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index.rst 1.46 KiB

Welcome to the documentation of OrcaSong!

OrcaSong is a part of the Deep Learning efforts of the neutrino telescope KM3NeT. Find more information about KM3NeT on http://www.km3net.org.

In this context, OrcaSong is a project that produces KM3NeT event images based on the raw detector data. This means that OrcaSong takes a datafile with (neutrino-) events and based on this data, it produces 2D/3D/4D 'images' (histograms). Currently, only simulations with a hdf5 data format are supported as an input. These event 'images' are required for some Deep Learning machine learning algorithms, e.g. Convolutional Neural Networks.

As of now, only ORCA detector simulations are supported, but ARCA geometries can be easily implemented as well.

The main code for generating the images is located in orcanet/data_to_images.py. If the simulated hdf5 files are not calibrated yet, you need to specify the directory of a .detx file in 'data_to_images.py'.

This documentation is currently WIP, and as of now, it only offers an (extensive) API documentation. Please feel free to contact me or just open an issue on Gitlab / Github if you have any suggestions.

Indices and tables

  • :ref:`genindex`
  • :ref:`modindex`
  • :ref:`search`