Algorithms to perform the real-time processing in the trigger and the reconstruction of both real and simulated detector data are critical components of HEP’s computing challenge. University personnel, including graduate students and post-docs working on physics research grants, frequently develop and maintain innovative algorithms and implementations. These algorithms face a number of new challenges in the next decade due to new and upgraded accelerator facilities, detector upgrades and new detector technologies, increases in anticipated event rates, and emerging computing architectures. Tracking for the HL-LHC is an area in particular need of novel approaches, though the Institute will pursue other high-impact applications. The Institute will employ a wide range of strategies for the development of Innovative Algorithms.
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ACTSDevelopment of experiment-independent, inherently parallel track reconstruction.
- 17 Apr 2019 - A hybrid deep learning approach to vertexing, Henry Schreiner (Princeton University), 3rd IML Machine Learning Workshop
- 03 Apr 2019 - A hybrid deep learning approach to vertexing, Henry Schreiner (Princeton University), Connecting The Dots and Workshop on Intelligent Trackers 2019
- 20 Mar 2019 - Machine Learning for the Primary Vertex reconstruction, Henry Schreiner (Princeton University), 2019 Joint HSF/OSG/WLCG Workshop
- 11 Mar 2019 - A hybrid deep learning approach to vertexing, Henry Schreiner (Princeton University), 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research
- 13 Feb 2019 - Using ML on FPGAs to enhance reconstruction output, Dylan Rankin (Massachusetts Institute of Technology), IRIS-HEP Topical Meeting
- 06 Feb 2019 - IRIS-HEP Innovative Algorithms, Heather Gray (University of California, Berkeley), IRIS-HEP Steering Board Meeting
We collaborate with groups around the world on code, data, and more. See our project pages for more.