Speaker
Description
Neutrino and gamma-ray observatories might have more in common than you think. The MAGIC Telescope system, comprising a pair of 17 m Imaging Atmospheric Cherenkov Telescopes (IACTs), is located at Roque de Los Muchachos Observatory in La Palma, Spain. MAGIC is designed to detect gamma rays from around 50 GeV to over 50 TeV via atmospheric air showers. Arrays of IACTs rely on a complex pipeline in which each air shower imaged by the detectors generates a temporal stereo signal. The signal must then be calibrated, flattened into an image, cleaned, parameterized, and ultimately reconstructed by an ensemble of Random Forest algorithms. While Convolutional Neural Networks have shown promise for full event reconstruction in recent years, we demonstrate that neutrino event reconstruction techniques from IceCube can significantly reduce the path from raw telescope data to scientific output.
In contrast to standard analysis methods, this study directly leverages calibrated waveform data for the first time. These data consist of 30 ns temporal signals from each camera pixel. Due to the unconventional geometry of the MAGIC cameras and asynchronous clocks between pixels, we employ Graph Neural Networks (GNNs) as a classification algorithm for the first time in an IACT, using the neutrino GraphNeT framework and DynEdge model. In addition, we find that DeepIce, a transformer model for arrival direction reconstruction in IceCube, provides robust reconstruction when applied to MAGIC data. These findings show that GNNs and transformers excel at reconstructing raw MAGIC data, demonstrating the benefits of when techniques can hop between domains.
| Broad physics domain | Astroparticle Physics |
|---|---|
| AI/ML technique(s) to be presented | GNN |
