Machine Learning and LHC Event Generation
Anja Butter (ITP, University of Heidelberg)
Abstract: First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. In the coming LHC runs, these simulations will face unprecedented precision requirements to match the experimental accuracy. New ideas and tools based on neural networks have been developed at the interface of particle physics and machine learning. They can improve the speed and precision of forward simulations and handle the complexity of collision data. Such networks can be employed within established simulation tools or as part of a new framework. Since neural networks can be inverted, they open new avenues in LHC analyses.
data structures and algorithmsmachine learningmathematical physicsinformation theoryoptimization and controldata analysis, statistics and probability
Audience: researchers in the topic
Mathematics, Physics and Machine Learning (IST, Lisbon)
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Zoom link: videoconf-colibri.zoom.us/j/91599759679
Organizers: | Mário Figueiredo, Tiago Domingos, Francisco Melo, Jose Mourao*, Cláudia Nunes, Yasser Omar, Pedro Alexandre Santos, João Seixas, Cláudia Soares, João Xavier |
*contact for this listing |