{"id":3232,"date":"2025-12-19T09:07:37","date_gmt":"2025-12-19T08:07:37","guid":{"rendered":"https:\/\/www.thphys.uni-heidelberg.de\/aitp\/?page_id=3232"},"modified":"2026-01-17T21:26:12","modified_gmt":"2026-01-17T20:26:12","slug":"anja-butter","status":"publish","type":"page","link":"https:\/\/www.thphys.uni-heidelberg.de\/aitp\/anja-butter\/","title":{"rendered":"Anja Butter"},"content":{"rendered":"
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ML bridges for particle physics<\/h2>\n\n\n\n

Breaking things is one of the most efficient \u2014 and most entertaining \u2014 ways to learn something new. Luckily for us, machine learning is currently breaking down many long\u2011standing boundaries in particle physics, opening the door to new, ML\u2011based approaches for theory\u2011inspired and ML\u2011supported data analysis.<\/p>\n\n\n\n

Connecting Paris and Heidelberg, particle physics and computing, theory and experiment, my group and I work at the intersection of our favorite topics to learn more about particle physics and to develop increasingly powerful methods for analyzing LHC data. Over the past years, we have built a strong collaboration between LPNHE and ITP, leading to numerous joint research projects, workshops, and exchange programs.<\/p>\n\n\n\n

Neural networks for precision predictions<\/h5>\n\n\n\n

Precision predictions are essential for the success of the LHC physics program. To overcome current bottlenecks in high\u2011precision calculations, we develop neural\u2011network\u2011based methods that improve the efficiency and accuracy of various steps in the simulation chain. Our work includes:<\/p>\n\n\n\n