Related papers: BOOSTR: A Dataset for Accelerator Control Systems
Over the past decade, Fermilab has focused efforts on the intensity frontier physics and is committed to increase the average beam power delivered to the neutrino and muon programs substantially. Many upgrades to the existing injector…
The Fermi National Accelerator Laboratory (Fermilab) Linac accepts 750 keV H- ions from the front end and accelerates them to 400 MeV for injection into the Booster rapid cycling synchrotron. Day-to-day drifts in the beam longitudinal…
The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss of beam time. A novel time series classification approach is applied to…
The Fermilab booster has an intensity upgrade plan called the Proton Improvement plan (PIP). The flux throughput goal is 2E17 protons/hour, which is almost double the current operation at 1.1E17 protons/hour. The beam loss in the machine is…
We propose DrBoost, a dense retrieval ensemble inspired by boosting. DrBoost is trained in stages: each component model is learned sequentially and specialized by focusing only on retrieval mistakes made by the current ensemble. The final…
Time-series forecasting is crucial for numerous real-world applications including weather prediction and financial market modeling. While temporal-domain methods remain prevalent, frequency-domain approaches can effectively capture…
To date, the 120 GeV Fermilab Main Injector accelerator has accelerated a single batch of protons from the 8 GeV rapid-cycling Booster synchrotron for production of antiprotons for Run II. In the future, the Main Injector must accelerate 6…
The Fermilab Proton Source machines, constituted by Pre-Injector, conventional Linac and Booster synchrotron, at Fermi National Accelerator Laboratory (Fermilab) had have a long history of successful beam operations. Built in late '60s, the…
Fermilab Booster synchrotron requires an intensity upgrade from 4.5x1012 to 6.5x1012 protons per pulse as a part of Fermilab's Proton Improvement Plan-II (PIP-II). One of the factors which may limit the high-intensity performance is the…
Tuning database management systems (DBMSs) is challenging due to trillions of possible configurations and evolving workloads. Recent advances in tuning have led to breakthroughs in optimizing over the possible configurations. However, due…
Boosting is a learning scheme that combines weak prediction rules to produce a strong composite estimator, with the underlying intuition that one can obtain accurate prediction rules by combining "rough" ones. Although boosting is proved to…
This paper explores the voltage regulation challenges in boost converter systems, which are critical components in power electronics due to their ability to step up voltage levels efficiently. The proposed control algorithm ensures…
The potentially realizable beam power at the Fermilab long-baseline neutrino program has motivated a reinvigorated design and optimization effort for a rapid-cycling synchrotron (RCS) intensity upgrade of the Fermilab proton complex. We…
Reset controllers have demonstrated their effectiveness in enhancing performance in precision motion systems. To further exploiting the potential of reset controllers, this study introduces a parallel-partial reset control structure.…
Particle accelerators are complex facilities that produce large amounts of structured data and have clear optimization goals as well as precisely defined control requirements. As such they are naturally amenable to data-driven research…
The Fermilab Linac experiences longitudinal beam phase drift, leading to increased particle loss, conventionally corrected through labor-intensive manual RF adjustments. This project explores machine learning-based automation for drift…
In this paper, an enhancement to the well known Phasor Power Oscillation Damper is proposed, aiming to increase its performance. Fundamental to the functioning of this controller is the estimation of a phasor representing oscillatory…
Memristive neuromorphic systems are designed to emulate human perception and cognition, where the memristor states represent essential historical information to perform both low-level and high-level tasks. However, current systems face…
The growing need for synthetic time series, due to data augmentation or privacy regulations, has led to numerous generative models, frameworks, and evaluation measures alike. Objectively comparing these measures on a large scale remains an…
Time-series data classification is central to the analysis and control of autonomous systems, such as robots and self-driving cars. Temporal logic-based learning algorithms have been proposed recently as classifiers of such data. However,…