Feb. 15, 2024, 5:43 a.m. | Uraz Odyurt, Stephen Nicholas Swatman, Ana-Lucia Varbanescu, Sascha Caron

cs.LG updates on arXiv.org arxiv.org

arXiv:2309.03780v2 Announce Type: replace-cross
Abstract: Subatomic particle track reconstruction (tracking) is a vital task in High-Energy Physics experiments. Tracking is exceptionally computationally challenging and fielded solutions, relying on traditional algorithms, do not scale linearly. Machine Learning (ML) assisted solutions are a promising answer. We argue that a complexity-reduced problem description and the data representing it, will facilitate the solution exploration workflow. We provide the REDuced VIrtual Detector (REDVID) as a complexity-reduced detector model and particle collision event simulator combo. REDVID …

abstract algorithms arxiv complexity cs.lg data data-driven energy hep-ex machine machine learning physics research scale simulations solutions tracking type vital

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