Conveners
Super Resolution, Reweighting, and Refinement
- Kevin Pedro (Fermi National Accelerator Lab. (US))
At experiments at the LHC, a growing reliance on fast Monte Carlo applications will accompany the high luminosity and detector upgrades of the Phase 2 era. Traditional FastSim applications which have already been developed over the last decade or more may help to cope with these challenges, as they can achieve orders of magnitude greater speed than standard full simulation applications....
Machine learning-based simulations, especially calorimeter simulations, are promising tools for approximating the precision of classical high energy physics simulations with a fraction of the generation time. Nearly all methods proposed so far learn neural networks that map a random variable with a known probability density, like a Gaussian, to realistic-looking events. In many cases, physics...
Accurately reconstructing particles from detector data is a critical challenge in experimental particle physics. The detector's spatial resolution, specifically the calorimeter's granularity, plays a crucial role in determining the quality of the particle reconstruction. It also sets the upper limit for the algorithm's theoretical capabilities. Super-resolution techniques can be explored as a...
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...
