Results for “Particle beam handling”

DOE OSTI2024

Experimental Safe Extremum Seeking for Accelerators

Here, we demonstrate the recent designs of safe extremum seeking (Safe ES) on the 1-km-long charged particle accelerator at the Los Alamos Neutron Science Center (LANSCE). Safe ES is a modification of extremum seeking (ES) which, in addition to minimizing an analytically unknown cost, also employs a safety filter based on an analytically unknown control barrier function (CBF) safety metric. Tuning is necessitated by accelerators being large complex systems, with many drifting parameters due to thermal effects and degradation. At the same time, safe operation (the maintenance of state constraints) is crucial, as damage brings astronomical costs, both financially and in operation downtime. Our measured (but analytically unknown) safety metric is the beam current. We perform multivariable Safe ES on three accelerator applications, in which we adapt 4, 6, and 3 magnet strength parameters, respectively. Two of the three applications are for validated simulation models of beamlines at LANSCE: the first for the proton radiography (pRad) beamline of 800-MeV protons for spot size tuning; the second on a high-performance code, HPSim, for tuning the low-energy beam transport (LEBT) region that contains a beam of 750-keV protons. The third is an experimental tuning of the steering magnets in the LEBT at LANSCE.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Radioisotope production at the Spallation Neutron Source: Design concept of experimental target station

Completion of the Proton Power Upgrade Project for the Spallation Neutron Source (SNS) accelerator at Oak Ridge National Laboratory opens an opportunity to utilize reserve beam power of more than 100 kW for applications beyond neutron production. One of these applications is the production of critical radionuclides. To demonstrate the feasibility of using the reserve beam power to produce radioisotope at SNS, a design concept of a small-scale experimental target station in the Linac Dump area has been developed. This experimental facility will provide isotope yield benchmarking data using protons in the GeV range. It will also enable additional research and development in isotope handling and radiochemical separation. The target station consists of a target module enclosed in a vessel and concrete shielding. Particle transport calculations and thermo-mechanical simulations are used to determine beam parameters, decay time, isotope yield, shielding dimensions, and target design parameters. Calculations verified that the irradiated capsule can be handled manually using hands-off tools and transported to a hot cell in a shielded container for post-irradiation characterizations.

Lee, Yong Joong [ORNL] (ORCID:0000000298381723)↗

DOE OSTI2024

PICOSEC-Micromegas Detector, an innovative solution for Lepton Time Tagging

The PICOSEC-Micromegas (PICOSEC-MM) detector is a novel gaseous detector designed for precise timing resolution in experimental measurements. It eliminates time jitter from charged particles in ionization gaps by using extreme UV Cherenkov light emitted in a crystal, detected by a Micromegas photodetector with an appropriate photocathode. The first single-channel prototype tested in 150 GeV/c muon beams achieved a timing resolution below 25 ps, a significant improvement compared to standard Micropattern Gaseous Detectors (MPGDs). This work explores the specifications for applying these detectors in monitored neutrino beams for the ENUBET Project. Key aspects include exploring resistive technologies, resilient photocathodes, and scalable electronics. New 7-pad resistive detectors are designed to handle the particle flux. In this paper, two potential scenarios are briefly considered: tagging electromagnetic showers with a timing resolution below 30ps in an electromagnetic calorimeter as well as individual particles (mainly muons) with about 20ps respectively.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

Continual Learning for Particle Accelerators

Particle accelerators operate under dynamically changing conditions, which often lead to data distribution drifts. These drifts pose significant challenges for Machine Learning (ML) models, which typically fail to maintain performance when faced with such non-stationary data. In particle accelerators, the primary sources of these data drifts include changes in accelerator settings and non-measured parameters such as machine degradation and environmental factors. Previous research has proposed conditional models to handle multiple beam configurations effectively; however, it is challenging to train the ML models on all possible configuration settings. Additionally, conditional models alone can not address performance degradation caused by drifts due to non-measured factors. These limitations contribute to a significant gap between ML development and its deployment in real-world operational settings. To bridge this gap, in this paper, we identify some of the key areas within particle accelerators where continual learning can help mitigate drift-induced performance degradation. In addition, we present a practical use case where a conditional Auto-Encoder model coupled with memory-based continual learning has been employed to demonstrate stable performance even when underlying data drifts.

Schram, Malachi [Thomas Jefferson National Acceler↗

DOE OSTI2026

Continual Learning for Particle Accelerators

Particle accelerators operate under dynamically changing conditions, which often lead to data distribution drifts. These drifts pose significant challenges for Machine Learning (ML) models, which typically fail to maintain performance when faced with such non-stationary data. In particle accelerators, the primary sources of these data drifts include changes in accelerator settings and non-measured parameters such as machine degradation and environmental factors. Previous research has proposed conditional models to handle multiple beam configurations effectively; however, it is challenging to train the ML models on all possible configuration settings. Additionally, conditional models alone can not address performance degradation caused by drifts due to non-measured factors. These limitations contribute to a significant gap between ML development and its deployment in real-world operational settings. To bridge this gap, in this paper, we identify some of the key areas within particle accelerators where continual learning can help mitigate drift-induced performance degradation. In addition, we present a practical use case where a conditional Auto-Encoder model coupled with memory-based continual learning has been employed to demonstrate stable performance even when underlying data drifts

Rajput, Kishansingh [Thomas Jefferson National Acc↗

NASA NTRS2020

Dynamic Modeling, Simulation, and Analysis of Orbiting Sample Capture for Potential Mars Sample Return

The current notional architecture for the Mars Sample Return (MSR) campaign would require autonomous on-orbit rendezvous and capture of a sample container after it has been delivered to Mars orbit by a preceding MSR mission. As part of the complete orbital payload known as the Capture, Contain, and Return System (CCRS), a concept has been developed for autonomously sensing and capturing this Orbiting Sample (OS) container. The concept, comprised of a capture mechanism and multiple optical break-beam arrays, would detect and enclose the OS into CCRS, and engage a dust-tight seal to prevent the escape of the OS or other unsterilized particles. A challenging goal for the system is to close the lid prior to the OS coming into physical contact after entering CCRS in order to reduce the risk of unsterilized Mars particles on the surface of the OS from contaminating the outside of the spacecraft. The mechanical and sensor performance requirements were driven by the broader architecture of CCRS, OS rendezvous parameters, and Planetary Protection requirements regarding the interaction and handling of the OS. The mechanism and break-beam array concepts were designed accordingly, and kinematic simulations of the mechanism’s behavior, combined with a Monte-Carlo simulation of OS rendezvous/collision behaviors and corresponding breakbeam sensor responses verify compliance.

Ishigo, Alyssa↗

DOE OSTI2024

Linac_Gen: Integrating Machine Learning and Particle-in-Cell Methods for Enhanced Beam Dynamics at Fermilab

Here, we introduce Linac_Gen, a tool developed at Fermilab, which combines machine learning algorithms with Particle-in-Cell methods to advance beam dynamics in linacs. Linac_Gen employs techniques such as Random Forest, Genetic Algorithms, Support Vector Machines, and Neural Networks, achieving a tenfold increase in speed for phase-space matching in Linacs over traditional methods, through the use of genetic algorithms. Crucially, Linac_Gen's adept handling of 3D field maps elevates the precision and realism in simulating beam instabilities and resonances, marking a key advancement in the field. Benchmarked against established codes, Linac_Gen demonstrates not only improved efficiency and precision in beam dynamics studies but also in the design and optimization of Linac systems, as evidenced in its application to Fermilab's PIP-II Linac project. This work represents a notable advancement in accelerator physics, marrying ML with PIC methods to set new standards for efficiency and accuracy in accelerator design and research. Linac_Gen exemplifies a novel approach in accelerator technology, offering substantial improvements in both theoretical and practical aspects of beam dynamics.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Linac_Gen: integrating machine learning and particle-in-cell methods for enhanced beam dynamics at Fermilab

Here, we introduce Linac_Gen, a tool developed at Fermilab, which combines machine learning algorithms with Particle-in-Cell methods to advance beam dynamics in linacs. Linac_Gen employs techniques such as Random Forest, Genetic Algorithms, Support Vector Machines, and Neural Networks, achieving a tenfold increase in speed for phase-space matching in linacs over traditional methods through the use of genetic algorithms. Crucially, Linac_Gen's adept handling of 3D field maps elevates the precision and realism in simulating beam instabilities and resonances, marking a key advancement in the field. Benchmarked against established codes, Linac_Gen demonstrates not only improved efficiency and precision in beam dynamics studies but also in the design and optimization of linac systems, as evidenced in its application to Fermilab's PIP-II linac project. This work represents a notable advancement in accelerator physics, marrying ML with PIC methods to set new standards for efficiency and accuracy in accelerator design and research. Linac_Gen exemplifies a novel approach in accelerator technology, offering substantial improvements in both theoretical and practical aspects of beam dynamics.

43 PARTICLE ACCELERATORS↗

NASA NTRS2018

Curating NASA's Past, Present, and Future Extraterrestrial Sample Collections

As codified in NASA Policy Directive 7100.10F, the Astromaterials Acquisition and Curation Office at NASA Johnson Space Center (hereafter JSC Curation) is charged with curation of all extraterrestrial material under NASA control, including future NASA missions. JSC Curation curates all or part of nine astromaterial collections in seven clean room suites: (1) Apollo Samples (1969; ISO 6-7), (2) Luna Samples (from USSR; 1972; ISO 7), (3) Antarctic Meteorites (1976; ISO 7), (4) Cosmic Dust (1981; ISO 5), (5) Microparticle Impact Collection (formerly called Space Exposed Hardware; 1985; ISO 5), (6) Genesis Solar Wind Atoms (2004; ISO 4); (7) Stardust Comet Particles (2006; ISO 5), (8) Stardust Interstellar Particles (2006; ISO 5), (9) Hayabusa Asteroid Particles (from JAXA; 2010; ISO 5). In addition to the labs that house the samples, we have installed and maintained a wide variety of facilities and infrastructure required to support the clean-rooms: more than 10 different HEPA-filtered air-handling systems, ultrapure dry gaseous nitrogen systems, an ultrapure water system (UPW) and cleaning facilities to provide clean tools and equipment for the labs. We also have sample preparation facilities for making thin sections, microtome sections, and even focused ion-beam (FIB) sections to meet the research requirements of scientists. To ensure that we are keeping the samples as pristine as possible, we routinely monitor the cleanliness of our clean rooms and infrastructure systems. This monitoring includes: daily monitoring of the quality of our UPW, weekly airborne particle counts in the labs, monthly monitoring of the stable isotope composition of the gaseous N2 system, and annual measurements of inorganic or organic contamination in processing cabinets. We track within our databases the current and ever-changing characteristics of more than 250,000 individual samples across our various collections (including the 19,141 samples on loan to 433 Principal Investigators in 24 countries). The next sample return missions that NASA will participate in are Hayabusa2 and OSIRIS-REx (Origins Spectral Interpretation Resource Identification Security - Regolith Explorer). The designs for a new state-of-the-art suite of clean rooms to house these samples at JSC have been finalized. This includes separate ISO class 5 clean rooms to house each collection, a common ISO class 7 area for general use, an ISO class 7 microtome laboratory, and a separate thin section lab. Additionally, a new cleaning facility is being designed and procedures developed that will allow for enhanced cleaning of cabinets and tools in an inorganically, organically, and biologically clean manner. We are also designing a large multi-purpose Advanced Curation laboratory that will allow us to develop the techniques necessary to fully support the Hayabusa2 and OSIRIS-REx missions, as well as future possible sample return missions (e.g., Lunar Polar Volatiles, Mars, Comet Surface). A micro-CT (micro Computed Tomography) laboratory dedicated to the study of astromaterials has come online within JSC Curation, and we plan to add additional facilities that will enable non-destructive (or minimally-destructive) analyses of astromaterials in the near future (e.g., micro-XRF (micro X-Ray Fluorescence), confocal imaging Raman Spectroscopy). These facilities will be available to: (1) develop sample handling and storage techniques for future sample return missions, (2) be utilized by PET (Positron Emission Tomography) for future sample return missions, (3) for retroactive PET-style analyses of our existing collections, and (4) for periodic assessments of the existing sample collections.

Zeigler, Ryan A.↗

NASA NTRS2016

Curating NASA's Past, Present, and Future Astromaterial Sample Collections

The Astromaterials Acquisition and Curation Office at NASA Johnson Space Center (hereafter JSC curation) is responsible for curating all of NASA's extraterrestrial samples. JSC presently curates 9 different astromaterials collections in seven different clean-room suites: (1) Apollo Samples (ISO (International Standards Organization) class 6 + 7); (2) Antarctic Meteorites (ISO 6 + 7); (3) Cosmic Dust Particles (ISO 5); (4) Microparticle Impact Collection (ISO 7; formerly called Space-Exposed Hardware); (5) Genesis Solar Wind Atoms (ISO 4); (6) Stardust Comet Particles (ISO 5); (7) Stardust Interstellar Particles (ISO 5); (8) Hayabusa Asteroid Particles (ISO 5); (9) OSIRIS-REx Spacecraft Coupons and Witness Plates (ISO 7). Additional cleanrooms are currently being planned to house samples from two new collections, Hayabusa 2 (2021) and OSIRIS-REx (2023). In addition to the labs that house the samples, we maintain a wide variety of infra-structure facilities required to support the clean rooms: HEPA-filtered air-handling systems, ultrapure dry gaseous nitrogen systems, an ultrapure water system, and cleaning facilities to provide clean tools and equipment for the labs. We also have sample preparation facilities for making thin sections, microtome sections, and even focused ion-beam sections. We routinely monitor the cleanliness of our clean rooms and infrastructure systems, including measurements of inorganic or organic contamination, weekly airborne particle counts, compositional and isotopic monitoring of liquid N2 deliveries, and daily UPW system monitoring. In addition to the physical maintenance of the samples, we track within our databases the current and ever changing characteristics (weight, location, etc.) of more than 250,000 individually numbered samples across our various collections, as well as more than 100,000 images, and countless "analog" records that record the sample processing records of each individual sample. JSC Curation is co-located with JSC's Astromaterials Research Office, which houses a world-class suite of analytical instrumentation and scientists. We leverage these labs and personnel to better curate the samples. Part of the cu-ration process is planning for the future, and we refer to these planning efforts as "advanced curation". Advanced Curation is tasked with developing procedures, technology, and data sets necessary for curating new types of collections as envi-sioned by NASA exploration goals. We are (and have been) planning for future cu-ration, including cold curation, extended curation of ices and volatiles, curation of samples with special chemical considerations such as perchlorate-rich samples, and curation of organically- and biologically-sensitive samples.

Zeigler, R. A.↗

DOE OSTI2024

An Updated Simulation of the Booster Neutrino Beam

For accelerator neutrino experiments, an accurate prediction of the incoming neutrino flux is crucial for reducing uncertainties for all physics measurements. In this exciting period for the Short-Baseline Neutrino program at Fermilab, with far detector (ICARUS) already operating and the near detector (SBND) nearing operation, an updated flux model for the Booster Neutrino Beam (BNB) is presented. The BNB delivers 8 GeV protons to a beryllium target, subsequently producing neutrinos from the decay of the secondary beam of hadrons. A updated Monte-Carlo simulation of beam production in GEANT4 has been created, allowing predictions to be made for detectors with different baselines, offsets and sizes. This new simulation contains new features, such as a full neutrino ancestry to handle hadron production systematics with more precision, with a view to storing all resulting particles - including neutral mesons - from the proton-Beryllium scatter to allow the study of exotic BSM scenarios. Results are presented, with comparisons to the original flux simulated for the MiniBooNE experiment.

Paton, Josephine↗

NASA NTRS2002

Ion Dynamic Capture Experiments With The High Performance Antiproton Trap (HiPAT)

To take the first step towards using the energy produced from the matter-antimatter annihilation for propulsion applications, the NASA Marshall Space Flight Center (MSFC) Propulsion Research Center (PRC) has initiated a research activity examining the storage of low energy antiprotons. The High Performance Antiproton Trap (HiPAT) is an electromagnetic system (Penning-Malmberg design) consisting of a 4 Tesla superconductor, a high voltage electrode confinement system, and an ultra high vacuum test section. It has been designed with an ultimate goal of maintaining 10(exp 12) charged particles with a half-life of 18 days. Currently, this system is being evaluated experimentally using normal matter ions that are cheap to produce, relatively easy to handle, and provide a good indication of overall trap behavior (with the exception of assessing annihilation losses). The ions are produced via a positive hydrogen ion source and transported to HiPAT in a beam line equipped with electrostatic optics. The optics serve to both focus and gate the incoming ions, providing microsecond-timed beam pulses that are dynamically captured by cycling the HiPAT forward containment field like a "trap door". Initial dynamic capture experiments have been successfully performed with beam energy and currents set to 1.9 kV and 23 micro-amps, respectively. At these settings up to 2x10(exp 9) ions have been trapped during a single dynamic cycle.

Martin, James↗

NASA NTRS2002

Overview of the High Performance Antiproton (HiPAT) Experiment

The annihilation of matter with antimatter represents the highest energy density of any known reaction, producing 10(exp 8) MJ/g, approximately 10 orders of magnitude more energy per unit mass than chemical based combustion. To take the first step towards using this energy for propulsion applications the NASA MSFC Propulsion Research Center (PRC) has initiated a research activity examining the storage of low energy antiprotons. Storage was identified as a key enabling technology since it builds the experience base necessary to understand the handling of antiprotons for virtually all utilization and high-density storage concepts. To address this need, a device referred to as the High Performance Antiproton Trap (HiPAT) is under development at the NASA MSFC PRC. The HiPAT is an electromagnetic system (Penning-Malmberg design) consisting of a 4 Tesla superconductor, a high voltage confinement electrode system (operation up to 20 KV), and an ultra high vacuum test section (operating in the 10(exp -12) torr range). The system was designed to be portable with an ultimate goal of maintaining 10(exp 12) charged particles with a half-life of 18 days. Currently, this system is being experimentally evaluated using normal matter ions which are cheap to produce and relatively easy to handle. These normal ions provide a good indication of overall trap behavior, with the exception of assessing annihilation losses. The ions are produced external to HiPAT using two hydrogen ion sources, with adjustable beam energy and current. Ion are transported in a beam line and controlled through the use of electrostatic optics. These optics serve to both focus and gate the incoming ions, providing microsecond-timed pulses that are dynamically captured by cycling the HiPAT electric containment field like a 'trap door'. The layout of this system more closely simulates the operations expected at an actual antiproton production facility where 'packets' of antiprotons with pulse widths measured in 100's of nanoseconds could be provided. Initial dynamic capture experiments have been performed with both trap and ton source system functioning at approximately 10% of maximum levels, minimizing the potential for extraneous effects. Dynamic trapping techniques demonstrated the successful capture of millions of hydrogen ions with good agreement with the predicted loading based on the timing sequence, trap electric field, and ion beam current. These techniques will be expanded to examine multiple shot capture or 'stacking' to increase the total number of ions stored within HiPAT.

Martin, James J.↗

DOE OSTI2024

MLOps for Beam Controls

Machine learning operations (MLOps) is the standardization and streamlining of the ML development lifecycle to address the challenges associated with large-scale machine learning applications. The full MLOps pipeline consists of open-source tools: DataHub, MinIO and MLflow. It is being used for dataset management and model development to handle changing data dependencies, varying business needs, reproducibility, and diverse teams working with differing tools and skills. To demonstrate the completion of an MLOps pipeline for particle accelerator operations, we are deploying a simple script that computes settings for the Booster’s gradient magnet power supply. Once the demonstration is complete, we will develop and deploy ML-based optimization algorithms to improve Booster’s overall efficiency. This MLOps pipeline opens the gate to systematically develop and deploy ML applications for accelerator controls and diagnostics.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

MLOps for Beam Controls

Machine learning operations (MLOps) is the standardization and streamlining of the ML development lifecycle to address the challenges associated with large-scale machine learning applications. The full MLOps pipeline consists of open-source tools: DataHub, MinIO and MLflow. It is being used for dataset management and model development to handle changing data dependencies, varying business needs, reproducibility, and diverse teams working with differing tools and skills. To demonstrate the completion of an MLOps pipeline for particle accelerator operations, we are deploying a simple script that computes settings for the Booster’s gradient magnet power supply. Once the demonstration is complete, we will develop and deploy ML-based optimization algorithms to improve Booster’s overall efficiency. This MLOps pipeline opens the gate to systematically develop and deploy ML applications for accelerator controls and diagnostics.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

MLOps for Beam Controls

Machine learning operations (MLOps) is the standardization and streamlining of the ML development lifecycle to address the challenges associated with large-scale machine learning applications. The full MLOps pipeline consists of open-source tools: DataHub, MinIO and MLflow. It is being used for dataset management and model development to handle changing data dependencies, varying business needs, reproducibility, and diverse teams working with differing tools and skills. To demonstrate the completion of an MLOps pipeline for particle accelerator operations, we are deploying a simple script that computes settings for the Booster’s gradient magnet power supply. Once the demonstration is complete, we will develop and deploy ML-based optimization algorithms to improve Booster’s overall efficiency. This MLOps pipeline opens the gate to systematically develop and deploy ML applications for accelerator controls and diagnostics.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Explainable and Differentiable Reinforcement Learning for Multi-objective Optimization in Particle Accelerators

Operating particle accelerators involves optimizing multiple goals simultaneously, which can be challenging due to trade-offs among objectives. While evolutionary algorithms like the genetic algorithm (GA) have been used for various Multi-Objective Optimization (MOO) tasks, they are not inherently suited for complex control problems. This talk highlights two variations of Reinforcement Learning (RL) for concurrently optimizing heat load and trip rates at the Continuous Electron Beam Accelerator Facility (CEBAF). The problem involves strict constraints on individual states, actions, and overall energy requirements of the beam. First, this talk highlights how differentiability can be harnessed through a Deep Differentiable Reinforcement Learning (DDRL) approach to address MOO issues within particle accelerators. We examine the DDRL method alongside Model Free Reinforcement Learning (MFRL), GA, and Bayesian Optimization (BO). The performance of these methods is assessed by generating a Pareto-front for two objectives. Our findings indicate that DDRL excels in handling high-dimensional problems more effectively than MFRL, BO, and GA. Next, we will show integration of explainable physics-based constraints into RL algorithms to enhance trans- parency and trust in decision-making processes by enabling users to verify that agents adhere to established physical principles. This surrogate function can be modeled using neural networks or sparse dictionary mod- els. By examining the mathematical form of the learned constraint function, we are able to confirm the agent has learned to use the established physics of each environment provided but the surrogate model. In addi- tion, we find that the introduction of a mathematical functional dictionary based surrogate model enables our reinforcement learning algorithms to reliably converge for difficult high-dimensional accelerator controls environments.

Rajput, Kishansingh [Thomas Jefferson National Acc↗

DOE OSTI2025

Virtual to Physical: Reinforcement Learning to Optimize SNS Particle Accelerator Controls

Complex accelerators must have control systems that can handle dynamic nonlinear environments. This makes traditional control methods unsuitable as they can struggle to adapt to these uncertainties. This provides an ideal environment for reinforcement learning algorithms as they are adaptable and generalizable. We present a reinforcement learning pipeline that can effectively handle the dynamics of a complex accelerator. We test and prove our pipelines capabilities on multiple environments including the Spallation Neutron Source (SNS) and the Beam Test Facility (BTF) at Oakridge National Lab (ORNL). Due to the limited time available to train an online algorithm like reinforcement learning on a real accelerator, we utilize a virtual twin accelerator (VIRAC) developed by ORNL to pretrain the policy and show its ability to converge in the virtual environment. We then test the adaptability of the pretrained RL model by applying it on the real accelerator and comparing the results. Utilizing our Scientific Optimization and Controls Toolkit (SOCT) and open-source standards such as Gymnasium we create and solve for a MEBT orbit correction problem in the SNS and an emittance maximization problem in the BTF. We show how Twin Delayed Deep Deterministic Policy Gradient (TD3) can solve this optimization environment in the virtual accelerator and transfer this policy onto the real accelerator for inference and model retraining. We show how reinforcement learning can be utilized as a control system for complex accelerators and provide a model pipeline for how an implementation performs and can be adapted to new accelerator control problems.

Kasparian, Armen [Thomas Jefferson National Accele↗