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A series of analyses are presented for Experiment S009, nuclear emulsion (MSFC), to be used for evaluating the performance of the Skylab corollary experiments under preflight, inflight, and postflight conditions. Experiment contingency plan workaround procedure and malfunction analyses are included in order to assist in making the experiment operationally successful.
Pulse height analyzer and logic circuits for IMP SATELLITE nuclear abundance experiments for identifying cosmic ray particles
Pulse height analyzer and logic circuits for interplanetary monitoring platform nuclear abundance experiment - explorer xviii
Computer program handles data from low-energy nuclear physics experiments which utilize the ND-160 pulse-height analyzer and the PHYLIS computing system. The program allows experimenters to choose from about 50 different basic data-handling functions and to prescribe the order in which these functions will be performed.
Charged track reconstruction is a critical task in nuclear physics experiments, enabling the identification and analysis of particles produced in high-energy collisions. Machine learning (ML) has emerged as a powerful tool for this purpose, addressing the challenges posed by complex detector geometries, high event multiplicities, and noisy data. Traditional methods rely on pattern recognition algorithms like the Kalman filter, but ML techniques, such as neural networks, graph neural networks (GNNs), and recurrent neural networks (RNNs), offer improved accuracy and scalability. By learning from simulated and real detector data, ML models can identify and classify tracks, predict trajectories, and handle ambiguities caused by overlapping or missing hits. Moreover, ML-based approaches can process data in near-real-time, enhancing the efficiency of experiments at large-scale facilities like the Large Hadron Collider (LHC) and Jefferson Lab (JLAB). As detector technologies and computational resources evolve, ML-driven charged track reconstruction continues to push the boundaries of precision and discovery in nuclear physics. In these proceedings, we highlight advancements in charged track identification leveraging Artificial Intelligence within the CLAS12 detector, achieving a notable enhancement in experimental statistics compared to traditional methods. Additionally, we showcase real-time event reconstruction capabilities, including the inference of charged particle properties, such as momentum, direction, and species identification, at speeds matching data acquisition rates. These innovations enable the extraction of physics observables directly from the experiment in real-time.
PDP-8 computer interface used with detector system for charged particle nuclear physics experiments
Blocked two level factorial experiment to measure radiolysis of water in water cooled reactors
Nuclear fusion appears to be the most promising concept for producing extremely high specific impulse rocket engines. One particular fusion concept which seems to be particularly well suited for fusion propulsion applications is the gasdynamic mirror (GDM). An experimental GDM device has been constructed at the NASA Marshall Space Flight Center to provide an initial assessment of the feasibility of this type of propulsion system. An initial shakedown of the device is currently underway with initial experiments slated to occur in late 2001. This device would operate at much higher plasma densities and with much larger L/D ratios than previous mirror machines. The high L/D ratio minimizes to a large extent certain magnetic curvature effects which lead to plasma instabilities causing a loss of plasma confinement. The high plasma density results in the plasma behaving much more like a conventional fluid with a mean free path shorter than the length of the device. This characteristic helps reduce problems associated with 'loss cone' microinstabilities. The device has been constructed to allow a considerable degree of flexibility in its configuration thus permitting the experiment to grow over time without necessitating a great deal of additional fabrication.
Nuclear fusion appears to be the most promising concept for producing extremely high specific impulse rocket engines. One particular fusion concept which seems to be particularly well suited for fusion propulsion applications is the gasdynamic mirror (GDM). This device would operate at much higher plasma densities and with much larger LD ratios than previous mirror machines. Several advantages accrue from such a design. First, the high LA:) ratio minimizes to a large extent certain magnetic curvature effects which lead to plasma instabilities causing a loss of plasma confinement. Second, the high plasma density will result in the plasma behaving much more Re a conventional fluid with a mean free path shorter than the length of the device. This characteristic helps reduce problems associated with "loss cone" microinstabilities. An experimental GDM device is currently being constructed at the NASA Marshall Space Flight Center to provide an initial assessment of the feasibility of this type of propulsion system. Initial experiments are expected to commence in the late fall of 2000.
The Experimental Physics Software and Computing Infrastructure (EPSCI) group at Jefferson Lab is leading the use of machine learning (ML) to enhance control systems in nuclear physics experiments. Collaborating closely with domain experts and data scientists, we have developed an ML-based control system that uses a Gaussian process to dynamically adjust the high voltage of the GlueX Central Drift Chamber. This results in stable detector performance by adapting to environmental changes, thereby reducing the offline calibration effort. Furthermore, we are developing ML-driven systems for optimizing the polarization of photon beams and polarized cryotargets. These systems will maintain the optimal microwave frequency in cryogenic targets and make real-time adjustments to diamond radiators for polarized photon sources, tasks traditionally handled by human operators. By automating these functions, we aim to optimize the polarization, reduce downtime, and minimize human error. This talk will highlight the development of reliable ML-based control systems and the policies to ensure they are both effective and trustworthy.
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The Molten Chloride Reactor Experiment (MCRE) will be the first reactor featuring a fast-spectrum molten chloride circulating nuclear fuel system in the world. Planning for equipment removal and disposal (ERD) of MCRE has identified several technology gaps due to the unique environment of this nuclear experiment. Some of the gaps arise from the application of existing disassembly and/or sizing methods to novel material forms or in novel configurations. Others arise from unknown material behavior. This paper summarizes proposed test plans for ERD validation experiments to address these complicated or unknown equipment removal procedures. At the Waste Management Symposia in 2024, the Idaho National Laboratory (INL) MCRE ERD team presented the challenges associated with hosting multiple nuclear experiments in series with only brief transition periods between systems. Such difficulties include higher dose rates, the presence of radioisotopes infrequently encountered in reactor decommissioning and radioactive waste management, lack of intrinsic remote-operations infrastructure in the test bed, space constraints in the test bed, and contamination minimization requirements. To address these challenges, remote or semi-remote technologies are planned to be implemented in a non-hot cell environment with limited space availability. The team also discussed how a systems engineering approach is being used for conceptual development and design of equipment removal systems to address these challenges. For example, to reduce constraints for the removal of more difficult components, non-activated, noncontaminated elements are planned to be taken out first where possible. Still, there are complexities associated with the remaining components. In this work, the operational framework for MCRE ERD was reviewed for technical gaps and open questions, and test plans were drafted to address these areas. The tests plans were written for the following categories: vision systems, pipe cutting, drill/grout/filler, flush salt, and miscellaneous, with the miscellaneous group consisting of tests like techniques for removing bearings and reflector bricks. The test plans explore material, infrastructure, and staffing requirements needed for test execution. The test plans additionally focus on the evaluation of success. Determining the outcome of a test is imperative - as these explorative actions have the potential to rearrange or re-scope planned ERD activities. Success criteria identified thus far include required tool output, required area(s), debris production and mitigation, and repeatability. Test plans are an essential aspect of the systems engineering approach to MCRE ERD. They are used as the beginning steps in defining use cases for the ERD system. Performance of the validation tests is expected to begin in the summer of 2025 and will take approximately 9 to 12 months to complete. Execution of these plans will be expedited by specifying test needs ahead of time, facilitating efficient interactions with any subcontractors tasked with running the requested tests. Evaluating the outcomes of these tests will inform MCRE ERD procedures and timing and will also identify additional technical constraints for the MCRE ERD System. This upfront process optimization effort will help the project save time and resources at the end of the experiment.
Human space flight requires protecting astronauts from the harmful effects of space radiation. The availability of measured nuclear cross section data needed for these studies is reviewed in the present paper. The energy range of interest for radiation protection is approximately 100 MeV/n to 10 GeV/n. The majority of data are for projectile fragmentation partial and total cross sections, including both charge changing and isotopic cross sections. The cross section data are organized into categories which include charge changing, elemental, isotopic for total, single and double differential with respect to momentum, energy and angle. Gaps in the data relevant to space radiation protection are discussed and recommendations for future experiments are made.
The goals of this project are to search for hypothesized spin-hall effects of neutrons and variants of neutron-electron hybrid spin hall effects in quantum materials with strong spin-orbit-coupling (SOC) – such as: • Deflection of neutrons dependent on spin (polarization) states (a); • Electrical current induced electron spin polarization or (dynamically polarized) nuclear spin polarization (as suggested in PI’s prior work) may affect polarization states of transmitted or reflected neutron beam • Spin polarized neutrons transfer some spin angular momenta to electrons, which get converted to electronic charge voltage via electronic inverse spin Hall effect (b), which if realized, offers an electrical method to detect neutron spins.
The first exposure on a spacecraft of a nuclear emulsion apparatus designed to collect 1000 high quality tracks of heavy nuclei under a negligible thickness of matter (0.07 g/sq cm) is described. The cosmic ray detector consisted of a stack of nuclear emulsions that were designed to register at least 400 heavy nuclei tracks for each 10 hours of useful exposure. The spacecraft had to be oriented in a heads-up attitude during the 10-hour period to eliminate atmospheric albedo particles. The results are as follows: (1) a definite odd-even effect, with low abundances for elements of atomic number 7, 9, and 11; (2) a ratio O/C approximately 0.9; (3) Ne/C, Mg/C, and Si/C ratios between 0.2 and 0.3; (4) an abundance gap in the region 15 less than or equal to Z less than or equal to 19; and (5) a ratio (20 less than or equal to Z less than or equal to 28)/C 0.2, with a large concentration at Z = 26. These results are indicative that successful exposures of nuclear emulsions were obtained on the Gemini 11 mission.
Experimental studies show that the output of a CO2 laser is significantly increased by products of the nuclear reaction He-3 (n,p)T. Helium-3 was used in lieu of the natural helium normally present in the 1:1:8 CO2:N2:He laser gas mixture (pressure = 6 torr). The laser assembly was then exposed to a reactor thermal neutron flux of about 100 million neutrons/sq cm/sec. Power output of the laser doubled while the electrical power input decreased; electrical efficiency was thus more than doubled. Results indicate that additional ionization by the energetic charged particles may be responsible for the improved laser performance.
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