Deep Learning for the Matrix Element Method

Matthew Feickert, Mihir Katare, Mark Neubauer, Avik Roy

Research output: Contribution to journalConference articlepeer-review


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 which can be used for analysis of particle collider data that utilizes an 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 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.

Original languageEnglish (US)
Article number246
JournalProceedings of Science
StatePublished - 2022
Event41st International Conference on High Energy Physics, ICHEP 2022 - Bologna, Italy
Duration: Jul 6 2022Jul 13 2022

ASJC Scopus subject areas

  • General


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