Electron Energy Regression in the CMS High-Granularity Calorimeter
  Prototype
Roger Rusack Bhargav Joshi Alpana Alpana Seema Sharma Thomas Vadnais
We present a new publicly available dataset that contains simulated data of a
novel calorimeter to be installed at the CERN Large Hadron Collider. This
detector will have more than six-million channels with each channel capable of
position, ionisation and precision time measurement. Reconstructing these
events in an efficient way poses an immense challenge which is being addressed
with the latest machine learning techniques. As part of this development a
large prototype with 12,000 channels was built and a beam of high-energy
electrons incident on it. Using machine learning methods we have reconstructed
the energy of incident electrons from the energies of three-dimensional hits,
which is known to some precision. By releasing this data publicly we hope to
encourage experts in the application of machine learning to develop efficient
and accurate image reconstruction of these electrons.