Paper
Fast Spectrogram Event Extraction via Offline Self-Supervised Learning: From Fusion Diagnostics to Bioacoustics
Authors
Nathaniel Chen, Kouroche Bouchiat, Peter Steiner, Andrew Rothstein, David Smith, Max Austin, Mike van Zeeland, Azarakhsh Jalalvand, Egemen Kolemen
Abstract
Next-generation fusion facilities like ITER face a "data deluge," generating petabytes of multi-diagnostic signals daily that challenge manual analysis. We present a "signals-first" self-supervised framework for the automated extraction of coherent and transient modes from high-noise time-frequency data. We also develop a general-purpose method and tool for extracting coherent, quasi-coherent, and transient modes for fluctuation measurements in tokamaks by employing non-linear optimal techniques in multichannel signal processing with a fast neural network surrogate on fast magnetics, electron cyclotron emission, CO2 interferometers, and beam emission spectroscopy measurements from DIII-D. Results are tested on data from DIII-D, TJ-II, and non-fusion spectrograms. With an inference latency of 0.5 seconds, this framework enables real-time mode identification and large-scale automated database generation for advanced plasma control. Repository is in https://github.com/PlasmaControl/TokEye.
Metadata
Related papers
Fractal universe and quantum gravity made simple
Fabio Briscese, Gianluca Calcagni • 2026-03-25
POLY-SIM: Polyglot Speaker Identification with Missing Modality Grand Challenge 2026 Evaluation Plan
Marta Moscati, Muhammad Saad Saeed, Marina Zanoni, Mubashir Noman, Rohan Kuma... • 2026-03-25
LensWalk: Agentic Video Understanding by Planning How You See in Videos
Keliang Li, Yansong Li, Hongze Shen, Mengdi Liu, Hong Chang, Shiguang Shan • 2026-03-25
Orientation Reconstruction of Proteins using Coulomb Explosions
Tomas André, Alfredo Bellisario, Nicusor Timneanu, Carl Caleman • 2026-03-25
The role of spatial context and multitask learning in the detection of organic and conventional farming systems based on Sentinel-2 time series
Jan Hemmerling, Marcel Schwieder, Philippe Rufin, Leon-Friedrich Thomas, Mire... • 2026-03-25
Raw Data (Debug)
{
"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.20317v1</id>\n <title>Fast Spectrogram Event Extraction via Offline Self-Supervised Learning: From Fusion Diagnostics to Bioacoustics</title>\n <updated>2026-02-23T20:03:10Z</updated>\n <link href='https://arxiv.org/abs/2602.20317v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.20317v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Next-generation fusion facilities like ITER face a \"data deluge,\" generating petabytes of multi-diagnostic signals daily that challenge manual analysis. We present a \"signals-first\" self-supervised framework for the automated extraction of coherent and transient modes from high-noise time-frequency data. We also develop a general-purpose method and tool for extracting coherent, quasi-coherent, and transient modes for fluctuation measurements in tokamaks by employing non-linear optimal techniques in multichannel signal processing with a fast neural network surrogate on fast magnetics, electron cyclotron emission, CO2 interferometers, and beam emission spectroscopy measurements from DIII-D. Results are tested on data from DIII-D, TJ-II, and non-fusion spectrograms. With an inference latency of 0.5 seconds, this framework enables real-time mode identification and large-scale automated database generation for advanced plasma control. Repository is in https://github.com/PlasmaControl/TokEye.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='eess.SP'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.AI'/>\n <category scheme='http://arxiv.org/schemas/atom' term='physics.plasm-ph'/>\n <published>2026-02-23T20:03:10Z</published>\n <arxiv:primary_category term='eess.SP'/>\n <author>\n <name>Nathaniel Chen</name>\n </author>\n <author>\n <name>Kouroche Bouchiat</name>\n </author>\n <author>\n <name>Peter Steiner</name>\n </author>\n <author>\n <name>Andrew Rothstein</name>\n </author>\n <author>\n <name>David Smith</name>\n </author>\n <author>\n <name>Max Austin</name>\n </author>\n <author>\n <name>Mike van Zeeland</name>\n </author>\n <author>\n <name>Azarakhsh Jalalvand</name>\n </author>\n <author>\n <name>Egemen Kolemen</name>\n </author>\n </entry>"
}