Particle Identification

Ground-based gamma-ray astronomy studies very energetic radiation of galactic and extragalactic origin by specially designed telescopes, the so-called Imaging Air Cherenkov Telescopes (IACTs). With this technique, gamma-rays are observed on the ground optically via the Cherenkov light emitted by extensive showers of secondary particles in the air when a very-high-energy gammaray strikes the atmosphere. Gamma-rays of such energies contribute only a fraction below one per million to the flux of cosmic rays, most of which are protons. Nevertheless, being particles without electric charge they can be extrapolated back to their origin, which makes them the best “messengers” of exotic and extreme processes and physical conditions in the Universe. That is why particle identification (gamma-ray discrimination against the cosmic-ray background) is an essential part of data analysis for the IACT technique.

Here you can find a prototype of the Astroparticle CNN client developed for gamma-astronomy tasks. This prototype provides access to on-line analysis of the gamma/proton separation using convolutional neural networks developed as part of the GRADLCI project. The Monte Carlo events of the TAIGA-IACT telescope are used to operate this prototype.

Firstly, you have to choose one of the proposed datasets:

  • pub1.h5
  • pub2.h5
  • pub3.h5
  • pub4.h5

Each dataset contains 10 telescope events. By using Events URL you can get the information about this events:

  • Event ID
  • Energy TeV
  • particle type
  • image

Then by using the Process you apply the neural nets to your chosen dataset and get the probability of the gamma reconstruction for each event as a result.

Go to an Astroparticle CNN client

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