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.
Online gamma-hadron separation using CNN
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/hadron 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. The developed convolutional neural networks get the probability of the gamma reconstruction for each event as a result. You can also check yourself if you can make gamma/hadron separation using the telescope image (instruction is below).
Instruction how to define gamma-event using telescope image
A shower image is fitted as an ellipse. The ellipse is characterized by its axes and has parameters length, width, distance and the angular miss-alignment of the major axis α. In comparison with hadron showers gamma-ray ones have more elliptic shape, less width and major axis pointed to the source.
If the telescope is pointed at a known source that source is placed in the center of the telescope’s camera. Therefore axes of gamma-induced images are pointed to the center of the camera.
To demonstrate the operation of the neural networks there five datasets have been prepaired, each contains 10 telescope events with the event information: event ID, particle energy (TeV), particle type, image. You can also download these datasets for your further research:
Process you own dataset
To test the neural nets on your own events just upload your dataset and click process.
The file structure and an example how to print the structure of the HDF5 file using python are described below you can find here.