9-13 July 2018
Sofia, Bulgaria
Europe/Sofia timezone

Partnering with industry for machine learning at HL-LHC

9 Jul 2018, 11:00
Hall 9 (National Palace of Culture)

Hall 9

National Palace of Culture

presentation Track 6 – Machine learning and physics analysis T6 - Machine learning and physics analysis


Maria Girone (CERN)


The High Luminosity LHC (HL-LHC) represents an unprecedented computing challenge. For the program to succeed the current estimates from the LHC experiments for the amount of processing and storage required are roughly 50 times more than are currently deployed. Although some of the increased capacity will be provided by technology improvements over time, the computing budget is expected to be flat and to close the gap huge gains in the efficiency for processing and analyzing events must be achieved. An area that has the potential for a significant breakthrough is Machine Learning. In recent years industry has invested heavily in both hardware and software to develop machine learning techniques to filter, process, analyze, and derive correlations from very large scale heterogeneous datasets. Through CERN openlab, with industry partners, and the R&D projects of the LHC experiments we are attempting to build on the industry investments to utilize these techniques for science. In this presentation we will discuss the activities of the CERN openlab industry partnerships in machine learning and how they can be extended to science applications. Industry has shown the ability to monitor the health of complex systems and predict failures and maintenance needs. We will show how these techniques could be applied to detector health. We will discuss how industry developments in anomaly detection might be applicable to monitoring data quality and identifying new signals. Industry has demonstrated the feasibility of automated resource scheduling and optimization. We will show how these techniques could be used for data placement and workflow execution. Industry has advanced systems for high speed and high accuracy image recognition. We will discuss explorations of how these techniques could be applied to physics object identification. In recent years there have been advancements in the use of adversarial networks to improve the accuracy and robustness of training. These techniques may be applicable generally to some physics machine learning applications, but are potentially particularly interesting for tuning fast event simulation. We will present examples of industry activity and how all of these are being explored in LHC applications. At the end we will look at data processing techniques and speed requirements for physics processing and how those compare to similar real time industry processing applications.

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