BNL, Upton, New York, USA
Osprey DCS LLC, Ocean City, USA
Brookhaven National Laboratory (BNL), Electron-Ion Collider, Upton, New York, USA
Preparations are underway to build the Electron Ion Collider (EIC) once Relativistic Heavy Ion Collider (RHIC) beam operations are end in 2025, providing an enhanced probe into the building blocks of nuclear physics for decades into the future. With commissioning of the new facility in mind, Accelerator Controls will require modernization in order to keep up with recent improvements in the field as well as to match the fundamental requirements of the accelerators that will be constructed. We will describe the status of the Controls System architecture that has been developed and prototyped for EIC, as well as plans for future work. Major influences on the requirements will be discussed, including EIC Common Platform applications as well as our expectation that we’ll need to support a hybrid environment covering both the proprietary RHIC Accelerator Device Object (ADO) environment as well as EPICS.
BNL, Upton, New York, USA
Brookhaven National Laboratory’s Collider-Accelerator Department houses over 550 Front-End Computers (FECs) of varying specifications and resource requirements. These FECs provide operations-critical functions to the complex, and uptime is a concern among the most resource constrained units. Asynchronous data delivery is widely used by applications to provide live feedback of current conditions but contributes significantly towards resource exhaustion of FECs. To provide a balance of performance and efficiency, the Reflective system has been developed to support unrestricted use of asynchronous data delivery with even the most resource constrained FECs in the complex. The Reflective system provides components which work in unison to offload responsibilities typically handled by core controls infrastructure to hosts with the resources necessary to handle heavier workloads. The Reflective system aims to be a drop-in component of the controls system, requiring few modifications and remaining completely transparent to users and applications alike.
BNL, Upton, New York, USA
The successful use of machine learning (ML) in particle accelerators has greatly expanded in recent years; however, the realities of operations often mean very limited machine availability for ML development, impeding its progress in many cases. This paper presents a framework for exploiting physics-based simulations, coupled with real machine data structure, to facilitate the investigation and implementation of reinforcement learning (RL) algorithms, using the longitudinal bunch-merge process in the Booster and Alternating Gradient Synchrotron (AGS) at Brookhaven National Laboratory (BNL) as examples. Here, an initial fake wall current monitor (WCM) signal is fed through a noisy physics-based model simulating the behavior of bunches in the accelerator under given RF parameters and external perturbations between WCM samples; the resulting output becomes the input for the RL algorithm and subsequent pass through the simulated ring, whose RF parameters have been modified by the RL algorithm. This process continues until an optimal policy for the RF bunch merge gymnastics has been learned for injecting bunches with the required intensity and emittance into the Relativistic Heavy Ion Collider (RHIC), according to the physics model. Robustness of the RL algorithm can be evaluated by introducing other drifts and noisy scenarios before the algorithm is deployed and final optimization occurs in the field.