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We demonstrate a spot-checking approach for real-time drift estimation and mitigation in continuously operating quantum links. Using polarization-entangled photon pairs, we achieve stable 16-hour operation, validating efficient, minimally invasive monitoring of quantum-network performance.
On 10 October 2025 an explosion occurred at a facility operated by Accurate Energetic Systems in Humphreys County, Tennessee. The incident resulted in 16 fatalities and created a debris field over several square kilometers. To address remaining questions about explosion timing and size, we collected about 20 seismic and 19 acoustic records of the blast from sensors up to hundreds of kilometers away. We then deployed 10 distinct physics-based, reduced order models (ROMs) that used validated geological structure and atmospheric conditions from the time of the event, along with observations of body- and surface-wave energy, as well as acoustic overpressure and phase duration. Each ROM predicted either timing, yield estimates, or both. We binned these estimates and their uncertainties according to each ROMs’ assumptions about confinement (aboveground, buried fully coupled, and buried partially coupled) and combined these estimates with other forensic data to conclude that the event occurred as a single, aboveground explosion on 10 December 2025 12:47:50.8 ±0.1 s with a yield equivalent to 11.8 [2.3,16.5] tons of Trinitrotoluene. Our estimates align with the Bureau of Alcohol, Tobacco, Firearms and Explosives inventory reports of 11–13 tons. This multimethod approach demonstrates the use of remotely observed geophysical data to rapidly aid conventional forensic investigations of accidental explosions.
Cherenkov gamma ray detectors are a valuable diagnostic technique for HED systems. Inertial confinement fusion is working towards high gain ignition. Gamma rays from ICF systems can help inform the degradation mechanisms preventing ignition: Carbon ablator is not being degraded, unlike the DT fuel; carbon γ timing can estimate shell energy during bangtime and may suggest hot spot cooling rate; and Cherenkov detectors are also used for high spatial and temporal resolution ICF mix studies on OMEGA. Cherenkov systems also inform energy spectrum of radiographic sources: ARC laser and Mercury pulsed power machine.
This report describes a proof-of-concept method of seismic source discrimination using seismic gradiometry and a common machine learning technique. The tests described here are purely numerical, using synthetic seismic data and well understood mathematical techniques. The primary innovation described here is the application of a richer seismic data set derived from seismic gradiometry. Seismic gradiometry is a method to estimate the time variable spatial gradient of the wavefield to compute various wavefield attributes such as slowness, dynamic strain, and rotational motions. With the addition of these wavefield attributes, we are afforded up to twenty "compo- nents" of time series data measured at a single point on, or in, the Earth. This is in direct contrast to conventional three-component seismic data collected at several locations using a seismic network. Using the gradiometrically-derived wavefield components directly in a single-layer neural network, I show that it is possible to discriminate between three common seismic source types (earthquakes, explosions, and opening fractures) for various noise conditions and gradiometry configurations.
The Arctic is warming and feedbacks in the coupled Earth system may be driving the Arctic to tipping events that could have critical downstream impacts for the rest of the globe. In this project we have focused on analyzing sea ice variability and loss in the coupled Earth system Summer sea ice loss is happening rapidly and although the loss may be smooth and reversible, it has significant consequences for other Arctic systems as well as geopolitical and economic implications. Accurate seasonal predictions of sea ice minimum extent and long-term estimates of timing for a seasonally ice-free Arctic depend on a better understanding of the factors influencing sea ice dynamics and variation in this strongly coupled system. Under this project we have investigated the most influential factors in accurate predictions of September Arctic sea ice extent using machine learning models trained separately on observational data and on simulation data from five E3SM historical ensembles. Monthly averaged data from June, July, and August for a selection of ice, ocean, and atmosphere variables were used to train a random forest regression model. Gini importance measures were computed for each input feature with the testing data. We found that sea ice volume is most important earlier in the season (June) and sea ice extent became a more important predictor closer to September. Results from this study provide insight into how feature importance changes with forecast length and illustrates differences between observational data and simulated Earth system data. We have additionally performed a global sensitivity analysis (GSA) using a fully coupled ultra- low resolution configuration E3SM. To our knowledge, this is the first global sensitivity analysis involving the fully-coupled E3SM Earth system model. We have found that parameter variations show significant impact on the Arctic climate state and atmospheric parameters related to cloud parameterizations are the most significant. We also find significant interactions between parameters from different components of E3SM. The results of this study provide invaluable insight into the relative importance of various parameters from the sea ice, atmosphere and ocean components of the E3SM (including cross-component parameter interactions) on various Arctic-focused quantities of interest (QOIs).
Sandia National Laboratories has developed a capability to estimate parameters of epidemiological models from case reporting data to support responses to the COVID-19 pandemic. A differentiating feature of this work is the ability to simultaneously estimate county-specific disease transmission parameters in a nation-wide model that considers mobility between counties. The approach is focused on estimating parameters in a stochastic SEIR model that considers mobility between model patches (i.e., counties) as well as additional infectious compartments. The inference engine developed by Sandia includes (1) reconstruction and (2) transmission parameter inference. Reconstruction involves estimating current population counts within each of the compartments in a modified SEIR model from reported case data. Reconstruction produces input for the inference formulations, and it provides initial conditions that can be used in other modeling and planning efforts. Inference involves the solution of a large-scale optimization problem to estimate the time profiles for the transmission parameters in each county. These provide quantification of changes in the transmission parameter over time (e.g., due to impact of intervention strategies). This capability has been implemented in a Python-based software package, epi_inference, that makes extensive use of Pyomo [5] and IPOPT [10] to formulate and solve the inference formulations.
The primary goal of the FNAL Muon g-2 Experiment, located in Batavia, IL, USA, is to measure the anomalous magnetic dipole moment, a μ = (g-2)/2, of a muon to a precision of 0.14 ppm. Initial results from the experiment confirm a discrepancy between the experimental value of the muon anomaly compared with the theoretical value first discovered by the Brookhaven Muon g-2 Experiment in 2001. This discrepancy hints at new physics. However, running the experiment with negative muons is essential in order to verify the experimental value of the muon anomaly. As neither experiments nor simulation studies pertaining to the muon anomaly have been performed with negative muons, the goal of this work is to estimate the rates of negative muons arriving at the FNAL Muon g-2 experiment so that scientists can estimate the time required to run the experiment with negative muons. In order to do this, we performed several simulations of the Muon Campus, which delivers muons to the FNAL Muon g-2 Experiment, using the G4Beamline v. 3.06 at the National Energy Research Computing Center. We performed separate simulations for different sections of the muon campus, and simulations were repeated twice, once for negative muons and once for positive muons. An initial 10 9 protons were shot on target for both the negative and positive muon simulations. We found that the rate of negative muons arriving at the experiment was 0.549 times the rate of positive muons arriving at the experiment.
Adding additional flow testing facilities has been considered as part of the High Flux Isotope Reactor (HFIR) Sustaining and Enhancing Neutron Science (SENSe) Initiative at Oak Ridge National Laboratory (ORNL) to support HFIR operations and experiments. This prospect has prompted many ideas and discussions regarding potential features, configurations, locations, and applications for the facilities. A working group of ORNL staff members was formed in fiscal year 2022 to recommend one or more configurations to best support future HFIR operations and scientific capacities and to develop order-of magnitude cost estimates and timing. The ideas discussed in this report include options ranging from upgrading existing small-scale testing facilities to building a full-scale HFIR mockup for detailed thermohydraulic testing and fuel assessment.
As part of the Sustaining and Enhancing Nuclear Science (SENSe) initiative at Oak Ridge National Laboratory (ORNL), the prospect of adding new detection systems to support High Flux Isotope Reactor (HFIR) operations and to advance scientific research has prompted many ideas and discussions regarding potential features, configurations, locations, and applications. A working group of ORNL staff members was organized to further develop the concepts and recommending one or more configurations to best support future HFIR operations and scientific capacities and to provide order-of-magnitude cost estimates and timing. The areas of investigation included fast access detection systems, non-scattering beamline instruments, shielded detection instruments, and an ultracold neutron source. In each case, the focus was to develop world leading capabilities that would be unmatched by any other facility.
This LANSCE Facility Longevity Assessment examined 34 total systems critical to the operation of the LANSCE accelerator complex. These systems are primarily owned, operated, and maintained by AOT Division. Based on the criteria used in this assessment, there are 12 AOT Division systems that are potential high risk as related to longterm LANSCE operations. These systems may also impact the success of LAMP. Also summarized in this report are the facility activities planned over the next decade. These include general facility maintenance and infrastructure upgrades, as well as planned electrical system upgrades. The LANSCE Facility Operations (LFO) Division is responsible for this activity scope and budget planning. Most of the planned activities are general in nature and should have little impact on accelerator operations or LAMP activities as currently envisioned. However, close coordination with between AOT, LFO, and LAMP would be prudent. A next step should be to develop an actionable plan to address the high-risk systems. Potential criteria for developing such a plan could include: (1) Evaluating the global impact of the system (operations, program, safety, etc.), (2) Ranking by impact (ranked now by risk), (3) Estimating cost to implement the corrective actions (Is there already a plan or not?), and (4) Estimating the time required for corrective actions. An alternative could include integration of the assessment data into both the Asset Management System and the Maintenance Analytics Risk Register.
This report describes characterization of nitrogen species (nitrate, nitrite, ammonia) in aqueous and solid phases in cores taken in borehole D0220 (well 299-E25-245) under crib 216-A-37-1 at the Hanford Site in 2022 to evaluate (a) the types of nitrogen species currently in the vadose zone and (b) the migration of nitrogen species in the vadose zone. Ammonia and tritium from PUREX decladding condensate were sporadically discharged to the crib from 1977 to 1989. During discharge operations, the estimated travel time through the vadose zone to groundwater was 2.5 to 9 months. By 2003, a characterization borehole (C4106) showed residual pore water with elevated tritium and nitrate in the first 100 ft of the vadose zone, which is likely from the crib. However, there was significantly more nitrate present at the shallowest depth (15 to 22 ft), which may indicate a different source for nitrate, such as adsorbed ammonia slowly being oxidized or nitrogen species precipitates slowly dissolving. In this study, more extensive nitrogen species characterization was done on D0220 cores (drilled in 2022) at 40- and 262-ft depths, which included (a) aqueous nitrate, nitrite, and tritium; (b) adsorbed ammonia; (c) nitrogen in carbonates (or other minerals dissolved in acidic acid); (d) nitrogen in iron oxides (or other minerals dissolved in oxalic acid); and (e) nitrogen in hard-to-extract minerals (minerals dissolved in nitric acid). High pore water nitrate (226 to 331 mg/L) at 40-ft depth measured in D0220 (2022) compared to 60 mg/L at 40-ft depth in C4106 (2003) may indicate nitrate is migrating deeper. Tritium concentrations (pore water 132,000 to 148,000 pCi/L) measured in D0220 at 40-ft depth in 2022 were considerably higher than in C4106 at 40-ft depth (160 pCi/L). Additional nitrogen species mass was present in adsorbed and precipitated phases in D0220 cores. Low adsorbed ammonia was measured at 40.1- and 261.7-ft depths. Low concentrations of carbonate associated nitrogen and iron oxide-associated nitrogen were present at 40.1 and 40.6 ft depths. Nitrogen species in solid phase extractions indicate nitrogen precipitates or aqueous nitrate or ammonia trapped in sediment microfractures that are coated by precipitates. Overall, the nitrogen species and tritium characterization at two depths in D0220 showed that additional nitrogen species were present in the vadose zone. In addition, elevated pore water nitrate in D0220 at 40 ft depth from 2022 may indicate vertical migration compared to C4106 nitrate profile from 2003. Analysis of additional depths in D0220 and spatial variability of the nitrate plume along the length of the crib (from surface electrical resistivity) would be extremely useful for this comparison.
The 2022 Transportation Annual Technology Baseline (ATB) provides detailed cost and performance data, estimates, and assumptions for vehicle and fuel technologies in the United States. It includes current and projected estimates: time-series through 2050 for light, medium, and heavy-duty vehicle technologies; scenarios for conventional and alternative fuels. It details the assumptions used to calculate those costs, such as natural gas and electricity prices, discount rates, and vehicle miles traveled. The 2022 Transportation ATB vehicle data are specifically for cars powered by gasoline, diesel, natural gas, gasoline hybrid, plug-in hybrid, battery electric, and fuel-cell powertrains and for trucks powered by diesel, diesel hybrid, plug-in hybrid, battery electric, and fuel cell powertrains. Fuels and blendstocks include gasoline, ethanol, blendstock for oxygenate blending, diesel, diesel from biomass, natural gas, electricity, hydrogen, aviation fuel, and marine fuel. At this time, the ATB does not include other vehicles such as buses, 2- and 3-wheeled motorized vehicles, or non-road vehicles such as aircraft, vessels, locomotives, and those for industry and agriculture. See "ATB Transportation Website" resource below for more project information.
The 2024 Transportation Annual Technology Baseline (ATB) provides detailed cost and performance data, estimates, and assumptions for vehicle and fuel technologies in the United States. It includes current and projected estimates: time-series through 2050 for light, medium, and heavy-duty vehicle technologies; scenarios for conventional and alternative fuels. It details the assumptions used to calculate those costs, such as natural gas and electricity prices, discount rates, and vehicle miles traveled. The 2024 Transportation ATB vehicle data are specifically for cars powered by gasoline, diesel, natural gas, gasoline hybrid, plug-in hybrid, battery electric, and fuel-cell powertrains and for trucks powered by diesel, diesel hybrid, plug-in hybrid, battery electric, and fuel cell powertrains. Fuels and blendstocks include gasoline, ethanol, blendstock for oxygenate blending, diesel, diesel from biomass, natural gas, electricity, hydrogen, aviation fuel, and marine fuel. At this time, the ATB does not include other vehicles such as 2- and 3-wheeled motorized vehicles, or non-road vehicles such as aircraft, vessels, locomotives, and those for industry and agriculture. See "Transportation ATB Website" resource below for more project information.
The Voyager 1 (V1) and Voyager 2 (V2) spacecraft were launched in 1977 on a mission to explore the outer planets and reach the heliopause, the boundary between the hot solar plasma and the relatively cool interstellar plasma. V1 reached the heliopause on 2012 August 25, at 121.6 au, and V2 reached the heliopause on 2018 November 5, at 119.0 au. One of their remarkable discoveries was the detection of shocks propagating into the interstellar plasma from energetic solar events. These shocks are typically preceded by electron plasma oscillations excited by electron beams streaming along interstellar magnetic field lines ahead of the shocks. The frequencies of the plasma oscillations have now provided radial electron density profiles in the outer heliosphere and in the interstellar medium to radial distances of more than 145 au. The oscillations are typically preceded by bursts of high-energy ∼5–100 MeV electrons. These electron bursts are interpreted as being due to the reflection (and acceleration) of cosmic-ray electrons by the shock at the time the shock first contacts the magnetic field line that passes through the spacecraft. Relative timing between the cosmic rays reflected by the shock and the onset of the plasma oscillations allow us, for the first time, to estimate the energy, ∼20–100 eV, of the electron beams responsible for the plasma oscillations. These observations are combined into a self-consistent model called the foreshock model that describes the interaction of shocks of solar origin with the interstellar plasma.
AM CVn systems are a rare type of accreting binary that consists of a white dwarf and a helium-rich, degenerate donor star. Using the Zwicky Transient Facility (ZTF), we searched for new AM CVn systems by focusing on blue, outbursting stars. We first selected outbursting stars using the ZTF alerts. We cross matched the candidates with Gaia and Pan-STARRS catalogs. The initial selection of candidates based on the Gaia BP-RP contains 1751 unknown objects. We used the Pan-STARRS g-r and r-i color in combination with the Gaia color to identify 59 strong AM CVn candidates. We obtained identification spectra of 35 sources, of which 18 are high-priority candidates, and discovered nine new AM CVn systems and one magnetic CV that shows only He-ii lines. Using the outburst recurrence time, we estimate the orbital periods of the nine new AM CVn systems that are in the range of 29–50 minutes. We conclude that targeted follow up of blue, outbursting sources is an efficient method to find new AM CVn systems and we plan to follow up all candidates we identified to systematically study the population of outbursting AM CVn systems.
Based on Sloan Digital Sky Survey Data Release 16, we have detected the large-scale structure of Lyα emission in the universe at redshifts z = 2–3.5 by cross-correlating quasar positions and Lyα emission imprinted in the residual spectra of luminous red galaxies. We apply an analytical model to fit the corresponding Lyα surface brightness profile and multipoles of the redshift-space quasar–Lyα emission cross-correlation function. The model suggests an average cosmic Lyα luminosity density of $6.6^{+3.3}_{-3.1}$ x 10 40 erg s -1 cMpc -3 , a ~2σ detection with a median value about 8–9 times those estimated from deep narrowband surveys of Lyα emitters at similar redshifts. Although the low signal-to-noise ratio prevents us from a significant detection of the Lyα forest–Lyα emission cross-correlation, the measurement is consistent with the prediction of our best-fit model from quasar–Lyα emission cross-correlation within current uncertainties. We rule out the scenario where the Lyα photons mainly originate from quasars. We find that Lyα emission from star-forming galaxies, including contributions from that concentrated around the galaxy centers and that in diffuse Lyα-emitting halos, is able to explain the bulk of the Lyα luminosity density inferred from our measurements. Ongoing and future surveys can further improve the measurements and advance our understanding of the cosmic Lyα emission field.
The KAZRARSCL-CLOUDSAT Value-Added Product (VAP) is based on the KAZR-ARSCL VAP, which provides cloud boundaries and best-estimate time-height fields of radar moments. The KAZRARSCL-CLOUDSAT VAP applies a statistically-derived calibration offset to reflectivity fields in order to align them with observations from the spaceborne CloudSat Cloud Profiling Radar. For details on the offset values applied, please refer to "Kollias, P., Puigdomènech Treserras, B., and Protat, A.: Calibration of the 2007–2017 record of ARM Cloud Radar Observations using CloudSat, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-34, 2019."