The matrix element method (MEM) is a powerful technique that can be used for the analysis of particle collider data utilizing an ab initio calculation of the approximate probability density function for a collision event to be due to a physics process of interest. The most serious difficulty with the ME method, which has limited its applicability to searches for beyond-the-SM physics and precision measurements at colliders, is that it is computationally expensive. Complex final states can take minutes per event or more to calculate the probability densities. ML methods can be used to speed up the numerical evaluation dramatically. In this work, we explore Deep Learning based solutions to approximate MEM calculations and compare their performance with respect to existing computational benchmarks.
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