Φ talk #1 Machine Learning at the Large Hadron Collider

The application of machine learning algorithms at the Large Hadron Collider (LHC) represents a significant development in the field of high-energy physics.

This presentation, organised within the ESA Φ-lab Collaborative Innovation Network, explores the use of machine learning in particle physics at the Large Hadron Collider.

Content synopsis

The following essay will explore the role of machine learning in particle reconstruction and identification, including the use of transformers, tensor-attention networks and graph neural networks.

The present applications in the study of rare events, Higgs-related processes and searches that extend beyond the Standard Model are as follows:

The following discussion will address the data practices at the LHC, and their correlation with methodological challenges in Earth observation.

Relevance
The LHC is responsible for the production of data on a large scale, and the application of machine learning facilitates the extraction of information that cannot be managed through traditional methods.
The talk offers insight for researchers working with complex data in the fields of physics, space science and Earth observation.

The slides are available here: https://bit.ly/3Xhvh4G