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This work demonstrates the first fine-tuning of an industrial foundation model for particle physics data acquisition systems, improving feature extraction and enabling real-time data handling. It combines large-scale AI models with on-detector deployments through distillation learning and hardware...
This work demonstrates the first fine-tuning of an industrial foundation model for particle physics data acquisition systems, improving feature extraction and enabling real-time data handling. It combines large-scale AI models with on-detector deployments through distillation learning and hardware...
AI-assisted summary based on the listed source.
Data acquisition (DAQ) systems at future particle physics experiments stand to benefit from the extremes of AI/ML development: large-scale foundation models can enhance the performance of feature extraction algorithms, and small-scale on-detector deployments can enable real-time intelligent data handling. This...
Integrating foundation models into particle physics detectors can significantly boost data processing capabilities, enabling more efficient and intelligent data acquisition. This approach bridges advanced AI developments with real-time experimental needs.
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