Event announcement as pdf-Download (https://tu-dresden.de/mn/phys ik/ressourcen/dateien/physikalisches-kolloquium/2026-07-07-Phys_Kolloq-Hei nrich-SoSe2026.pdf).
Abstract: Particle Physics h as long been tightly coupled to advanced computational techniques due to t he complexity of the theory and experiments that originate which are requi red to bridge the indirectness of the field: to study the smallest constit uents of matter\, we must build some of the largest experiments ever const ructed. Within the last decade the rapid advancements in machine learning and artificial intelligence have not only pushed the capabilities of exper imental data analysis techniques far beyond what was previously deemed pos sible but increasingly evidence is mounting that the fundamental limit to sensitivity may still be far away. The key to these developments is the em ergence of “Scaling Laws”\, which have been the driving force in the r ise language models and have recently been observed in particle physics da ta as well. In this talk I will review recent progress across both of thes e frontiers and discuss possible future directions.
Short b io: Prof. Heinrich holds the \"Professorship for Data Science in Physics\" at the School of Natural Sciences at the Technical University o f Munich. After his PhD in 2019 from the University of New York\, he was a CERN Fellow and Staff Scientist\, before joining TUM. His research backgr ound is particle physics\, where\, as a member of the ATLAS experiment at the Large Hadron Collider (LHC) at the CERN. In this context he is develop ing novel data analysis methods that combine physical knowledge and artifi cial intelligence\, for which he has also been awarded an ERC Starting Gra nt.