3,904,055 Search Results

4 page paper submitted to ANS Student conference. Summary of paper similar to the following abstract: The ability to predict how a reactor will operate, understand when anomalous conditions arise, and ensure a reactor is being operated as expected is crucial for deploying new nuclear facilities. Digital twins serve as a unique solution to recognizing reactor behavior; however, they require data to be useful. For next-generation reactors, this data may not currently be available. To explore how synthetic physics-informed reactor data can be used to predict reactor operations, a recurrent neural network was implemented for the Idaho State University AGN-201 digital twin. The goal of this work is to determine how synthetic data can be used to train a recurrent neural network model for predicting the reactor power of the AGN-201. The recurrent neural network was validated using both synthetic and real operational data. We envision this approach will help bridge the gap between the virtual and physical sides of a digital twin, where reactor physics models based on as-built data can be corrected for actual operating parameters to ensure the virtual model mirrors reality.

Abstract Multidimensional spectroscopy has a long history originating from nuclear magnetic resonance, and has now found widespread application at infrared and optical frequencies as well. However, the energy scales of traditional multidimensional probes have been ill-suited for studying quantum materials. Recent technological advancements have now enabled extension of these multidimensional techniques to the terahertz frequency range, in which collective excitations of quantum materials are typically found. This Perspective introduces the technique of two-dimensional terahertz spectroscopy (2DTS) and the unique physics of quantum materials revealed by 2DTS spectra, accompanied by a selection of the rapidly expanding experimental and theoretical literature. While 2DTS has so far been primarily applied to quantum materials at equilibrium, we provide an outlook for its application towards understanding their dynamical non-equilibrium states and beyond.

Hamiltonian moments in Fourier space—expectation values of the unitary evolution operator under a Hamiltonian at different times—provide a convenient framework to understand quantum systems. They offer insights into the energy distribution, higher-order dynamics, response functions, correlation information, and physical properties. This paper focuses on the computation of Fourier moments within the context of a nuclear effective field theory on superconducting quantum hardware. The study integrates echo verification and noise renormalization into Hadamard tests using control reversal gates. These techniques, combined with purification and error suppression methods, effectively address quantum hardware decoherence. The analysis, conducted using noise models, reveals a significant reduction in noise strength by two orders of magnitude. Moreover, quantum circuits involving up to 266 gates over five qubits demonstrate high accuracy under these methodologies when run on IBM superconducting quantum devices. Published by the American Physical Society 2025

MagNetUS is a network of scientists and research groups that coordinates and advocates for fundamental magnetized plasma research in the USA. Its primary goal is to bring together a broad community of researchers and the experimental and numerical tools they use in order to facilitate the sharing of ideas, resources and common tasks. Discussed here are the motivation and goals for this network and details of its formation, history and structure. An overview of associated experimental facilities and numerical projects is provided, along with examples of scientific topics investigated therein. Finally, a vision for the future of the organization is given.

We solve ‘half’ the problem of finding three-dimensional quasisymmetric magnetic fields that do not necessarily satisfy magnetohydrostatic force balance. This involves determining which hidden symmetries are admissible as quasisymmetries, and then showing explicitly how to construct quasisymmetric magnetic fields given an admissible symmetry. The admissibility conditions take the form of a system of overdetermined nonlinear partial differential equations involving second derivatives of the symmetry's infinitesimal generator.

The frequency of type-I ELMs decreases as the initiation of the neutral beam injection (NBI) heating is delayed with respect to the time when plasma current (Ip) reaches flat-top in the ITER Baseline Scenario discharges in DIII-D. Henceforth, the time gap between the NBI initiation and Ip flat-top will be referred to as “heating delay.” As the heating delay is modified, pedestal formation follows different trajectories in the edge current density–pedestal pressure gradient (jedge−∇peped) space from the L-H transition toward the first ELM event. During the stationary phase after the first ELM, the ELM frequency (fELM) decreases by a factor of ∼2 as the heating delay is increased. A longer pedestal recovery time in the inter-ELM period is observed for the low fELM discharges as compared to the high fELM discharges. Both low and high fELM discharges show nearly identical profiles of electron density and temperature and have a similar MHD stability just before an ELM crash. However, a marked difference is observed in the magnetic spectrogram of the high and low fELM discharges in response to the variation in the heating delay. The main difference is in the 200–400 kHz range of the magnetic spectra. A quasi-coherent mode (QCM) at 220 kHz and weaker broadband fluctuations are observed in the high fELM discharges, while only strong broadband fluctuations are prevalent in the low fELM discharges. ELM-synchronized analysis shows that the time evolution of these modes is different for the high and low fELM discharges. The localization of both these modes is confirmed at the maximum gradient region of the pedestal. We hypothesize that these modes cause important pedestal transport and that the difference in the pedestal recovery of the high and low fELM discharges is a result of the difference in transport driven by these modes, as they change with changes in the heating delay. It is demonstrated experimentally for the first time that discharges with similar pedestal parameters can carry the history of the heating delay into the stationary phase and that changes in turbulent-driven transport are a likely cause of changes in fELM observed with variations of heating delay.

Polar-direct-drive implosions at the National Ignition Facility generated large plasma volumes to study the 3He + 3He fusion reaction. The ion temperature, which determines the Gamow peak energy, was constrained by isolating the thermal contribution to the D3He-proton spectral width in a 3He plasma doped with deuterium. X-ray penumbral imaging was used to measure electron temperature, density, and hotspot volume, which was subsequently used to model the spectral broadening from plasma stopping power. Results showed 30% of the D3He-proton spectral width was due to stopping power, with residual flows contributing ≈10%. The 3He temperature was determined as T3He = 12.4 ± 3.2 keV, corresponding to a Gamow energy of 95 ± 14 keV. These experiments achieved the lowest Gamow energy to date for studying 3He + 3He fusion in high energy density plasma, approaching conditions in the Sun.

Understanding the complex temporal and spatial correlations of ions in disordered perovskite oxides is critical to rationalize their functional properties. Here, we provide insights into the longstanding controversy regarding the off-centering of transition metal (TM) ions in the archetypal ferroelectric alloy KTa ${}_{1-x}$ Nb ${}_x$ O ${}_3$ (KTN). By mapping the full energy ( $E$ ) and wavevector ( $\mathbf{Q}$ ) dependence of the dynamical structure factor $S(\mathbf{Q},E)$ using neutron scattering, and rationalizing our observations with atomistic simulations leveraging machine learning, we fully resolve the static v s dynamic nature of diffuse scattering sheets, as well as their composition ( $x$ ) and temperature dependence. Our first-principles simulations, extended with machine-learning molecular dynamics, reproduce both inelastic neutron spectra and diffuse features, and establish how dynamically correlated TM off-centerings couple to phonons, unifying local and collective viewpoints. This study sheds light into an exemplary ferroelectric system and shows the importance of mapping the full $S(\mathbf{Q},E)$ to reveal critical spatiotemporal correlations of atomic disorder from which functional properties emerge.

Abstract Timelapse microscopy has recently been employed to study the metabolism and physiology of cyanobacteria at the single-cell level. However, the identification of individual cells in brightfield images remains a significant challenge. Traditional intensity-based segmentation algorithms perform poorly when identifying individual cells in dense colonies due to a lack of contrast between neighboring cells. Here, we describe a newly developed software package called Cypose which uses machine learning (ML) models to solve two specific tasks: segmentation of individual cyanobacterial cells, and classification of cellular phenotypes. The segmentation models are based on the Cellpose framework, while classification is performed using a convolutional neural network named Cyclass. To our knowledge, these are the first developed ML-based models for cyanobacteria segmentation and classification. When compared to other methods, our segmentation models showed improved performance and were able to segment cells with varied morphological phenotypes, as well as differentiate between live and lysed cells. We also found that our models were robust to imaging artifacts, such as dust and cell debris. Additionally, the classification model was able to identify different cellular phenotypes using only images as input. Together, these models improve cell segmentation accuracy and enable high-throughput analysis of dense cyanobacterial colonies and filamentous cyanobacteria.

Artificial Intelligence (AI) foundation models, while successful in various domains of language, speech, and vision, have not been adopted in production for fusion energy experiments. This brief paper presents how AI foundation models can be used for fusion energy diagnostics, enabling, for example, visual automated logbooks to provide greater insights into chains of plasma events in a discharge, in time for between-shot analysis.