Can I read Deep Learning for the Matrix Element Method on EtoBox?

Deep Learning for the Matrix Element Method by Feickert, Matthew; Katare, Mihir; Neubauer, Mark; Roy, Avik is a scholarly article available to read on EtoBox.

What is Deep Learning for the Matrix Element Method about?

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

Author

Feickert, Matthew; Katare, Mihir; Neubauer, Mark; Roy, Avik

Published

2022

Language

EN