Upgrading LAPD diagnostic pipelines for training generative ML models

Machine learning may transform the way science is conducted. We seek to update the Large Plasma Device (LAPD) data acquisition system to better capture machine state information (MSI) and global diagnostics that are important for ML-based analysis pipelines. Plasma processes in LAPD are typically studied by gathering high-spatial-resolution data using probes, combining measurements over many discharges at a 1 Hz shot rate. ML can instead be used to infer behavior over larger spatial regions from a few localized probe measurements and global or spatially-averaged diagnostics. An auxiliary system was appended to the current labview data acquisition routines to record LAPD MSI and auxiliary diagnostics including interferometers, visible light diodes, a diamagnetic loop, and a fast framing camera. An implicitly-generative, neural network-based energy based model (EBM) constructed in pytorch was trained on these auxiliary diagnostics, MSI, and probe measurements. Artificial discharges are sampled from the learned model via Langevin dynamics to predict time evolution of ion saturation current profiles. The EBM is able to reproduce general trends in profile evolution for a variety of magnetic field configurations. In addition, the EBM can be conditionally sampled to find the machine state required for a desired profile. Results from this model and comments on its accuracy will be presented, and the scientific prospects of this type of generative model will be discussed.