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Nagaslaev, Vladimir

Publications and source records attributed to Nagaslaev, Vladimir.

7 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Disentangling Beam Losses in The Fermilab Main Injector Enclosure Using Real-Time Edge AI

The Fermilab Main Injector enclosure houses two accelerators, the Main Injector and Recycler Ring. During normal operation, high intensity proton beams exist simultaneously in both. The two accelerators share the same beam loss monitors (BLM) and monitoring system. Deciphering the origin of any of the 260 BLM readings is often difficult. The (Accelerator) Real-time Edge AI for Distributed Systems project, or READS, has developed an AI/ML model, and implemented it on fast FPGA hardware, that disentangles mixed beam losses and attributes probabilities to each BLM as to which machine(s) the loss originated from in real-time. The model inferences are then streamed to the Fermilab accelerator controls network (ACNET) where they are available for operators and experts alike to aid in tuning the machines.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Feasibility of using crystal channeling for the beam loss mitigation in Slow Extraction at 8GeV

The mitigation of the beam losses in slow extraction is becoming more and more demanding in accelerator applications for HEP as the beam power is gradually increasing. The successful demonstration of using the proton beam channeling at 450GeV to deflect the beam away from the extraction septa opens the new levels of improving the slow extraction efficiency. It is yet to be demonstrated that this method is still effective at low and medium proton beam energies. Here we present the promising results of the recent computer simulation studies of the septum shadowing at 8GeV for the Mu2e project slow extraction at Fermilab. Depending on the beam parameters the beam loss reduction is shown to be achievable in the range of 1/3 to factor of 3.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Semantic Regression for Disentangling Beam Losses in the Fermilab Main Injector and Recycler

Fermilab’s Main Injector enclosure houses two accelerators: the Main Injector (MI) and the Recycler (RR). In periods of joint operation, when both machines contain high intensity beam, radiative beam losses from MI and RR overlap on the enclosure’s beam loss monitoring (BLM) system, making it difficult to attribute those losses to a single machine. Incorrect diagnoses result in unnecessary downtime that incurs both financial and experimental cost. In this work, we introduce a novel neural approach for automatically disentangling each machine’s contributions to those measured losses. Using a continuous adaptation of the popular UNet architecture in conjunction with a novel data augmentation scheme, our model accurately infers the machine of origin on a per-BLM basis in periods of joint and independent operation. Crucially, by extracting beam loss information at varying receptive fields, the method is capable of learning both local and global machine signatures and producing high quality inferences using only raw BLM loss measurements.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Simulation of Bunch Formation for the Mu2e Experiment

The Fermilab Recycler is an 8 GeV storage ring composed of permanent magnets that was crucial to the success of the Fermilab Tevatron Collider program. It is currently being used to slip-stack protons for the high energy neutrino program and to re-bunch protons for use in the Muon g-2 and Mu2e experiments. For the latter applications, the Recycler re-bunches each 1.6 µs "batch" from the Fermilab Booster into four 2.5 MHz bunches. For the Mu2e experiment, it is crucial that beam more than 125 ns from the nominal bunch center be suppressed by at least a factor of 1E-5. While bunch formation is currently in operation for the g-2 experiment, this out of time requirement has not been met, and the reason is not understood. This work presents a simulation of bunch formation in the Recycler, in an effort to understand the reason for this excessive out of time beam and to search for a way to reduce it.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Real-Time Edge AI for Distributed Systems (READS): Progress on Beam Loss De-Blending for the Fermilab Main Injector and Recycler

The Fermilab Main Injector enclosure houses two accelerators, the Main Injector and Recycler. During normal operation, high intensity proton beams exist simultaneously in both. The two accelerators share the same beam loss monitors (BLM) and monitoring system. Beam losses in the Main Injector enclosure are monitored for tuning the accelerators and machine protection. Losses are currently attributed to a specific machine based on timing. However, this method alone is insufficient and often inaccurate, resulting in more difficult machine tuning and unnecessary machine downtime. Machine experts can often distinguish the correct source of beam loss. This suggests a machine learning (ML) model may be producible to help de-blend losses between machines. Work is underway as part of the Fermilab Real-time Edge AI for Distributed Systems Project (READS) to develop a ML empowered system that collects streamed BLM data and additional machine readings to infer in real-time, which machine generated beam loss.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Optimizing Mu2e Spill Regulation System Algorithms

A slow extraction system is being developed for the Fermilab’s Delivery Ring to deliver protons to the Mu2e experiment. During the extraction, the beam on target experiences small intensity variations owing to many factors. Various adaptive learning algorithms will be employed for beam regulation to achieve the required spill quality. We discuss here preliminary results of the slow and fast regulation algorithms validation through the computer simulations before their implementation in the FPGA. Particle tracking with sextupole resonance was used to determine the fine shape of the spill profile. Fast semi-analytical simulation schemes and Machine Learning models were used to optimize the fast regulation loop.

43 PARTICLE ACCELERATORS↗