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Seiya, Kiyomi

Publications and source records attributed to Seiya, Kiyomi.

10 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Flattening the field during injection in the Fermilab booster using dipole corrector magnets

The FNAL Booster is a fast cycling 15 Hz resonant circuit synchrotron accelerating proton beam from 400 MeV to 8 GeV. The linac pulse injected into the Booster is ~32 μsec long and fills the ring by multi-turn charge-exchange injection. As part of the PIP-II project, the Booster injection energy and repetition rate will be increased to 800 MeV and 20 Hz respectively. Due to much reduced average current in the new superconducting PIP-II linac, the injection time will increase to 550 μs. A shorter machine cycle coupled to a longer injection time make flattening the injection porch B-field during injection important requirement for successful PIP-II operation. We aim to achieve: (1) flattening of the net bending during injection using dipole correctors, and (2) using a new system based on an Altera FPGA board, reduction of the cycle-to-cycle bending field variation caused by current jitter in the Gradient Magnet Power Supply (GMPS). While the flat injection scheme is essential to future PIP-II operations, it should also noticeably improve efficiency for present HEP operations.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Flattening the Field during Injection in the Fermilab Booster using Dipole Corrector Magnets

The FNAL Booster is a fast cycling 15 Hz resonant circuit synchrotron accelerating proton beam from 400 MeV to 8 GeV. The linac pulse injected into the Booster is ˜32 usec long and fills the ring by multi-turn charge-exchange injection. As part of the PIP-II project, the Booster injection energy and repetition rate will be increased to 800 MeV and 20 Hz respectively. Due to much reduced average current in the new superconducting PIP-II linac, the injection time will increase to 550us. A shorter machine cycle coupled to a longer injection time make flattening the injection porch B-field during injection important requirement for successful PIP-II operation. We aim to achieve: (1) flattening of the net bending during injection using dipole correctors, and (2) using a new system based on an Altera FPGA board, reduction of the cycle-to-cycle bending field variation caused by current jitter in the Gradient Magnet Power Supply (GMPS). While the flat injection scheme is essential to future PIP-II operations, it should also noticeably improve efficiency for present HEP operations.

43 PARTICLE ACCELERATORS↗
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

Artificial Intelligence for improved facilities operation in the FNAL LINAC

The energy consumption in accelerator structures during beam downtimes is a significant fraction of the overall energy budget. Accurate prediction of downtime duration could inform actions to reduce this energy consumption. The LCAPE project started in 2020 and develops artificial intelligence to improve operations in the FNAL control room by reducing the time to identify the cause of a beam outage, improving the reproducibility of labeling it, predicting their duration and forecasting their occurrence. We present our solution for incorporating information from ~2.5k monitored devices in near-real time to distinguish between dozens of different causes of down time. We discuss the performance of different techniques for modeling the state of health of the facility and we compare unsupervised clustering techniques to distinguish between different causes of down time.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Measurement of Beam Energy in the Fermilab's Linac taken at the Transfer Line

Linac is the first machine in the accelerator chain at Fermilab where H$^{-}$ ions are accelerated from 35 keV to 401.5\,MeV and then injected into a synchronton known as Booster where they are stripped of their electrons to become protons. One of the tools used during tuning of the Linac extraction energy is two beam pickups known as Griffin Detectors. Our goal is to control the output energy using machine learning techniques to increase the reliability and quality of the beam delivered from Linac. The first step is to understand the data from the diagnostics to develop reliable and accurate energy measurement, and control methods before implementing machine learning techniques. Two methods of energy measurement were studied, and their results are compared. The first method was the time of flight measurement using Beam Position Monitors that provide beam phase measurement. The second method used the relation between beam transverse positions and dispersion values to calculate momentum variation. The results of these two measurement methods are found to be consistent.

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

The L-CAPE Project at FNAL

The controls system at FNAL records data asynchronously from several thousand Linac devices at their respective cadences, ranging from 15Hz down to once per minute. In case of downtimes, current operations are mostly reactive, investigating the cause of an outage and labeling it after the fact. However, as one of the most upstream systems at the FNAL accelerator complex, the Linac’s foreknowledge of an impending downtime as well as its duration could prompt downstream systems to go into standby, potentially leading to energy savings. The goals of the Linac Condition Anomaly Prediction of Emergence (L-CAPE) project that started in late 2020 are (1) to apply data-analytic methods to improve the information that is available to operators in the control room, and (2) to use machine learning to automate the labeling of outage types as they occur and discover patterns in the data that could lead to the prediction of outages. We present an overview of the challenges in dealing with time-series data from 2000+ devices, our approach to developing an ML-based automated outage labeling system, and the status of augmenting operations by identifying the most likely devices predicting an outage.

43 PARTICLE ACCELERATORS↗