The Analysis, not the Aperture: End-to-End Transformer Reconstruction for Imaging Atmospheric Cherenkov Telescopes

Abstract

Imaging Atmospheric Cherenkov Telescopes (IACTs) detect very-high-energy gamma rays by imaging the nanosecond Cherenkov flash of the air shower they initiate in the Earth's atmosphere. For four decades the first steps of IACT event reconstruction have been essentially unchanged, relying on a heavy parameterisation and dimensionality reduction of the recorded images. This is reasonable when the image is bright, but discards important information when only a few tens of Cherenkov photons are recorded, which is a primary reason why small telescopes perform poorly at sub-TeV energies. We show that this limitation is a property of the analysis rather than of the hardware. We simulate a deliberately simple and idealised compact telescope and treat each event as a short movie that is passed directly to a video vision transformer with a factorised spatio-temporal encoder. A single composite network with a gradient-normalised multi-task loss performs gamma/hadron classification, energy regression and arrival-direction regression at once. This is the first application of a video vision transformer to IACT data. We compare it against an optimised standard analysis on the same dataset. The transformer lowers the energy threshold by a factor of three, from 0.22 to 0.07 TeV, and reconstructs arrival directions down to 0.05 TeV. At 0.2 TeV it increases the effective collection area by a factor of three, and raises the gamma/hadron separation power from an area under the receiver operating characteristic curve of 0.80 to 0.91. At 0.05 TeV, where the standard analysis retains almost nothing, that area grows by nearly two orders of magnitude. These results show promising new opportunities for compact and affordable telescopes operating at sub-TeV energies, paving the way for a broader exploration of time-domain astrophysics.