  Extracting scientific results from high-energy collider data involves the
comparison of data collected from the experiments with synthetic data produced
from computationally-intensive simulations. Comparisons of experimental data
and predictions from simulations increasingly utilize machine learning (ML)
methods to try to overcome these computational challenges and enhance the data
analysis. There is increasing awareness about challenges surrounding
interpretability of ML models applied to data to explain these models and
validate scientific conclusions based upon them. The matrix element (ME) method
is a powerful technique for analysis of particle collider data that utilizes an
\textit{ab initio} calculation of the approximate probability density function
for a collision event to be due to a physics process of interest. The ME method
has several unique and desirable features, including (1) not requiring training
data since it is an \textit{ab initio} calculation of event probabilities, (2)
incorporating all available kinematic information of a hypothesized process,
including correlations, without the need for feature engineering and (3) a
clear physical interpretation in terms of transition probabilities within the
framework of quantum field theory. These proceedings briefly describe an
application of deep learning that dramatically speeds-up ME method calculations
and novel cyberinfrastructure developed to execute ME-based analyses on
heterogeneous computing platforms.
