreme sources in the universe. With the recent advent of multi-messenger astronomy it has become critical for IACTs to publicly share their data and software. For the first time since the inception of IACT technology, in a combined effort of the H.E.S.S., MAGIC, VERITAS, and FACT collaborations, observations of the Crab Nebula were made available to the general public in a common data format. The first part of my thesis demonstrates the viability of the common data format by performing a spectral analysis of the Crab Nebula on the published datasets. The text gives detailed descriptions and mathematical formalizations of instrument response functions (IRFs) and the statistical modeling used for typical spectral analyses. This is essential to understand the measurement process of IACTs. The ultimate goal of this part of the thesis will be to use Hamilton Markov Monte Carlo methods to test spectral models and unfold flux-point estimates for the Crab Nebula. The common data format paves the road for the operation of the upcoming Cherenkov Telescope Array (CTA). Once CTA has been constructed, it will be the largest and most sophisticated experiment in the field of ground-based gamma-ray astronomy. It will be operated as an open observatory allowing anyone to access the recorded data. The second part of my thesis concentrates on reproducible analysis for the Cherenkov Telescope Array (CTA). Once operational, CTA will produce a substantial amount of data creating new challenges for data storage and analysis technologies. In this part of the thesis I use simulated CTA data to build a comprehensive analysis chain based on fully open-source methods. The goal is to create a pipeline that rivals the physics performance of CTA’s closed-source reference implementation. Every step of the analysis, from raw-data processing to the calculation of sensitivity curves, will be optimized with respect to complexity, reproducibility and run-time.
Keywords: Maschinelles Lernen; AI; Datenanlyse; Software; Gammastrahlung; Pulsar; Krebsnebel; Monte Carlo; 530; Maschinelles Lernen; Künstliche Intelligenz; Cherenkov Telescope Array; Gammastrahlung; Pulsar; Monte-Carlo-Simulation; Technische Universität Dortmund. Lehrstuhl Experimentelle Physik 5