  Rapidly applying the effects of detector response to physics objects (e.g.
electrons, muons, showers of particles) is essential in high energy physics.
Currently available tools for the transformation from truth-level physics
objects to reconstructed detector-level physics objects involve manually
defining resolution functions. These resolution functions are typically derived
in bins of variables that are correlated with the resolution (e.g.
pseudorapidity and transverse momentum). This process is time consuming,
requires manual updates when detector conditions change, and can miss important
correlations. Machine learning offers a way to automate the process of building
these truth-to-reconstructed object transformations and can capture complex
correlation for any given set of input variables. Such machine learning
algorithms, with sufficient optimization, could have a wide range of
applications: improving phenomenological studies by using a better detector
representation, allowing for more efficient production of Geant4 simulation by
only simulating events within an interesting part of phase space, and studies
on future experimental sensitivity to new physics.
