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Extending the Physics Reach of LHCb in Run 3 Using Machine Learning in the Real-Time Data Ingestion and Reduction System

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posted on 04.02.2020, 23:29 by Michael Sokoloff, Rui Fang, Henry Fredrick Schreiner, Marian Stahl, Weisser, Constantin, Mike Williams
This poster describes a hybrid machine learning algorithm for finding primary vertices in proton-proton collisions produced in the LHCb detector at CERN in Run 3. A proof-of-principle has been demonstrated using a kernel density estimator that transforms sparse 3D data into a rich 1D data set that is processed by a convolutional neural network. The algorithm learns target histograms that serve as proxies for the primary vertex positions. Basic concepts are illustrated. Results to date are summarized. Plans for future work are presented.


NSF OAC-1740102

NSF OAC-1739772

NSF OAC-1836650