  The Matrix Element Method (MEM) is a powerful method to extract information
from measured events at collider experiments. Compared to multivariate
techniques built on large sets of experimental data, the MEM does not rely on
an examples-based learning phase but directly exploits our knowledge of the
physics processes. This comes at a price, both in term of complexity and
computing time since the required multi-dimensional integral of a rapidly
varying function needs to be evaluated for every event and physics process
considered. This can be mitigated by optimizing the integration, as is done in
the MoMEMta package, but the computing time remains a concern, and often makes
the use of the MEM in full-scale analysis unpractical or impossible. We
investigate in this paper the use of a Deep Neural Network (DNN) built by
regression of the MEM integral as an ansatz for analysis, especially in the
search for new physics.
