AI Projects at Jefferson Lab

Use AI models to optimize glass/crystal material selection in "shared rapidity regions" for best performance of EM and joint EM/hadronic calorimetry.

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Train GAN to produce π+π- events with same feature set as CLAS6 experimental data set.

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Use AI models to monitor plots online during data taking and alert shift takers if an issue.

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Update detector controls during data taking to reduce or remove need for offline calibrations.

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 Automated data quality monitoring using recent Learn-by-Calibration neural network models. Automated calibrations.

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Use AI models to optimize glass/crystal material selection in "shared rapidity regions" for best performance of EM and joint EM/hadronic calorimetry.

Train GAN to produce π+π- events with same feature set as CLAS6 experimental data set.

Use AI models to monitor plots online during data taking and alert shift takers if an issue.

Update detector controls during data taking to reduce or remove need for offline calibrations.

 Automated data quality monitoring using recent Learn-by-Calibration neural network models. Automated calibrations.