Conveners
Data Science: Parallel 5
- Alexander Ostrovidov (Flordia State University)
Streaming Readout has been adopted as the paradigm of data acquisition (DAQ) at many major experiments at LHC, RHIC, and the future EIC. Distinct from the traditional triggered readout, streaming DAQs rely on modern digital data processing for large factors of data reduction, which opens unique opportunities for the application of AI/ML that is high throughput, low latency, energy efficient,...
I review recent developments in the application of machine learning techniques to problems in nuclear theory, with a particular emphasis on generative models for lattice quantum field theory.
Exclusive reactions measured at the intensity frontier open new opportunities to study QCD. Processes in a multi-dimensional phase space require adequate tools to take advantage of correlations between variables that embed the underlying strong-force interaction. To extend our capability of interpreting multi-dimensional data and fully reconstruct correlations between final state particles, we...
Jefferson Laboratory (JLab) is home to the Continuous Electron Beam Accelerator Facility (CEBAF) and four experimental physics halls. JLab’s data science portfolio includes projects to advance research in nuclear physics, accelerator facilities, and engineering. With a specific focus on expanding capabilities in machine learning (ML)-based uncertainty quantification, design and control, and...
Artificial Intelligence (AI) for design is a relatively new but active area of research but when it comes to detector design, surprisingly this is an area of applications at its infancy.
Nonetheless the Electron Ion Collider (EIC), the future ultimate machine to study the strong force, utilized AI starting from the design phase. EIC is a large-scale experiment with an integrated detector...
