Speaker
Description
Photons are important objects at collider experiments. For example, the
Higgs boson is studied with high precision in the diphoton decay channel. For this purpose, it is crucial to achieve the best possible spatial resolution for photons and to discriminate against other particles which mimic the photon signature, mostly Lorentz-boosted $\pi^0\to\gamma\gamma$ decays.
In this talk, a study of super-resolution algorithms for photons is presented.
We utilize Wasserstein generative adversarial networks based on the ESRGAN architecture, augmented by a physics-driven perceptual loss term and other modifications.
The energy depositions of simulated showers of photons and neutral-pion decays in a PbWO4 calorimeter are treated as 2D images, which are upsampled with our super-resolution networks by a factor of four in each dimension. The generated images are able to reproduce features of the simulated high-resolution showers that are not obvious from the nominal resolution. It is shown that using the artificially-enhanced images
for the reconstruction of shower-shape variables and the positions of the
shower centers results in significant improvements. In addition, it is illustrated that the performance of deep-learning based identification algorithms can be enhanced by using super-resolution as image-preprocessing, if only low statistics are available in the classifiers’ training sample.
