High-energy physics (HEP) strives for understanding nature’s fundamental building blocks and the interactions between them. Large amounts of high-dimensional data, collected for example with the experiments at the Large Hadron Collider (LHC) at CERN in Switzerland are compared to theoretical predictions based on quantum field theory. Modern Machine Learning (ML) brought a lot of new ideas and improvements to HEP in recent years. On the experimental side, this includes data collection, particle reconstruction and selection as well as subsequent analysis. On the theoretical side, ML contributed a lot to better and faster simulation and parameter inference.

Overall, ML has not only led to significant improvements of existing algorithms, but also to new ideas for previously considered impossible-to-solve problems. Given the broad range of applicability of ML in HEP, almost all different types of objectives (like regression, classification, generation) on various different types of data (like tabular, point clouds, graphs) are being explored. In this SIG, we bring together experts from various domains of particle physics to use ML to improve our understanding of Nature.

Members

• Robert Schöfbeck (HEPHY)
• Wolfgang Waltenberger (HEPHY)
• Andreas Ipp (TU Vienna)
• Postdocs in CMS and HEP-ML group at HEPHY (currently 5 people)