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Matthew Feickert, Steps Towards Differentiable and Scalable Physics Analyses at the LHC"


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Meeting ID: 621 853 118

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With the many computational challenges that face Run 3 of the LHC, there is an increased demand to exploit the recorded data as much as possible. There is a complimentary desire to empower analysts with toolkits that allow for expressive manipulation of the data and rapid iteration from analysis design to final insights. In this talk I will motivate the importance of differentiable programming in analysis toolkits and illustrate the opportunities for physics analyses at the LHC to be optimized from end-to-end using efficient gradient-based optimization algorithms. I will additionally explore ongoing dedicated efforts across the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP) to integrate differentiable programming techniques with highly scalable workflows to create performant end-user analysis tools, with applications beyond the scope of experimental high energy physics.


Meeting ID: 621 853 118

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