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At least 55 records · Page 3

Testing of the 2.6 GHz SRF Cavity Tuner for the Dark Photon Experiment at 2 K

At FNAL two single cell 2.6 GHz SRF cavities are being used to search for dark photons, the experiment can be conducted at 2 K or in a dilution refrigerator. Precise frequency tuning is required for these two cavities so they can be matched in frequency. A cooling capacity constraint on the dilution refrigerator only allows piezo actuators to be part of the design of the 2.6 GHz cavity tuner. The tuner is equipped with three encapsulated piezos that deliver long and short-range frequency tuning. Modifications were implemented on the first tuner design due to the low forces on the piezos caused by the cavity. Three brass rods with Belleville washers were added to the design to increase the overall force on the piezos. The testing results at 2 K are presented with the original design tuner and with the modification.

43 PARTICLE ACCELERATORS↗

Interpretation of HFTS 2 Microseismic Data Using Bedding-Plane-Slip Mechanism

Summary The objective of this study is to understand how microseismic events are generated during hydraulic fracturing, as well as the role of geomechanical conditions (i.e., stress and mechanical stratigraphy) in this process. In the industry, microseismic event clouds have been generally used as an “outer boundary” of the “stimulated reservoir volume” (SRV). However, by comparing with other surveillance data (low-frequency distributed acoustic sensing, or LF-DAS, strain) in the Hydraulic Fracturing Test Site (HFTS) 2 experiment, we show that this assumption is fundamentally flawed. The HFTS 2 data have three unique observations that have not been commonly observed in other datasets: (1) Due to the influence of offset pad depletion, microseismic data show that hydraulic fractures from the child well can propagate over 3,000 ft into the depleted low-stress zone. (2) By comparing microseismic and horizontal fiber LF-DAS strain data, we observe that the microseismic event cloud does not necessarily reflect the created hydraulic fracture volume. Particularly, the extent of microseismic event clouds near heel stages is much shorter than what is shown with LF-DAS strain data. (3) Microseismic event magnitudes are larger in the depleted regions. Through geomechanical analysis, we demonstrate that the “bedding-plane-slip” model is likely the mechanism for microseismic generation during hydraulic fracturing. This model successfully explains the above field observations from the HFTS 2 experiment. We also provide a quantitative relationship connecting the microseismic event magnitude with fracture width increment and layer mechanical property contrast.

Engineering↗

PNNL's Characterization Summary for MP-2 Experiment

Characterization of as-fabricated fuel was performed at Pacific Northwest National Laboratory (PNNL) in accordance with the characterization plan for the fabrication of U 10Mo plate fuel for the U.S. High Performance Research Reactor conversion program’s Fuel Fabrication Pillar (INL 2021). Similar characterization work is also being performed at Idaho National Laboratory to provide a detailed understanding of the as-fabricated foils that would be irradiated in the Mini-Plate 2 (MP 2) experiment. Under the MP 2 characterization plan, foils are studied that have different fabrication parameters (such as rolling condition, rolling thickness reduction, co-rolling with Zr layers). Similar samples from master foils were sent to both the organizations, so that the testing and analysis can be done independently using similar equipment and standardized measurement and analysis procedures. A final, consolidated report will be prepared based on this work and will summarize all the information obtained from the two laboratories. The MP 2 experiment will provide an opportunity to understand the effects of processing conditions on the final fuel microstructure, to compare results obtained independently, and achieve a two-way validation. In Fiscal Year 2022, PNNL received five MP 2 cast (PD STD2) samples to examine the foils’ chemistry and microstructure. For each cast sample, PNNL received samples from three different locations. PNNL also received and characterized 24 U 10Mo foil samples, by sectioning four pieces/specimens from each foil, in accordance with the MP 2 Characterization Plan (INL 2021). These 24 samples consist of four types of foils from BWX Technologies: 0.047 in. thick hot-rolled and annealed samples with Zr layers; 0.025 in. thick cold-rolled and annealed samples with Zr layers; 0.0105 in. thick cold-rolled and annealed samples with Zr layers. Along with these, PNNL also received four plates with Zr layers that were 0.025 in. and 0.0105 in. thick. This report describes the results of PNNL’s MP 2 foil characterization. Microstructure, Mo homogeneity, carbide fraction and morphology, U 10Mo foil thickness, and Zr thickness were evaluated in both the longitudinal and transverse directions for all the foils of the three different thicknesses.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Effects of machine learning errors on human decision-making: manipulations of model accuracy, error types, and error importance

Abstract This study addressed the cognitive impacts of providing correct and incorrect machine learning (ML) outputs in support of an object detection task. The study consisted of five experiments that manipulated the accuracy and importance of mock ML outputs. In each of the experiments, participants were given the T and L task with T-shaped targets and L-shaped distractors. They were tasked with categorizing each image as target present or target absent. In Experiment 1, they performed this task without the aid of ML outputs. In Experiments 2–5, they were shown images with bounding boxes, representing the output of an ML model. The outputs could be correct (hits and correct rejections), or they could be erroneous (false alarms and misses). Experiment 2 manipulated the overall accuracy of these mock ML outputs. Experiment 3 manipulated the proportion of different types of errors. Experiments 4 and 5 manipulated the importance of specific types of stimuli or model errors, as well as the framing of the task in terms of human or model performance. These experiments showed that model misses were consistently harder for participants to detect than model false alarms. In general, as the model’s performance increased, human performance increased as well, but in many cases the participants were more likely to overlook model errors when the model had high accuracy overall. Warning participants to be on the lookout for specific types of model errors had very little impact on their performance. Overall, our results emphasize the importance of considering human cognition when determining what level of model performance and types of model errors are acceptable for a given task.

97 MATHEMATICS AND COMPUTING↗

COMPASS-FME Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) Experiment Level 2 Sensor Data v2-1

This is the version v2-1 Level 2 (L2) data release for COMPASS-FME environmental sensors located at our Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental site. This manipulative, ecosystem-scale TEMPEST experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments. Level 2 (L2) data consist of sensor observations from the COMPASS-FME synoptic sites, TEMPEST, and DELUGE. Compared to the L1 data, these are more consistent (always 15-minute timestamps for the entire year); better QA/QC’d (out of bounds, out of service, and extreme outlier values are removed); and more complete, with a gap-filled time series available alongside the main observations, and additional derived (calculated) variables. L2 data are intended to be rapidly and easily usable in analyses and simulations. However, algorithmic outlier identification always carries the risk of removing valid data, and Level 1 data may be more suitable for analyses that focus on variability or extreme events. This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding variable-specific Parquet (a high performance, space efficient format; see https://parquet.apache.org) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, detailed flood times, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are reported every 15 minutes. Please see v2-1 TEMPEST L2 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning. Data files are in Apache Parquet, a high performance, space efficient format for tabular data. These files can be read using R's `arrow` package (https://arrow.apache.org/docs/r/), with similar tools available in other languages. The TEMPEST flood events occurred on the following dates. They lasted for ~10 hours each day and delivered ~80,000 gallons to each plot; many data streams are available at 1 or 5 minute frequency during these periods. * Tests: Aug 25 (fresh plot) and Sep 9 (salt plot), 2021 * TEMPEST 1: June 22, 2022 * TEMPEST 2: June 6-7, 2023 * TEMPEST 3: June 11-13, 2024

EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATU↗

Increases in Future AR Count and Size: Overview of the ARTMIP Tier 2 CMIP5/6 Experiment

Abstract The Atmospheric River (AR) Tracking Method Intercomparison Project (ARTMIP) is a community effort to systematically assess how the uncertainties from AR detectors (ARDTs) impact our scientific understanding of ARs. This study describes the ARTMIP Tier 2 experimental design and initial results using the Coupled Model Intercomparison Project (CMIP) Phases 5 and 6 multi‐model ensembles. We show that AR statistics from a given ARDT in CMIP5/6 historical simulations compare remarkably well with the MERRA‐2 reanalysis. In CMIP5/6 future simulations, most ARDTs project a global increase in AR frequency, counts, and sizes, especially along the western coastlines of the Pacific and Atlantic oceans. We find that the choice of ARDT is the dominant contributor to the uncertainty in projected AR frequency when compared with model choice. These results imply that new projects investigating future changes in ARs should explicitly consider ARDT uncertainty as a core part of the experimental design.

54 ENVIRONMENTAL SCIENCES↗

Sea Ice Rheology Experiment (SIREx): 2. Evaluating Linear Kinematic Features in High-Resolution Sea Ice Simulations

Simulating sea ice drift and deformation in the Arctic Ocean is still a challenge because of the multiscale interaction of sea ice floes that compose the Arctic Sea ice cover. The Sea Ice Rheology Experiment (SIREx) is a model intercomparison project of the Forum of Arctic Modeling and Observational Synthesis (FAMOS). In SIREx, skill metrics are designed to evaluate different recently suggested approaches for modeling linear kinematic features (LKFs) to provide guidance for modeling small-scale deformation. These LKFs are narrow bands of localized deformation that can be observed in satellite images and also form in high resolution sea ice simulations. In this contribution, spatial and temporal properties of LKFs are assessed in 36 simulations of state-of-the-art sea ice models and compared to deformation features derived from the RADARSAT Geophysical Processor System. All simulations produce LKFs, but only very few models realistically simulate at least some statistics of LKF properties such as densities, lengths, or growth rates. All SIREx models overestimate the angle of fracture between conjugate pairs of LKFs and LKF lifetimes pointing to inaccurate model physics. The temporal and spatial resolution of a simulation and the spatial resolution of atmospheric boundary condition affect simulated LKFs as much as the model's sea ice rheology and numerics. Only in very high resolution simulations (≤2 km) the concentration and thickness anomalies along LKFs are large enough to affect air-ice-ocean interaction processes.

54 ENVIRONMENTAL SCIENCES↗

Data for "Genetics of flooding tolerance in an F2 Miscanthus sacchariflorus ssp. lutarioriparius × M. sinensis population"

This dataset contains all data and supplementary materials from "Genetics of flooding tolerance in an F2 Miscanthus sacchariflorus ssp. lutarioriparius × M. sinensis population". 1. The dataset S1 table contains the raw phenotypic data collected during the experiment. 2. The dataset S2 table contains the LSmean values for the 24 traits studied. 3. The dataset S3 table contains the TASSEL GBSv2 map, marker information, and genotype data used for mapping. 4. The dataset S4 table contains information on candidate genes found in each of the QTL intervals. 5. The dataset S5 table contains the GO annotations and KEGG enrichment analyses for those candidate genes. 6. The dataset S6 table contains information on the sequences used to classify AP2 ERF transcription factors. 7. The dataset S7 table contains information on AP2 ERF orthologs between Miscanthus and rice based on synteny. 8. Supplementary file 1 contains the ANOVA results using the raw phenotypic data collected from protocol "A". 9. Supplementary file 2 contains the ANOVA results using the raw phenotypic data collected from protocol "B". 10. Supplementary file 3 contains notes on the comparison of SNP calling methods. 11. Supplementary file 4 is a script for analyzing candidate genes found in QTL intervals.

Miscanthus, flood, partial submergence, complete s↗

Towards understanding the robust strengthening of ENSO and more frequent extreme El Niño events in CMIP6 global warming simulations

The El Niño Southern Oscillation (ENSO) has profound impacts on weather patterns across the globe, yet there is no consensus on its response to global warming. Several modelling studies find a stronger ENSO in global warming scenarios, while other studies suggest ENSO weakening. Using a broad range of models from the Coupled Model Intercomparison Project phase 6 (CMIP6) and four types of warming experiments, here we show that the majority of the models predict a stronger ENSO by century-end in Shared Social Pathway (SSP) experiments, and in idealized 1pctCO 2 and abrupt 4xCO 2 experiments. Several models, however, do predict no change or ENSO weakening, especially in the idealized experiments. Critically, the strongest forcing (abrupt-4xCO 2 ) does not induce the strongest ENSO response, while differences between the models are much greater than those between warming scenarios. For the long-term response (over 1000 years) the models disagree even on the sign of change. Furthermore, changes in ENSO sea surface temperature (SST) variability are only modestly correlated with the tropical Pacific mean state change. The highest correlation for ENSO SST amplitude is found with the mean zonal SST gradient in the SSP5-8.5 experiment (R = –0.58). In contrast, changes in ENSO rainfall variability correlate well with changes in the mean state, as well as with changes in ENSO SST variability. When evaluating the Bjerknes Stability Index for a subset of models, we find that it is not a reliable predictor of ENSO strengthening, as this index tends to predict greater stability with warming. We argue that the enhanced ENSO stability is offset by increases in atmospheric noise or/and potential nonlinear effects. However, a robust inter-model mechanism that can explain a stronger ENSO simulated with global warming is still lacking. Therefore, caution should be exercised when considering ENSO changes based on a single model or warming scenario.

54 ENVIRONMENTAL SCIENCES↗

Measurement of the muon anomalous precession frequency in Runs 4, 5, and 6 of the Muon g-2 experiment at Fermilab

The Fermilab E989 Muon g − 2 experiment measures the muon’s anomalous magnetic moment to a precision of 127 parts per billion, as reported in June 2025. The value is proportional to the difference between the muon’s cyclotron frequency and the spin precession frequency in the presence of a uniform magnetic field, for muons contained within the g − 2 storage ring. Spin precession frequency is extracted from the time distribution of the muon’s decay positrons recorded by 24 electromagnetic calorimeters positioned around the inner circumference of the storage ring. The anomalous precession frequency is one of the primary experimental inputs necessary to estimate the anomalous magnetic moment, the other being the measurement of the magnetic field. This dissertation details the anomalous precession frequency extraction, including reconstruction, time-distribution fitting, and treatment of systematic uncertainties for the final three data-collection runs: Run-4, Run-5, and Run-6. This data represents a fourfold increase in statistics over the previous analysis release, halving the statistical uncertainty. The residual slow term from previous analyses is now well understood and documented in a systematic treatment. As of the writing of this dissertation, the theoretical prediction for the SM estimate of the muon’s anomalous magnetic moment is under debate, with two competing prediction methods, so a definitive comparison with theory is not available. The results submitted for experimental release use the kernel-ratio asymmetry method, contributing 115 parts per billion to the statistical uncertainty and 34 parts per billion to the systematic uncertainty. When combined with the previous analyses in earlier data runs, this thereby improves the measurement beyond the experimental goal and sets the world’s most precise measurement of the muon’s anomalous magnetic moment.

Israel, Scott Nathan [Boston U.]↗

Latest results of the Muon g-2 experiment at Fermilab

The muon magnetic anomaly, aμ = g - 2/ 2, is a low-energy observable which can be both measured and computed to high precision, making it a sensitive test of the Standard Model and a probe for new physics. The Muon g − 2 experiment at Fermilab aims to measure aμ with a final accuracy of 140 parts per billion (ppb). The experiment is based on the measurement of the muon spin anomalous precession frequency, ωa, in a magnetic field. The first result of the experiment, based on the 2018 data-taking campaign, was published in 2021 and it confirmed the previous result obtained at Brookhaven National Laboratory with a similar sensitivity of 460 ppb. In this proceeding, the result based on the 2019 and 2020 datasets is presented and the improvement in the accuracy with respect to the 2018 dataset are discussed.

Sorbara, Matteo [INFN, Rome2; Rome U., Tor Vergata↗

Flavor-changing light bosons with accidental longevity

We consider a model with a complex scalar field that couples to (e, μ) or (μ, τ) within the “longevity” window: [|m l 1 – m l 2 |, m l 1 + m l 2 ] in which l 1 and l 2 are the two different charged leptons. Within such a mass window, even a relatively large coupling (e.g. of the size commensurate with the current accuracy/discrepancy in the muon g – 2 experiment) leads to long lifetimes and macroscopic propagation distance between production and decay points. We propose to exploit several existing neutrino experiments and one future experiment to probe the parameter space of this model. For the μ – e sector, we exploit the muonium decay branching ratio and the production and decay sequence at the LSND experiment, excluding the parametric region suggested by g μ – 2 anomaly. For the τ – μ sector, we analyze three main production mechanisms of scalars at beam dump experiments: the Drell-Yan process, the heavy meson decay, and the muon scattering. We explore the constraints from the past CHARM and NuTeV experiments, and evaluate sensitivity for the proposed beam dump experiment, SHiP. The latter can thoroughly probe the parameter space relevant for the g μ – 2 anomaly.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Load Control for Frequency Response - A Literature Review

As electricity grids employ greater fractions of renewable energy, which introduce additional variability and uncertainty in the net load, balancing electrical load and generation becomes more challenging. This paper reviews the literature documenting physical simulations and real systems that employ load control (LC) for frequency response and other grid services, which balance net load on the grid and prevent unwanted frequency excursions. Apart from academic and simulation studies, few sources exist on large-scale laboratory hardware testing or actual real-world systems that employ LC for frequency response, and we review them here. Four types of systems that we consider are: 1) Laboratory-based LC experiments, 2) Isolated microgrids that employ LC, 3) Larger grids that employ LC and 4) vehicle-to-grid (V2G) technology, using electric vehicles (EVs). In general, these systems have successfully used LC to meet their objectives, which are often keeping grid frequency within a required band. However, LC struggled to balance grid frequency in an isolated system powered by a single wind turbine, and V2G technology requires refinement in communication and control to provide optimal regulation that adheres to industry standards. As LC grows in the energy industry, we have three main recommendations: 1) encouraging system operators who use LC to publish system characteristics and lessons learned; 2) transitioning more LC theoretical/simulated systems to physical experiments, and physical experiments to real-world pilot systems; 3) demonstrating load control to support isolated, high-wind-contribution systems.

17 WIND ENERGY↗

Measurement of lepton mass squared moments in B → X c ℓ ν ¯ ℓ decays with the Belle II experiment

We present measurements of the first to fourth moments of the lepton mass squared $q$ 2 of $B$ → $X_cℓ\overline{ν}_ℓ$ decays for $ℓ = e, μ$ and with $X_c$ a hadronic system containing a charm quark. These results use a sample of electron-positron collisions at the $Υ$($4S$) resonance corresponding to 62.8 fb -1 of integrated luminosity and collected by the Belle II 2 experiment in 2019 and 2020. To identify the $X_c$ system and reconstruct $q$ 2 , one of the $B$ mesons from an $Υ$($4S$) → $B\overline{B}$ decay is fully reconstructed in a hadronic decay mode using a multivariate $B$ tagging algorithm. We report raw and central moments for $q$ 2 > 1.5 $GeV$ 2 =$c$ 4 up to $q$ 2 > 8.5 $GeV$ 2 =$c$ 4 , probing up to 77% of the accessible $B$ → $X_cℓ\overline{ν}_ℓ$ phase space. This is the first measurement of moments in the experimentally challenging range of [1.5; 2.5] $GeV$ 2 =$c$ 4 . The results can be used for a new determination of |$V_{cb}$| using inclusive $B$ → $X_cℓ\overline{ν}_ℓ$ decays.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Two-dimensional mapping of absolute OH densities in an atmospheric pressure plasma effluent via planar laser-induced fluorescence: effects of He/H 2 O and He/O 2 mixtures in N 2 and air, with and without solid targets

Planar laser-induced fluorescence (LIF) was employed to measure the absolute density of hydroxyl radicals (OH) in the effluent of the COST Reference Microplasma Jet for two feed gas mixtures: He/H 2 O and He/O 2 . Experiments were conducted with the effluent propagating into air and N 2 environments. For the He/H 2 O case, measurements were also performed with the effluent impinging on a solid target at varying distances from the jet nozzle. Calibration of the OH-LIF signal from the COST-Jet was achieved by comparing it to a reference signal generated by the photofragmentation of H 2 O 2 . Results demonstrated that OH densities were sustained longer when the effluent propagates in a nitrogen environment compared to air, particularly with water added to the feed gas. The broader OH distribution in N 2 suggests slower consumption due to the absence of oxygen, which accelerates OH depletion in air via reactions involving O 2 and HO 2 . Even when water was not added to the feed, as in the He/O 2 case, appreciable OH densities were observed, due to gas impurities and reactive species interactions with atmospheric humidity, forming reaction fronts that delineate the gas flow. Two-dimensional fluid dynamics simulations elucidated the influence of atmospheric gas entrainment and solid targets on the OH distribution. Experimental trends were further compared with a zero-dimensional chemistry model to explore OH production and consumption mechanisms in air and nitrogen environments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗