Unmasking the gamma-ray sky: comprehensive and reproducible analysis for Cherenkov telescopes
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Date
2019
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Abstract
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.
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Keywords
Maschinelles Lernen, AI, Datenanlyse, Software, Gammastrahlung, Pulsar, Krebsnebel, Monte Carlo