3 research outputs found
Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster
We describe a method for precisely regulating the gradient magnet power
supply at the Fermilab Booster accelerator complex using a neural network
trained via reinforcement learning. We demonstrate preliminary results by
training a surrogate machine-learning model on real accelerator data to emulate
the Booster environment, and using this surrogate model in turn to train the
neural network for its regulation task. We additionally show how the neural
networks to be deployed for control purposes may be compiled to execute on
field-programmable gate arrays. This capability is important for operational
stability in complicated environments such as an accelerator facility.Comment: 16 pages, 10 figures. Submitted to Physical Review Accelerators and
Beams. For associated dataset and data sheet see
http://doi.org/10.5281/zenodo.408898
BOOSTR: A Dataset for Accelerator Control Systems
The Booster Operation Optimization Sequential Time-series for Regression (BOOSTR) dataset was created to provide a cycle-by-cycle time series of readings and settings from instruments and controllable devices of the Booster, Fermilab’s Rapid-Cycling Synchrotron (RCS) operating at 15 Hz. BOOSTR provides a time series from 55 device readings and settings that pertain most directly to the high-precision regulation of the Booster’s gradient magnet power supply (GMPS). To our knowledge, this is one of the first well-documented datasets of accelerator device parameters made publicly available. We are releasing it in the hopes that it can be used to demonstrate aspects of artificial intelligence for advanced control systems, such as reinforcement learning and autonomous anomaly detection
DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification
With increased adoption of supervised deep learning methods for processing
and analysis of cosmological survey data, the assessment of data perturbation
effects (that can naturally occur in the data processing and analysis
pipelines) and the development of methods that increase model robustness are
increasingly important. In the context of morphological classification of
galaxies, we study the effects of perturbations in imaging data. In particular,
we examine the consequences of using neural networks when training on baseline
data and testing on perturbed data. We consider perturbations associated with
two primary sources: 1) increased observational noise as represented by higher
levels of Poisson noise and 2) data processing noise incurred by steps such as
image compression or telescope errors as represented by one-pixel adversarial
attacks. We also test the efficacy of domain adaptation techniques in
mitigating the perturbation-driven errors. We use classification accuracy,
latent space visualizations, and latent space distance to assess model
robustness. Without domain adaptation, we find that processing pixel-level
errors easily flip the classification into an incorrect class and that higher
observational noise makes the model trained on low-noise data unable to
classify galaxy morphologies. On the other hand, we show that training with
domain adaptation improves model robustness and mitigates the effects of these
perturbations, improving the classification accuracy by 23% on data with higher
observational noise. Domain adaptation also increases by a factor of ~2.3 the
latent space distance between the baseline and the incorrectly classified
one-pixel perturbed image, making the model more robust to inadvertent
perturbations.Comment: 20 pages, 6 figures, 5 tables; accepted in MLS