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At least 1,639 records · Page 91

What Levels of Coupled Cluster Theory Are Appropriate for Transition Metal Systems? A Study Using Near-Exact Quantum Chemical Values for 3d Transition Metal Binary Compounds

Transition metal compounds are traditionally considered to be challenging for standard quantum chemistry approximations like coupled cluster (CC) theory, which are usually employed to validate lower level methods like density functional theory (DFT). To explore this issue, we present a database of bond dissociation energies (BDEs) for 74 spin states of 69 diatomic species containing a 3d transition metal atom and a main group element, in the moderately sized def2-SVP basis. The presented BDEs appear to have an (estimated) 3σ error less than 1 kJ/mol relative to the exact solutions to the nonrelativistic Born-Oppenheimer Hamiltonian. These benchmark values were used to assess the performance of a wide range of standard single reference CC models, as the results should be beneficial for understanding the limitations of these models for transition metal systems. We find that interactions between metals and monovalent ligands like hydride and fluoride are well described by CCSDT. Similarly, CCSDTQ appears to be adequate for bonds between metals and nominally divalent ligands like oxide and sulfide. However, interactions with polyvalent ligands like nitride and carbide are more challenging, with even CCSDTQ(P)Λ yielding errors on the scale of a few kJ/mol. We also find that many perturbative and iterative approximations to higher order terms either yield disappointing results or actually worsen the performance relative to the baseline low level CC method, indicating that complexity does not always guarantee accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Stillwater Hybrid Geo-Solar Power Plant Optimization Analyses

The Stillwater Power Plant is the first hybrid plant in the world able to bring together a medium-enthalpy geothermal unit with solar thermal and solar photovoltaic systems. Solar field and power plant models have been developed to predict the performance of the Stillwater geothermal / solar-thermal hybrid power plant. The models have been validated using operational data from the Stillwater plant. A preliminary effort to optimize performance of the Stillwater hybrid plant using optical characterization of the solar field has been completed. The Stillwater solar field optical characterization involved measurement of mirror reflectance, mirror slope error, and receiver position error. The measurements indicate that the solar field may generate 9% less energy than the design value if an appropriate tracking offset is not employed. A perfect tracking offset algorithm may be able to boost the solar field performance by about 15%. The validated Stillwater hybrid plant models were used to evaluate hybrid plant operating strategies including turbine IGV position optimization, ACC fan speed and turbine IGV position optimization, turbine inlet entropy control using optimization of multiple process variables, and mixed working fluid substitution. The hybrid plant models predict that each of these operating strategies could increase net power generation relative to the baseline Stillwater hybrid plant operations.

15 GEOTHERMAL ENERGY↗

Superdense coding interleaved with forward error correction

Superdense coding promises increased classical capacity and communication security but this advantage may be undermined by noise in the quantum channel. We present a numerical study of how forward error correction (FEC) applied to the encoded classical message can be used to mitigate against quantum channel noise. By studying the bit error rate under different FEC codes, we identify the unique role that burst errors play in superdense coding, and we show how these can be mitigated against by interleaving the FEC codewords prior to transmission. As a result, we conclude that classical FEC with interleaving is a useful method to improve the performance in near-term demonstrations of superdense coding.

97 MATHEMATICS AND COMPUTING↗

Resilience Design Patterns - A Structured Approach to Resilience at Extreme Scale (version 1.0)

Reliability is a serious concern for future extreme-scale high-performance computing (HPC) systems. Projections based on the current generation of HPC systems and technology roadmaps suggest that very high fault rates in future systems. The errors resulting from these faults will propagate and generate various kinds of failures, which may result in outcomes ranging from result corruptions to catastrophic application crashes. Practical limits on power consumption in HPC systems will require future systems to embrace innovative architectures, increasing the levels of hardware and software complexities. The resilience challenge for extreme-scale HPC systems requires management of various hardware and software technologies that are capable of handling a broad set of fault models at accelerated fault rates. These techniques must seek to improve resilience at reasonable overheads to power consumption and performance. While the HPC community has developed various solutions, application-level as well as system-based solutions, the solution space of HPC resilience techniques remains fragmented. There are no formal methods and metrics to investigate and evaluate resilience holistically in HPC systems that consider impact scope, handling coverage, and performance & power eciency across the system stack. Additionally, few of the current approaches are portable to newer architectures and software ecosystems, which are expected to be deployed on future systems. In this document, we develop a structured approach to the management of HPC resilience based on the concept of resilience-based design patterns. A design pattern is a general repeatable solution to a commonly occurring problem. We identify the commonly occurring problems and solutions used to deal with faults, errors and failures in HPC systems. The catalog of resilience design patterns provides designers with reusable design elements. We define a design framework that enhances our understanding of the important constraints and opportunities for solutions deployed at various layers of the system stack. The framework may be used to establish mechanisms and interfaces to coordinate flexible fault management across hardware and software components. The framework also enables optimization of the cost-benefit trade-os among performance, resilience, and power consumption. The overall goal of this work is to enable a systematic methodology for the design and evaluation of resilience technologies in extreme-scale HPC systems that keep scientific applications running to a correct solution in a timely and cost-ecient manner in spite of frequent faults, errors, and failures of various types.

97 MATHEMATICS AND COMPUTING↗

Predicting the volumes of crystals

New crystal structures are frequently derived by performing ionic substitutions on known crystal structures. These derived structures are then used in further experimental analysis, or as the initial guess for structural optimization in electronic structure calculations, both of which usually require a reasonable guess of the lattice parameters. In this work, we propose two lattice prediction schemes to improve the initial guess of a candidate crystal structure. The first scheme relies on a one-to-one mapping of species in the candidate crystal structure to a known crystal structure, while the second scheme relies on data-mined minimum atom pair distances to predict the crystal volume of the candidate crystal structure and does not require a reference structure. We demonstrate that the two schemes can effectively predict the volumes within mean absolute errors (MAE) as low as 3.8% and 8.2%. Here we also discuss the various factors that may impact the performance of the schemes. Implementations for both schemes are available in the open-source pymatgen software.

36 MATERIALS SCIENCE↗

Diagnosing Model Errors in Simulation of Solar Radiation on Inclined Surfaces

Transposition models have been widely used in the solar energy industry to simulate solar radiation on inclined PV panels. Following numerous studies comparing the performance of transposition models, this paper aims to understand the quantitative uncertainty in the state-of-the-art transposition models and the sources leading to the uncertainty. Our results show significant differences between two highly used isotropic transposition models with one substantially underestimating the diffuse plane-of-array (POA) irradiances when diffuse radiation is perfectly isotropic. In the empirical transposition models, the selection of empirical coefficients and land surface albedo can both result in uncertainty in the output. This study can be used as a guide for future development of physics-based transposition models.

14 SOLAR ENERGY↗

Dynamic Approach to Dependency Analysis in Human Reliability Analysis: Application in a Stream Generator Tube Rupture Scenario

Dependency analysis in human reliability analysis (HRA) is a method of adjusting the failure probability of a given action by considering the impact of the action preceding it. It plays a role in reasonably accounting for human actions in the context of probabilistic safety assessments (PSAs), preventing PSA results from being estimated too optimistically based on the HRA results. Nevertheless, the existing dependency methods present a couple of challenges in that the quantification approaches rarely explain the adjustment of human error probabilities (HEPs). For this reason, the authors’ previous research has pointed out challenges of the existing dependency approaches and conceptually, theoretically proposed a performance shaping factor (PSF)-based dynamic dependency analysis method for HRA in order to complement the existing dependency methods. The current paper explores the latest version of the method and guidance for applying it to a steam generator tube rupture (SGTR) scenario.

99 GENERAL AND MISCELLANEOUS↗

SU-E-J-01: 3D Fluoroscopic Image Estimation From Patient-Specific 4DCBCT-Based Motion Models

Purpose: 3D motion modeling derived from 4DCT images, taken days or weeks before treatment, cannot reliably represent patient anatomy on the day of treatment. We develop a method to generate motion models based on 4DCBCT acquired at the time of treatment, and apply the model to estimate 3D time-varying images (referred to as 3D fluoroscopic images). Methods: Motion models are derived through deformable registration between each 4DCBCT phase, and principal component analysis (PCA) on the resulting displacement vector fields. 3D fluoroscopic images are estimated based on cone-beam projections simulating kV treatment imaging. PCA coefficients are optimized iteratively through comparison of these cone-beam projections and projections estimated based on the motion model. Digital phantoms reproducing ten patient motion trajectories, and a physical phantom with regular and irregular motion derived from measured patient trajectories, are used to evaluate the method in terms of tumor localization, and the global voxel intensity difference compared to ground truth. Results: Experiments included: 1) assuming no anatomic or positioning changes between 4DCT and treatment time; and 2) simulating positioning and tumor baseline shifts at the time of treatment compared to 4DCT acquisition. 4DCBCT were reconstructed from the anatomy as seen at treatment time. In case 1) the tumor localization error and the intensity differences in ten patient were smaller using 4DCT-based motion model, possible due to superior image quality. In case 2) the tumor localization error and intensity differences were 2.85 and 0.15 respectively, using 4DCT-based motion models, and 1.17 and 0.10 using 4DCBCT-based models. 4DCBCT performed better due to its ability to reproduce daily anatomical changes. Conclusion: The study showed an advantage of 4DCBCT-based motion models in the context of 3D fluoroscopic images estimation. Positioning and tumor baseline shift uncertainties were mitigated by the 4DCBCT-based motion models, while they caused errors when using 4DCT-based motion models. Partially funded by Varian research grant.

60 APPLIED LIFE SCIENCES↗

Impact of Measured Spectrum Variation on Solar Photovoltaic Efficiencies Worldwide

In photovoltaic power ratings, a single solar spectrum, AM1.5, is the de facto standard for record laboratory efficiencies, commercial module specifications, and performance ratios of solar power plants. More detailed energy analysis that accounts for local spectral irradiance, along with temperature and broadband irradiance, reduces forecast errors to expand the grid utility of solar energy. Here, ground-level measurements of spectral irradiance collected worldwide have been pooled to provide a sampling of geographic, seasonal, and diurnal variation. Applied to nine solar cell types, the resulting divergence in solar cell efficiencies illustrates that a single spectrum is insufficient for comparisons of cells with different spectral responses. Cells with two or more junctions tend to have efficiencies below that under the standard spectrum. Silicon exhibits the least spectral sensitivity: relative weekly site variation ranges from 1% in Lima, Peru to 14% in Edmonton, Canada.

energy yield↗

Solid State Transformer Controls for Mitigation of E3a High-Altitude Electromagnetic Pulse Insults

This paper explores the use of a solid state transformer (SST) to mitigate the 𝐸 3𝐴 component of a high-altitude electromagnetic pulse (HEMP) insult using external energy storage optimal control techniques. In lieu of conventional passive blocking devices or feedback-controlled energy storage devices, a novel implementation of Hamiltonian error tracking is utilized to develop a feedback control law for the variable converter ratio in an SST. The findings of the simulations performed in this paper suggest that additional energy storage is not necessary to protect an individual load from a HEMP insult. The simulations performed examine the response of a single-phase SST connected to a single voltage source on a long transmission line on the one side and a single linear resistor on the other. The control law is specifically developed for the late-time, low-frequency portion of a HEMP insult, namely the 𝐸 3𝐴 components. The Hamiltonian error-based converter ratio control law is compared with nonlinear optimal feedforward controls to show that the HSSPFC is an external energy storage optimal controller.

HEMP mitigation↗

Benchmark Calculations of Energetic Properties of Groups 4 and 6 Transition Metal Oxide Nanoclusters Including Comparison to Density Functional Theory

The heats of formation and the normalized clustering energies (NCEs) for the group 4 and group 6 transition metal oxide (TMO) trimers and tetramers have been calculated by the Feller-Peterson-Dixon (FPD) method. The heats of formation predicted by the FPD method do not differ much from those previously derived from the NCEs at the CCSD(T)/aT level except for the CrO3 nanoclusters. New and improved heats of formation for Cr3O9 and Cr4O12 were obtained using PW91 orbitals instead of Hartree-Fock (HF) orbitals. Diffuse functions are necessary to predict accurate heats of formation. The fluoride affinities (FAs) are calculated with the CCSD(T) method. The relative energies (REs) of different isomers, NCEs, electron affinities (EAs), and FAs of (MO2)n ( M = Ti, Zr, Hf, n = 1 – 4 ) and (MO3)n ( M = Cr, Mo, W, n = 1 – 3) clusters have been benchmarked with 55 exchange-correlation DFT functionals including both pure and hybrid types. The absolute errors of the DFT results are mostly less than ±10 kcal/mol for the NCEs and the EAs, and less than ±15 kcal/mol for the FAs. Hybrid functionals usually perform better than the pure functionals for the REs and NCEs. The performance of the two types of functionals in predicting EAs and FAs is comparable. The B1B95 and PBE1PBE functionals provide reliable energetic properties for most isomers. Long range corrected pure functionals usually give poor FAs. The standard deviation of the absolute error is always close to the mean errors and the probability distributions of the DFT errors are often not Gaussian (normal). The breadth of the distribution of errors and the maximum probability are dependent on the energy property and the isomer.

Environmental Molecular Sciences Laboratory↗

Challenges for unsupervised anomaly detection in particle physics

Anomaly detection relies on designing a score to determine whether a particular event is uncharacteristic of a given background distribution. One way to define a score is to use autoencoders, which rely on the ability to reconstruct certain types of data (background) but not others (signals). In this paper, we study some challenges associated with variational autoencoders, such as the dependence on hyperparameters and the metric used, in the context of anomalous signal (top and W) jets in a QCD background. We find that the hyperparameter choices strongly affect the network performance and that the optimal parameters for one signal are non-optimal for another. In exploring the networks, we uncover a connection between the latent space of a variational autoencoder trained using mean-squared-error and the optimal transport distances within the dataset. We then show that optimal transport distances to representative events in the background dataset can be used directly for anomaly detection, with performance comparable to the autoencoders. Whether using autoencoders or optimal transport distances for anomaly detection, we find that the choices that best represent the background are not necessarily best for signal identification. These challenges with unsupervised anomaly detection bolster the case for additional exploration of semi-supervised or alternative approaches.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Online and Offline Data Quality Monitoring for the Mu2e Calorimeter

This thesis presents the design, implementation, and validation of a calorimeter Data Quality Monitoring (DQM) toolchain for the Mu2e experiment at Fermilab. Mu2e searches for charged lepton flavor violation via coherent muon-to-electron conversion in the field of an aluminum nucleus, $\mu^- Al \rightarrow e^-Al$, a process whose observation would constitute clear evidence of physics beyond the Standard Model. Achieving target sensitivity requires stringent control of detector performance and data integrity during acquisition, as subtle issues in readout configuration, data formatting, or electronics behavior can compromise reconstruction and bias downstream analyzes. To address these challenges, this work develops a multi-layer DQM approach spanning both raw data validation and reconstructed digi-level diagnostics. At the low level, a fragment analysis component performs word- and bit-field decoding of calorimeter readout blocks, enabling sanity checks of the expected structure and producing detailed error and integrity statistics useful for commissioning and troubleshooting. At the digi level, the CaloDigiDQM analyzer is implemented within the art framework and transforms each CaloDigiCollection into a structured hierarchy of ROOT histograms designed for fast drill-down diagnostics. The module generates coherent monitoring views at global, disk, board, and channel granularity, including occupancy, waveform-derived features (baseline, RMS, peak amplitude and position), and left-right sensor consistency metrics. Detector-aware channel-to-electronics mapping is performed through the conditions system (CaloDAQMap), ensuring that diagnostics remain aligned with hardware identifiers used in operations. For end-to-end testing without reliance on live DAQ data, a synthetic CaloDigi producer is developed to generate realistic waveforms with controlled noise and pulse shapes. The resulting system supports both offline ROOT-file production and online operation, including optional histogram streaming through otsdaq via ots::HistoSender. This toolchain provides a practical and scalable foundation for calorimeter commissioning and stable data collection, enabling early detection of anomalies and reducing operational risk for Mu2e.

Vakulenko, Mark [Drew U.] (ORCID:0009000276197818)↗

ADC evaluation boards design and test framework for LCLS-II precision receiver

In the low-level RF control field, ADC acquisition accuracy and noise set the boundary of our control ability, making it important to develop low-noise acquisition systems. From the design to test stage, all the noise terms should be understood and characterized. The specific need addressed here is the precision acquisition system for the second Linac Coherent Light Source (LCLS-II), led by SLAC National Accelerator Laboratory. Test circuit boards for the LTC2174 and AD9268 ADCs are designed and fabricated by LBNL. An ADC test framework based on FPGA evaluation board to assess performance has been developed. The ADC test framework includes both DSP (Digital Signal Processing) firmware and processing software. It is useful for low-level RF control and other synchronization projects. Investigating the clock jitter between two channels give us an understanding of that noise source. Working with the test framework, the raw ADC data are transferred to a computer through a Gigabit Ethernet interface. Then short-term error signal can be calculated based on a sine wave fit. By changing low-pass filter bandwidth, relative long-term performance can also be obtained. Amplitude jitter and differential phase jitter are the key issues for ADCs. This paper will report the test results for LTC2174 and AD9268 chips. The integral amplitude jitter is smaller than 0.003 %, and the integral phase noise is smaller than 0.0015° (measured at 47 MHz RF, 100 MHz clock, bandwidth 1 Hz to 100 kHz) for both ADC chips.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Does the Use of a Checklist Help Medical Students in the Detection of Abnormalities on a Chest Radiograph?

The interpretation of chest radiographs is a complex task that is prone to diagnostic error, especially for medical students. The aim of this study is to investigate the extent to which medical students benefit from the use of a checklist regarding the detection of abnormalities on a chest radiograph. We developed a checklist based on literature and interviews with experienced thorax radiologists. Forty medical students in the clinical phase assessed 18 chest radiographs during a computer test, either with (n = 20) or without (n = 20) the checklist. We measured performance and asked participants for feedback using a survey. Participants that used a checklist detected more abnormalities on images with multiple abnormalities (M = 50.1%) than participants that could not use a checklist (M = 41.9%), p = 0.04. The post-experimental survey shows that on average, participants considered the checklist helpful (M = 3.25 on a five-point scale), but also time consuming (M = 3.30 on a five-point scale). In conclusion, a checklist can help medical students to detect abnormalities in chest radiographs. Moreover, students tend to appreciate the use of a checklist as a helpful tool during the interpretation of a chest radiograph. Therefore, a checklist is a potentially important tool to improve radiology education in the medical curriculum.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

POD-based constrained sensor placement and field reconstruction from noisy wind measurements: A perturbation study

Sensor placement at the extrema of Proper Orthogonal Decomposition (POD) is efficient and leads to accurate reconstruction of the wind field from a limited number of measure- ments. In this paper we extend this approach of sensor placement and take into account measurement errors and detect possible malfunctioning sensors. We use the 48 hourly spa- tial wind field simulation data sets simulated using the Weather Research an Forecasting (WRF) model applied to the Maine Bay to evaluate the performances of our methods. Specifically, we use an exclusion disk strategy to distribute sensors when the extrema of POD modes are close. It turns out that this strategy can also reduce the error of recon- struction from noise measurements. Also, by a cross-validation technique, we successfully locate the malfunctioning sensors.

54 ENVIRONMENTAL SCIENCES↗

On Neural Architectures for Astronomical Time-series Classification with Application to Variable Stars

Despite the utility of neural networks (NNs) for astronomical time-series classification, the proliferation of learning architectures applied to diverse data sets has thus far hampered a direct intercomparison of different approaches. Here we perform the first comprehensive study of variants of NN-based learning and inference for astronomical time series, aiming to provide the community with an overview on relative performance and, hopefully, a set of best-in-class choices for practical implementations. In both supervised and self-supervised contexts, we study the effects of different time-series-compatible layer choices, namely the dilated temporal convolutional neural network (dTCNs), long-short term memory NNs, gated recurrent units and temporal convolutional NNs (tCNNs). Additionally, we also study the efficacy and performance of encoder-decoder (i.e., autoencoder) networks compared to direct classification networks, different pathways to include auxiliary (non-time-series) metadata, and different approaches to incorporate multi-passband data (i.e., multiple time series per source). Performance—applied to a sample of 17,604 variable stars (VSs) from the MAssive Compact Halo Objects (MACHO) survey across 10 imbalanced classes—is measured in training convergence time, classification accuracy, reconstruction error, and generated latent variables. We find that networks with recurrent NNs generally outperform dTCNs and, in many scenarios, yield to similar accuracy as tCNNs. In learning time and memory requirements, convolution-based layers perform better. We conclude by discussing the advantages and limitations of deep architectures for VS classification, with a particular eye toward next-generation surveys such as the Legacy Survey of Space and Time, the Roman Space Telescope, and Zwicky Transient Facility.

79 ASTRONOMY AND ASTROPHYSICS↗

CBETA 4-pass Orbit Correction and Tolerance Study (CBETA Machine Note #037)

The main goal of this study is to find the tolerance of the CBETA FFAG lattice subjected to various errors. Common error sources include non-ideal magnet positioning and alignment, undesired multipole field strengths, and BPM offsets. In order to fix the beam trajectories (orbits) affected by the errors, dipole kickers have been allocated throughout the FFAG beamline to serve as correctors. Since there are four coexisting orbits in the FFAG, the correction scheme must aim to correct all four of them simultaneously. The simulation is performed using Bmad, developed by Cornell University to model relativistic beam dynamics in customized accelerator lattices. The core of orbit correction is the singular value decomposition (SVD) optimization. As the error magnitudes increase, the beam size and emittance after correction could undesirably increase, and our goal is to find the "individual limit" of each error type beyond which the increases become unacceptable. Typically this limit is reached before the orbits become un-correctable. Similarly we can also find the "combined limit" of various errors. Both limits will be properly defined in the later section.

43 PARTICLE ACCELERATORS↗