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At least 1,315 records · Page 73

The impact of human expert visual inspection on the discovery of strong gravitational lenses

We investigate the ability of human ‘expert’ classifiers to identify strong gravitational lens candidates in Dark Energy Survey like imaging. We recruited a total of 55 people that completed more than 25 percent of the project. During the classification task, we present to the participants 1489 images. The sample contains a variety of data including lens simulations, real lenses, non-lens examples, and unlabelled data. We find that experts are extremely good at finding bright, well-resolved Einstein rings, while arcs with g-band signal to noise less than ~25 or Einstein radii less than ~1.2 times the seeing are rarely recovered. Very few non-lenses are scored highly. There is substantial variation in the performance of individual classifiers, but they do not appear to depend on the classifier’s experience, confidence or academic position. These variations can be mitigated with a team of 6 or more independent classifiers. Our results give confidence that humans are a reliable pruning step for lens candidates, providing pure and quantifiably complete samples for follow-up studies.

79 ASTRONOMY AND ASTROPHYSICS↗

Measuring & Verifying Monopole Gauge Factors Into 50 Ohms

Student poster for HEART 2025. Abstract: Experimental data from multiple methods of measurement as compared to simulation and model data show that free-field antenna characterization methods can be appropriately applied to short, surface mount monopole antennas used in small cavities for shielding effectiveness testing.

Ferreri, Joseph Anthony [Sandia National Laborator↗

Preliminary verification of the MCNP perturbation and fixed-source tally sensitivity tools

Integral benchmark experiments are vital in the adjustment and validation of the nuclear data that govern predictive simulations across the nuclear community. The nuclear data sensitivity capabilities of the Monte Carlo N-Particle (MCNP ®) transport code are currently limited; however, expanding sensitivity capabilities will allow benchmark experiments to be designed to resolve compensating errors and adjust nuclear data where previously prohibitively difficult. This paper provides details of a preliminary verification for the use of (i.) the recently revised perturbation and (ii.) developmental fixed-source sensitivity tools within MCNP to calculate sensitivities of tallied responses (such as current integrated over a surface, F1, and flux averaged over a cell, F4) to nuclear data in fixed-source simulations. Energy-binned and energy-integrated sensitivities calculated with these tools are compared against sensitivities calculated using a central-difference approximation. Here, the verification is completed for four configurations of a benchmarked system using a 4.5-kg plutonium sphere surrounded by varying amounts of copper and/or polyethylene. The results show that sensitivities calculated with the perturbation and fixed-source sensitivity tools agree with the central-difference-based approach.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Quantum and classical spin dynamics across temperature scales in the S = 1 / 2 Heisenberg antiferromagnet

Using the framework of semiclassical Landau-Lifshitz dynamics (LLD), we conduct a systematic investigation of the temperature-dependent spin dynamics in the S = 1 / 2 Heisenberg square-lattice antiferromagnet (SAFM). By performing inelastic neutron scattering measurements on Zn 2 VO ( PO 4 ) 2 (ZVPO) and corresponding finite-temperature spin dynamics simulations based on LLD, we present a comprehensive analysis that bridges quantum and classical spin dynamics over a broad temperature range. First, remarkable agreement between experimental data and LLD simulations is found in the paramagnetic phase of ZVPO, demonstrating the capability of LLD in accurately determining the spin Hamiltonian of S = 1 / 2 systems and capturing the quantum-to-classical crossover of their spin dynamics. Second, by analyzing the discrepancies between the experimental data and the LLD simulations at lower temperatures, we determine the experimental temperature dependence of the quantum effects in the excitation spectrum of the S = 1 / 2 SAFM: the quantum renormalization factor for the magnon energies and the quantum continuum above the one-magnon bands. Notably, the emergence of each quantum effect is found to correlate with the formation of three-dimensional long-range order. This work demonstrates the utility of LLD in gaining experimental insights into the temperature-induced modifications of quantum spin dynamics and their convergence towards classical expectations at higher temperatures. This motivates further applications to more challenging quantum antiferromagnets dominated by stronger quantum fluctuations. Published by the American Physical Society 2024

36 MATERIALS SCIENCE↗

Data-Driven Validation of NOvA's Convolutional Neural Network for Electron (Anti)Neutrino Selection

NOvA is a long-baseline neutrino oscillation experiment, designed to make measurements of several oscillation parameters using muon neutrino disappearance and electron neutrino appearance. It consists of two functionally equivalent detectors and utilizes the Fermilab NuMI neutrino beam. NOvA uses a convolutional neural network for particle identification of electron neutrino events with a validation process that includes several data-driven techniques. These Muon Removed studies ensure that our classifier performs the same on data as it does on simulation. In particular, Muon Removed Electron-Added studies involve selecting muon neutrino charged current events from both data and simulation and replacing the muon with a simulated electron of similar energy. For Muon Removed Bremsstrahlung and Muon Removed Decay-in-Flight studies, we remove muonic hits from cosmic muons that have either experienced Bremsstrahlung radiation or decayed in flight, producing samples of pure electromagnetic showers. Each of these electron neutrino-like samples are then evaluated by our classifier to obtain selection efficiencies. Our most recent analysis showed good agreement in the electron selection efficiency between data and simulation using these techniques. Furthermore, these cross-checks can be extended to corrections to our predicted electron neutrino signal. The impact of one such set of corrections on the overall analysis results were also evaluated in thesis.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Incident beam simulations for the CNCS instrument

This data set contains incident beam simulations for the 100 most frequently used configurations of the cold neutron chopper spectrometer (CNCS) instrument at the Spallation Neutron Source. A mcpl output was placed 0.2 m upstream of the sample position. Each file provides enough events to sufficiently sample the beam profile and intensity incident on the sample for that configuration. They can then be used for sample simulations.

47 OTHER INSTRUMENTATION↗

a priori uncertainty quantification of reacting turbulence closure models using Bayesian neural networks

While many physics-based closure model forms have been posited for the sub-filter scale (SFS) in large eddy simulation (LES), vast amounts of data available from direct numerical simulations (DNS) create opportunities to leverage data-driven modeling techniques. Albeit flexible, data-driven models still depend on the dataset and the functional form of the model chosen. Increased adoption of such models requires reliable uncertainty estimates both in the data-informed and out-of-distribution regimes. Here, in this work, we employ Bayesian neural networks (BNNs) to capture both epistemic and aleatoric uncertainties in a reacting flow model. In particular, we model the filtered progress variable scalar dissipation rate which plays a key role in the dynamics of turbulent premixed flames. We demonstrate that BNN models can provide unique insights about the structure of uncertainty of the data-driven closure models. We also propose a method for the incorporation of out-of-distribution information in a BNN, which can be used for out-of-distribution query detection. The efficacy of the model is demonstrated by a priori evaluation on a dataset consisting of a variety of flame conditions and fuels.

97 MATHEMATICS AND COMPUTING↗

Feature Analysis, Tracking, and Data Reduction: An Application to Multiphase Reactor Simulation MFiX-Exa for In-Situ Use Case

As we enter the exascale computing regime, powerful supercomputers continue to produce much higher amounts of data than what can be stored for offline data processing. To utilize such high compute capabilities on these machines, much of the data processing needs to happen in situ, when the full high-resolution data is available at the supercomputer memory. In this article, we discuss our MFiX-Exa simulation, which models multiphase flow by tracking a very large number of particles through the simulation domain. In one of the use cases, the carbon particles interact with air to produce carbon dioxide bubbles from the reactor. These bubbles are of primary interest to the domain experts for these simulations. For this particle-based simulation, we propose a streaming technique that can be deployed in situ to efficiently identify the bubbles, track them over time, and use them to down-sample the data with minimal loss in these features.

97 MATHEMATICS AND COMPUTING↗

A scalable framework for efficient coupling of thermal and microstructural simulations in additive manufacturing

Predicting microstructure evolution in metal additive manufacturing (AM) is important for process optimization, but spatiotemporal scale disparities between thermal transport and microstructure evolution create significant challenges for efficient data transfer between simulation codes. To address this, we present Stork, a scalable framework for coupling thermal and microstructural simulations. Stork uses a sparse data representation to identify and store active solidification sub-volumes, enabling highly parallel quad-linear interpolation from coarse thermal grids to fine microstructure grids without large intermediate storage. We demonstrate the framework by coupling the semi-analytic heat transfer code 3DThesis with the time-parallel cellular automata code Toucan. This approach achieves over two orders of magnitude reduction in data generation time and file size compared to prior workflows. Numerical studies show that quad-linear interpolation preserves grain morphology and crystallographic texture in laser powder bed fusion (LPBF) simulations for coarsening ratios up to 16. Overall, Stork provides a scalable pathway for high-throughput, component-scale AM simulations on modern high-performance computing systems.

36 MATERIALS SCIENCE↗

Data-guided Multi-Map variables for ensemble refinement of molecular movies

Driving molecular dynamics simulations with data-guided collective variables offer a promising strategy to recover thermodynamic information from structure-centric experiments. In this study, the three-dimensional electron density of a protein, as it would be determined by cryo-EM or x-ray crystallography, is used to achieve simultaneously free-energy costs of conformational transitions and refined atomic structures. Unlike previous density-driven molecular dynamics methodologies that determine only the best map-model fits, our work employs the recently developed Multi-Map methodology to monitor concerted movements within equilibrium, non-equilibrium, and enhanced sampling simulations. Construction of all-atom ensembles along the chosen values of the Multi-Map variable enables simultaneous estimation of average properties, as well as real-space refinement of the structures contributing to such averages. Using three proteins of increasing size, we demonstrate that biased simulation along the reaction coordinates derived from electron densities can capture conformational transitions between known intermediates. The simulated pathways appear reversible with minimal hysteresis and require only low-resolution density information to guide the transition. The induced transitions also produce estimates for free energy differences that can be directly compared to experimental observables and population distributions. The refined model quality is superior compared to those found in the Protein Data Bank. We find that the best quantitative agreement with experimental free-energy differences is obtained using medium resolution density information coupled to comparatively large structural transitions. Practical considerations for probing the transitions between multiple intermediate density states are also discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DUNE – Simulation Validation of Fermilab Detector Reconstruction

DUNE (Deep Underground Neutrino Experiment) is Fermilab’s flagship international experiment designed to study neutrinos by sending an intense beam from Illinois to detectors located 1,300 kilometers away at the Sanford Underground Research Facility (SURF) in South Dakota. To prepare for such a large-scale experiment, physicists develop detailed simulations to produce mock data sets which are analyzed by the CAFAna framework. During my internship, I developed software using the CAFAna framework to analyze simulated detector data and generated plots to make data trends easier to interpret and identify patterns. My analysis has uncovered inconsistencies in reconstructed neutrino tracks, duplicated reconstructed tracks causing sporadic spikes in the data, and unnatural differences in energy levels between interaction types. These analyses help verify that the improvements to detector simulations do not introduce unintended resolution errors and ensure proper reconstruction performance, supporting DUNE’s goal of making precise neutrino measurements and advancing the Department of Energy’s mission of fundamental scientific discovery.

Vershaw, Andre [Unlisted, US, IL; Fermilab] (ORCID↗

Combined Cycle Integrated Thermal Energy Storage “CiTES” (Final Scientific/Technical Report)

The Phase I of this project confirmed the technical feasibility of a Combined Cycle integrated Thermal Energy Storage “CiTES” system, calculated the key performance parameters like power efficiency and costs, and proved its commercial value with full-year simulations for several US electricity markets with high degree of variable renewable generation and volatile hourly electricity prices. The core element of this project is the Electro Thermal Energy Storage (ETES) technology from Siemens Gamesa Renewable Energy GmbH, using thermally stable and inexpensive volcanic rocks as storage material and air as heat transfer medium. This technology is backed by more than 10 years of experience and a 440MMBTU (130MWh-th) pilot plant in Hamburg, Germany, which is in operation since 2019. The integration of this thermal storage in an existing combined cycle power plant (CCPP) is typical power plant technology without any major technology risks. It allows the storage of inexpensive renewable energy during times of surplus renewable generation and the discharge of this energy in times of high energy demand when the fossil plant is in operation. This supplements the fossil power generation with CO2-emission-free energy. The secondary effect of the CiTES system is that a small part of the stored thermal energy is used to keep the heat recovery steam generator (HRSG) and steam turbine (ST) of the combined cycle power plant in hot and ready-to-start condition. This enables the plant to start rapidly when fossil generation is required to satisfy demand as soon variable generation drops off in the evenings or during cloud cover and calm wind periods. Without pre-warming of the HRSG and ST, the CCPP would need several hours for a cold or warm start, burn a lot of gas and release high NOx emissions during start and wouldn’t be able to use the short times of high energy prices in an efficient or economical manner. The economic parameters of CiTES were determined by a full year “8760” simulation using a data set calculation for each of the hours of the year, and historical electricity and gas prices. For consistency, the simulations were focused on the pre-COVID year 2019. The financially most attractive markets were in the Energy Reliability Council of Texas (ERCOT) region, which allowed substantial value generation with arbitrage (charge with cheap energy during renewable surplus times and discharge when energy is needed and expensive). The improvement of flexibility with the CiTES system by pre-heating and warm-keeping of the CCPP allowed for additional power generation during short time periods when demand is high but renewable generation is down; when the hourly energy prices are highest in these markets. The simulations are based on 2019 data, when ERCOT had 27GW of installed photovoltaic (PV) and wind generation. They showed that the created revenue with the prototypically sized CiTES system of 1,000MMBTU (300MWh-th) falls a little bit short of what is expected from a commercially viable investment. The system has optimization opportunities for cost reduction and increased effectiveness which will be realized during a potential Phase II Pre-FEED study following this project. Furthermore, it is safe to assume that a lot of renewable generation capacity will be added all over the US in the coming years. As an example, ERCOT is predicting to more than double its renewable generation from 27GW in 2019 to a forecasted 63GW in 2023. This will increase the amount of renewable overproduction exponentially. This rapid increase of local overproduction and the need to curtail renewable generation is well documented by the California ISO (www.CAISO.com / managing oversupply). However, the simulations also revealed a weakness in the structure of the electricity markets in the US. More specifically, when electricity prices are very low and approaching negative levels, the owners of Variable Renewable Energy (VRE) will curtail a part of their facility to stabilize the price by reducing supply. This results in a situation in which storage facilities, which are integrated in existing fossil assets and don’t have the behind-the-meter benefit of a VRE, won’t be able to purchase low cost – otherwise curtailed – renewable energy off the grid. A special tariff, which motivates VRE owners to sell otherwise curtailed renewable energy to storage facilities (Hydrogen, thermal, pumped hydro, etc.) can solve this issue. The implementation of such a regulating tariff by Independent System Operators, thus avoiding renewable curtailment, is a pre condition for successful commercialization for renewable energy storage technologies. With this advancement of design and technology and improvements in the market environment, it can be expected that the Combined Cycle integrated Thermal Energy Storage proves itself as an important innovation to keep highly efficient, natural gas-based power generation economically successful and relevant for the power industry in the United States of America.

Wolf, Thorsten↗

Development and Validation of SAM Multi-dimensional Flow Model for Thermal Mixing and Stratification Modeling

Thermal mixing and stratification in large pools or enclosures are very important phenomena that are critical to nuclear reactor safety. Because of the wide ranges of time and length scales associated with such phenomena, accurate modeling and simulation of thermal mixing and stratification remain as the key unresolved, challenging problems for reactor transient analyses. In traditional system analysis codes, simplified zero-dimensional (0-D) models are widely used for their high numerical efficiency, but they generally suffer from very limited prediction accuracies or range of applicability. Like traditional system analysis codes, the current version of the SAM code has implemented such simplified 0-D and one-dimensional mixing models. On the other hand, high-resolution Computational Fluid Dynamics (CFD) tools are often used to model complex thermal mixing and stratification phenomena. They are, however, generally numerically expensive, and they require large amounts of computational resources. It is therefore desirable to implement advanced and efficient thermal mixing and stratification modeling capabilities embedded in a system analysis code. This approach will improve the accuracy of reactor safety analyses when thermal mixing and stratification are involved, and also avoid using the large computational resources needed for high-resolution CFD analysis. Currently, with the support of the U.S. DOE Office of Nuclear Energy’s Nuclear Energy Advanced Modeling and Simulation program, an effort has been launched to develop and implement a multi-dimensional flow model in the system analysis code SAM, and demonstrate its applications to model thermal mixing and stratification phenomena in large enclosures. The main outcomes of this research and development activity are summarized in this report, which presents an attempt to include a built-in advanced multi-dimensional flow model in a system analysis code with the focus on overcoming the simulation challenges of thermal mixing and stratification phenomena. In this report, we start with the introduction of existing SAM code capabilities to simulate thermal mixing and stratification phenomena, which is followed by a short summary of the multi-dimensional model implemented in the SAM code, including both the physical model and the Finite Element Method code implementation. In this study, two options were implemented in the SAM code to model turbulent flows: a relatively simple built-in turbulence model and an interface to accept externally computed turbulent viscosities (e.g., from a high- resolution CFD simulation). Code validation studies on this newly added capability were then carried out to compare SAM simulation results with experimental data from the SUPERCAVNA facility, which was designed to study the complex flow recirculation and thermal stratification phenomena relevant to sodium fast reactor designs. In this study, one transient and two steady-state test cases were used for code validation. Different approaches have been used to model the complex turbulence flow fields in the SUPERCAVNA facility. A highly simplified zero-equation turbulence model was first used, but it was determined that it is too simple to capture the complex turbulence flow fields in these test cases. Subsequently, the code validation continued with the use of turbulent viscosity data from high-resolution STAR-CCM+ CFD simulations to improve the accuracy of the results. Using this approach, the SAM simulation results showed very good agreement with both the SUPERCAVNA experimental data and STAR-CCM+ simulation results. In this report, we demonstrate the development, implementation, and successful validation of a multi-dimensional flow model in the SAM code, which aims to improve the simulation accuracy for complex thermal mixing and stratification phenomena. Lessons have also been learned, including that in cases where the flow fields are not well predicted by the zero-equation model, the iinput of turbulent viscosities from an external source can enhance the overall predictive capabilities needed to accurately capture complex thermal-hydraulic phenomena. Therefore, future research will be needed to further improve the code’s capabilities, such as by developing a more efficient and robust approach to capture the turbulence effects in the SAM code.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Simulation of the Charge Measurements for the SuperCDMS Soudan Experiment

Astronomical observations lead us to believe that dark matter constitutes the majority of the mass of the Universe, yet it has never been directly observed experimentally. The Super Cryogenic Dark Matter Search Experiment (SuperCDMS) aims to discover the particle nature of dark matter, by developing and using some of the most sensitive detectors ever built. The SuperCDMS detectors are capable of rejecting known particles to search for dark matter interactions, but the discrimination is a complicated analysis task. Previous analyses with SuperCDMS set world leading sensitivity limits to dark matter interactions, but the analysis techniques have not been based on simulations. Now we have developed an efficient simulations infrastructure that will help better understand the detectors, develop a better background model, and optimize the next generation dark matter searches. In this thesis we present the fully-functional simulations infrastructure to produce high quality samples a s well as results for the charge system readout simulations, and comparisons to expectations from data. We show that our simulations well-reproduce dominant features of data, bringing SuperCDMS closer to a robust simulation-based analysis method, much needed for the upcoming SuperCDMS dark matter searches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Massive all-atom analysis of 2D materials with quantum properties (Final report)

Improvements in microscopy have enabled the acquisition of data at a scale that is difficult to process manually, making automated machine learning approaches to analyzing experimental images essential. In this project, we developed and applied machine learning (ML) workflows for atomic resolution scanning transmission electron microscopy (STEM) images. This development included improving both methodology as well as generating user-friendly codes. We developed machine learning architectures which, after training, automatically identify the location and types of defects throughout a material. We used these data to produce class-averaged images of 2D atomic coordinates with up to 0.3 pm precision, uncovering the structure and oscillations of long-range strain fields around point defects in WSe 2-2x Te 2x . We also resolved a long-standing problem in this field in the training of ML models, a lack of labeled experimental data, by developing a cycle-GAN that transformed simulated-generated labeled data into labeled data indistinguishable from experiment and therefore suitable for training. This removed the remaining parts of the ML data processing workflow where human intervention was still critical and therefore a bottleneck to working at scale. Codes have been developed and released for this full machine learning workflow. ML approaches to partially automate STEM acquisition were also developed. Finally we applied ML and other advanced data processing methods to several materials science problems in two-dimensional materials, including studying the evolution of hyperuniformity with defect concentration in WSe2, understanding phase transformations in transition metal dichalcogenides during in-situ heating in the STEM, and exploring how 2D interfaces transform from twisted into aligned structures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Two-Phase Turbulence Statistics from High Fidelity Dispersed Droplet Flow Simulations in a Pressurized Water Reactor (PWR) Sub-Channel with Mixing Vanes

In the dispersed flow film boiling regime (DFFB), which exists under post-LOCA (loss-of-coolant accident) conditions in pressurized water reactors (PWRs), there is a complex interplay between droplet dynamics and turbulence in the surrounding steam. Experiments have accredited particular significance to droplet collision with the spacer-grids and mixing vane structures and their consequent positive feedback to the heat transfer recorded in the immediate downstream vicinity. Enabled by high-performance computing (HPC) systems and a massively parallel finite element-based flow solver—PHASTA (Parallel Hierarchic Adaptive Stabilized Transient Analysis)—this work presents high fidelity interface capturing, two-phase, adiabatic simulations in a PWR sub-channel with spacer grids and mixing vanes. Selected flow conditions for the simulations are informed by the experimental data found in the literature, including the steam Reynolds number and collision Weber number (Wec={40,80}), and are characteristic of the DFFB regime. Data were collected from the simulations at an unprecedented resolution, which provides detailed insights into the continuous phase turbulence statistics, highlighting the effects of the presence of droplets and the comparative effect of different Weber numbers on turbulence in the surrounding steam. Further, axial evolution of droplet dynamics was analyzed through cross-sectionally averaged quantities, including droplet volume, surface area and Sauter mean diameter (SMD). The downstream SMD values agree well with the existing empirical correlations for the selected range of Wec. The high-resolution data repository from the simulations herein is expected to be of significance to guide model development for system-level thermal hydraulic codes.

Saini, Nadish↗

Propagating sample variance uncertainties in redshift calibration: simulations, theory, and application to the COSMOS2015 data

ABSTRACT Cosmological analyses of galaxy surveys rely on knowledge of the redshift distribution of their galaxy sample. This is usually derived from a spectroscopic and/or many-band photometric calibrator survey of a small patch of sky. The uncertainties in the redshift distribution of the calibrator sample include a contribution from shot noise, or Poisson sampling errors, but, given the small volume they probe, they are dominated by sample variance introduced by large-scale structures. Redshift uncertainties have been shown to constitute one of the leading contributions to systematic uncertainties in cosmological inferences from weak lensing and galaxy clustering, and hence they must be propagated through the analyses. In this work, we study the effects of sample variance on small-area redshift surveys, from theory to simulations to the COSMOS2015 data set. We present a three-step Dirichlet method of resampling a given survey-based redshift calibration distribution to enable the propagation of both shot noise and sample variance uncertainties. The method can accommodate different levels of prior confidence on different redshift sources. This method can be applied to any calibration sample with known redshifts and phenotypes (i.e. cells in a self-organizing map, or some other way of discretizing photometric space), and provides a simple way of propagating prior redshift uncertainties into cosmological analyses. As a worked example, we apply the full scheme to the COSMOS2015 data set, for which we also present a new, principled SOM algorithm designed to handle noisy photometric data. We make available a catalogue of the resulting resamplings of the COSMOS2015 galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗

Improving Detector Systematic Uncertainties Through Data-Driven Machine Learning

Detector simulation in liquid argon time projection chambers (LArTPCs) is a constant challenge. In particular the modeling of electrons response on wires is highly nontrivial. However, new machine learning techniques exist which can be leveraged to ameliorate these concerns. We present a novel methodology to attempt to learn from cosmic muon data in the ICARUS detector how reconstructed wire signals are influenced by features of the hits such as location, particle direction, angle relative to the wire plane, etc. A model can then be generated which can apply the learned mapping to Monte Carlo events to create a more data-like simulation sample. By creating such a sample we expect to reduce the systematic uncertainties at ICARUS due to our detector modeling.

Hausner, Harry [Fermilab] (ORCID:0000000188932280)↗