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Nathaniel Chen, Alvin Garcia, Ricardo Shousha, Jalal Butt, Azarakhsh Jalalvand, Egemen Kolemen
· 1 min read
ResearcharXiv cs.AI
Tokamak-SI: Interpreting Tokamak Discharges with Labeled Catalogs
arXiv:2610.32264v1 Announce Type: cross
Abstract: Finding which phenomena occur in a tokamak discharge, and when, is often a manual task. Previous efforts at labeling events have been task-specific, each with its own corpus of data. We introduce Tokamak-SI, which combines the labels and labeling methods of many earlier studies into a single scalable, time-aligned, and categorical dataset covering observations from confinement, detachment, alfven eigenmodes (AE), edge localized modes (ELM), sawtooth, tearing modes (TM), and resistive wall modes in DIII-D tokamak. Models based on this, which can extend the labels to new shots, match or beat earlier detectors on its labels, including for AE, ELM, and TM detection. Tokamak-SI is intended to enable task-specific benchmarking, physics-searchable archives, and direct identification of key events during an experiment.
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This story was published by arXiv cs.AI and written by Nathaniel Chen, Alvin Garcia, Ricardo Shousha, Jalal Butt, Azarakhsh Jalalvand, Egemen Kolemen. SyncAI.news shows a preview; the complete article is on the publisher's site.
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