Bulletin of the American Physical Society
APS April Meeting 2022
Volume 67, Number 6
Saturday–Tuesday, April 9–12, 2022; New York
Session T02: Machine Learning in Particle PhysicsInvited Live Streamed
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Sponsoring Units: DPF Chair: Julia Gonski, Columbia University Room: Broadway South |
Monday, April 11, 2022 3:45PM - 4:21PM |
T02.00001: AI/ML Methods for Simulations across HEP Frontiers Invited Speaker: Katrin Heitmann Simulations play a key role in high-energy physics research across the frontiers. They provide an important tool to explore new physics ideas and predict their signatures that can then be measured. Simulations are often essential for the proper analysis of experimental and observational measurements and in deriving error estimates. The accuracy requirements for large-scale simulations pose a major challenge, as many simulation parameters have to be tuned and the computational cost of the simulations can be very large. New AI/ML approaches can help with these challenges in many ways, for example by optimizing modeling approaches or speeding up simulations. However, at the same time, it is important to ensure that these new approaches do not introduce hidden biases into the predictions. In this talk I will provide an overview of how AI/ML methods have played important roles to support simulation approaches in the different frontiers. I will also discuss what challenges still lie ahead of us to bring AI/ML methods to full fruition in the area of high-energy physics simulations. |
Monday, April 11, 2022 4:21PM - 4:57PM |
T02.00002: Machine Learning for Data Analysis Invited Speaker: Benjamin Nachman In this talk, I will cover machine learning for data analysis in high energy physics. In particular, I will describe how modern machine learning methods can be used to significantly enhance precision measurements and searches for physics beyond the Standard Model. I will cover topics at varying levels of readiness (e.g. phenomenological proposals to performance studies to experimental results), providing examples across the high energy physics frontiers. |
Monday, April 11, 2022 4:57PM - 5:33PM |
T02.00003: Machine Learning and Data Reconstruction in Experimental Physics Invited Speaker: Kazuhiro Terao During the last decade, research of machine learning (ML) and artificial intelligence (AI) has gone through an explosive evolution and made impacts across many domains of science and human lives. ML/AI models in computer vision can perform high quality analysis of image data from physics experiments, and models developed for natural language processing are applied to analyze sequential data. Graph models can be used to represent more general relations and thus powerful tools for analyzing data from multi-modal detector data in particle physics experiments. These models are promising not only for its performance on data reconstruction tasks, but also for automated optimization procedures that reduce much of human interventions needed for traditional, hand-engineered algorithms. Furthermore, the software eco-systems they are built on top of can exploit modern computing infrastructures such as High Performance Computing clusters and supported by interdisciplinary research communities across both academic and industrial research disciplines. In this talk, I introduce examples and challenges of ML/AI models that are used for data reconstruction tasks in experimental High Energy Physics with a focus on imaging detectors. |
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