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Monitoring the High Energy Universe – Open, Reproducible, Machine Learning Based Analysis for the First G-APD Cherenkov Telescope

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phd_thesis

Software

To build my thesis, you will need:

  1. The python packages defined in environment.yaml. Create a new conda environment using:
$ conda env create -n phd_maxnoe -f environment.yaml
$ conda activate phd_maxnoe
  1. TeXLive 2020

  2. make

  3. the input data

Input data

All input files to produce the final plots in the thesis are stored on the storage server big-tank.app.tu-dortmund.de in /POOL/users/mnoethe/phd_thesis.

These are the higher level analysis results (so after FACT-Tools processing), as the Raw Data would be to much to download to a single machine and do the processing.

Link that storage locationto data in the root of this repository, e.g. by using ln -s /net/big-tank/POOL/users/mnoethe/phd_thesis data or by using sshfs or just downloading the data.

Final PDF/A document

Unfortunately, I was unable to produce a fully PDF/A-3u compatible document out of matplotlib/pgfplots/lualatex.

As a final step, a conversion using Adobe Acrobat DC was done, this fixed the one remaining issue, namely the /Interpolate true setting for images.

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Monitoring the High Energy Universe – Open, Reproducible, Machine Learning Based Analysis for the First G-APD Cherenkov Telescope

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