Welcome to OrcaSong’s documentation!

OrcaSong is a part of the Deep Learning efforts for the neutrino telescope KM3NeT.

Find more information about KM3NeT on http://www.km3net.org.

In this regard, 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/make_nn_images.py.

As of now, the documentation contains a small introduction to get started and and a complete API documentation. Please feel free to contact me or just open an issue on Gitlab / Github if you have any suggestions.

Contents:

• Getting started with OrcaSong
  • Introduction
  • Preprocessing
  • Usage of OrcaSong
• OrcaSong Plag
  • Basic Use
  • Adding mc_info
• Contributing
  • Contact Us
  • Filing Bugs or Feature Requests
  • Make a Fork of OrcaSong
• Source (Git)
• API Reference
  • orcasong
  • orcasong_contrib

Indices and tables

• Index
• Module Index
• Search Page