PoS - Proceedings of Science
Volume 397 - The Ninth Annual Conference on Large Hadron Collider Physics (LHCP2021) - Poster Session
Convolutional Neural Networks for event classification
A.R. Jimenez*, J.E. García Navarro and M.M. Llácer
Full text: pdf
Pre-published on: October 20, 2021
Published on: November 17, 2021
Cutting-edge Artificial Intelligence is being implemented in a wide range of tasks in High Energy Physics (HEP) in order to facilitate the analysis of large datasets. However, visual recognition has not been explored as much in HEP for event classification. This study shows how Convolutional Neural Networks could be applied for such an important task, for which a novel method to represent the event information in images is explored.

This technique is applied for a classification problem corresponding to a search for Dark Matter in proton-proton collisions. The results obtained with this technique are also compared with the performance of a Boosted Decision Tree.
DOI: https://doi.org/10.22323/1.397.0264
How to cite

Metadata are provided both in "article" format (very similar to INSPIRE) as this helps creating very compact bibliographies which can be beneficial to authors and readers, and in "proceeding" format which is more detailed and complete.

Open Access
Creative Commons LicenseCopyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.