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Nicklaus, Dennis

Publications and source records attributed to Nicklaus, Dennis.

5 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 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

Beam Commissioning and Integrated Test of the PIP-II Injector Test Facility

The PIP-II Injector Test (PIP2IT) facility is a near-complete low energy portion of the Superconducting PIP-II linac driver. PIP2IT comprises the warm front end and the first two PIP-II superconducting cryomodules. PIP2IT is designed to accelerate a 2 mA H⁻ beam to an energy of 20 MeV. The facility serves as a testbed for a number of advanced technologies required to operate PIP-II and provides an opportunity to gain experience with commissioning of the superconducting linac, significantly reducing project technical risks. Some PIP2IT components are contributions from international partners, who also lend their expertise to the accelerator project. The project has been successfully commissioned with the beam in 2021, demonstrating the performance required for the LBNF/DUNE. In this paper, we describe the facility and its critical systems. We discuss our experience with the integrated testing and beam commissioning of PIP2IT, and present commissioning results. This important milestone ushers in a new era at Fermilab of proton beam delivery using superconducting radio-frequency accelerators.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

LCSL-II Cryomodule Testing at Fermilab

Cold powered testing of all LCLS-II production cryomodules at Fermilab is complete as of February 2021. A total of twenty-five tests on both 1.3 GHz and 3.9 GHz cryomodules were conducted over a nearly five year time span beginning in the summer of 2016. During the course of this campaign cutting-edge results for cavity Q₀ and gradient in continuous wave operation were achieved. A summary of all test results will be presented, with a comparison to established acceptance criteria, as well as overall test stand statistics and lessons learned.

43 PARTICLE ACCELERATORS↗