  Design of new experiments, as well as upgrade of ongoing ones, is a
continuous process in the experimental high energy physics. Since the best
solution is a trade-off between different kinds of limitations, a quick turn
over is necessary to evaluate physics performance for different techniques in
different configurations. Two typical problems which slow down evaluation of
physics performance for particular approaches to calorimeter detector
technologies and configurations are:
  - Emulating particular detector properties including raw detector response
together with a signal processing chain to adequately simulate a calorimeter
response for different signal and background conditions. This includes
combining detector properties obtained from the general Geant simulation with
properties obtained from different kinds of bench and beam tests of detector
and electronics prototypes.
  - Building an adequate reconstruction algorithm for physics reconstruction of
the detector response which is reasonably tuned to extract the most of the
performance provided by the given detector configuration.
  Being approached from the first principles, both problems require significant
development efforts. Fortunately, both problems may be addressed by using
modern machine learning approaches, that allow a combination of available
details of the detector techniques into corresponding higher level physics
performance in a semi-automated way. In this paper, we discuss the use of
advanced machine learning techniques to speed up and improve the precision of
the detector development and optimisation cycle, with an emphasis on the
experience and practical results obtained by applying this approach to
epitomising the electromagnetic calorimeter design as a part of the upgrade
project for the LHCb detector at LHC.
