Transformer models applied to simulated water-Cherenkov array data improve gamma-hadron separation and reconstruction of direction, core position, and energy compared to established techniques.
Ctlearn: Deep learning for gamma-ray astronomy.arXiv preprint arXiv:1912.09877, 2019
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LACT array direction reconstruction using 2D Gaussian fits and LightGBM quality weighting achieves sub-0.06 degree angular resolution at 100 TeV, with potential further gains from neural ratio estimation.
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Enhancing event reconstruction for $\gamma$-ray particle detector arrays using transformers
Transformer models applied to simulated water-Cherenkov array data improve gamma-hadron separation and reconstruction of direction, core position, and energy compared to established techniques.
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Enhancing the Angular Resolution of Large Array of imaging atmospheric Cherenkov Telescope (LACT) at Ultra-High Energies
LACT array direction reconstruction using 2D Gaussian fits and LightGBM quality weighting achieves sub-0.06 degree angular resolution at 100 TeV, with potential further gains from neural ratio estimation.