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A set of RM resonance parameters was formatted in an ENDF-compatible file (with resonance flag LRF =7) and submitted to the ENDF GitHub repository for further testing and verification. The RM parameter representation impacted the fission reaction channel by introducing interference effects.
Abstract. The main goal of the TRacking Aerosol Convection interactions ExpeRiment (TRACER) project was to further understand the role that regional circulations and aerosol loading play in the convective cloud life cycle across the greater Houston, Texas, area. To accomplish this goal, the United States Department of Energy and research partners collaborated to deploy atmospheric observing systems across the region. Cloud and precipitation radars, radiosondes, and air quality sensors captured atmospheric and cloud characteristics. A dense lower-atmospheric dataset was developed using ground-based remote sensors, a tethersonde, and uncrewed aerial systems (UASs). TRACER-UAS is a subproject that deployed two UAS platforms to gather high-resolution observations in the lower atmosphere between 1 June and 30 September 2022. The University of Oklahoma CopterSonde and the University of Colorado Boulder RAAVEN (Robust Autonomous Aerial Vehicle – Endurant Nimble) were flown at two coastal locations between the Gulf of Mexico and Houston. The University of Colorado Boulder RAAVEN gathered measurements of atmospheric thermodynamic state, winds and turbulence, and aerosol size distribution. Meanwhile, the University of Oklahoma CopterSonde system operated on a regular basis to resolve the vertical structure of the thermodynamic and kinematic state. Together, a complementary dataset of over 200 flight hours across 61 d was generated, and data from each platform proved to be in strong agreement. In this paper, the platforms and respective data collection and processing are described. The dataset described herein provides information on boundary layer evolution, the sea breeze circulation, conditions prior to and nearby deep convection, and the vertical structure and evolution of aerosols. The quality-controlled TRACER-UAS observations from the CopterSonde and RAAVEN can be found at https://doi.org/10.5439/1969004 (Lappin, 2023) and https://doi.org/10.5439/1985470 (de Boer, 2023), respectively.
Montane snowpack is a vital source of water in the western United States. Here, we use a large-ensemble approach to evaluate the agreement across 124 snow water equivalent (SWE) projections with statistically downscaled forcing between end-of-century (2076–95) and early twenty-first century (2106–35) periods. Comparisons were performed on dates corresponding with the end of winter (15 April) and midspring snowmelt (15 May) in five western U.S. domains. Using 1) the percent change to end-of-century SWE across different ensembles of snow projections and 2) the shift between early twenty-first century and end-of-century SWE distributions for each snow projection, we identified relationships between projections that were consistent across each domain. In low to midelevations, end-of-century SWE decreases were 48% and larger on 15 April. These regions had projected changes to SWE that were both high confidence and in relative agreement across projections. Despite this, the majority of 15 April SWE volume existed in higher elevations where the magnitude and direction (positive or negative) of SWE changes were most uncertain. The results of this study show that large-ensemble approaches can be used to measure coherence between snow projections and identify 1) the highest confidence changes to future snow water resources and 2) the locations and periods where and when improvements to snow projections would most benefit estimates of future snow water resources.
We report evidence of the resonant scattering effect at the center of the galaxy cluster PKS 0745-191 with XRISM. We analyzed XRISM/Resolve commissioning-phase observations of the distant cluster PKS 0745-191 ($z = 0.103$) with a $54$ ks exposure. The gain drift was corrected using the onboard modulated X-ray source (MXS), and spectra were extracted from all the pixels that were well illuminated by MXS, the core region (four central pixels, $\sim$100 kpc), and the surrounding region. A single-temperature collisional ionization equilibrium (CIE) model fits the full field-of-view spectrum with $kT \approx 6$ keV and a turbulent velocity of ${\approx}120$ km s$^{-1}$. From the core ($r < 50$ kpc) spectrum, we detected a ${\approx}22\%$ suppression of the Fe xxv He$\alpha$ resonance (w) line relative to the CIE prediction. We performed a Monte Carlo simulation to calculate the resonant scattering (RS) effect using radial profiles from Chandra data. The RS-inferred turbulence agrees with that by Resolve line-broadening, demonstrating that RS provides an independent and consistent constraint on ICM turbulence. These results highlight XRISM/Resolve’s potential for turbulence studies in galaxy clusters.
The Dark matter Nanosatellite Equipped with Skipper Sensors (DarkNESS) deploys a recently developed skipper-CCD architecture with sub-electron readout noise in low Earth orbit (LEO) to investigate potential signatures of dark matter (DM). The mission addresses two interaction channels: electron recoils from strongly interacting sub-GeV DM and X-rays produced through decaying DM. Orbital observations avoid attenuation that limits ground-based measurements, extending sensitivity reach for both channels. The mission proceeds toward launch following laboratory validation of the instrument. A launch opportunity has been secured through Firefly Aerospace’s DREAM 2.0 program, awarded to the University of Illinois Urbana-Champaign (UIUC). As a result, this will constitute the first use of skipper-CCDs in space and evaluate their suitability for low-noise X-ray and single-photon detection in future space observatories.
The observational link between long gamma-ray bursts (GRBs) and broad-lined stripped-envelope core-collapse supernovae (SNe Ic-BL) is well established. Significant progress has been made in constraining what fraction of SNe Ic-BL may power high- or low-luminosity GRBs when viewed at small off-axis angles. However, the GRB–SN connection still lacks a complete understanding in the broader context of massive-star evolution and explosion physics. Models predict a continuum of outcomes for the fastest ejecta, from choked to ultrarelativistic jets, and observations from radio to X-rays are key to probing these scenarios across a range of viewing angles and velocities. Here, we present results from a coordinated radio-to-X-ray campaign targeting nearby (z ≲ 0.1) SNe Ic-BL designed to explore this diversity. With eight new radio-monitored events and updated data for one previously observed SN, we further tighten constraints on the fraction of SNe Ic-BL as relativistic as SN 1998bw (GRB 980425). We identify SN 2024rjw as a new radio-loud event likely powered by strong interaction with circumstellar material, and add evidence supporting a similar interpretation for SN 2020jqm. We also establish new limits on the properties of radio-emitting ejecta with velocities consistent with cocoons from choked jets, highlighting SN 2022xxf as a promising cocoon-dominated candidate. These results refine our understanding of the continuum linking ordinary SNe Ic-BL, engine-driven explosions, and GRBs, and contribute to building a sample that will inform future multimessenger searches for electromagnetic counterparts to high-energy neutrinos.
Climate change is intensifying fire behavior, with the largest and fastest-spreading fires causing the greatest impacts on people and ecosystems. Yet the mechanisms driving variability and trends in large fires remain poorly understood. Using 12-hour satellite-derived fire tracking data from 2012 to 2023, we show that the merging of separate ignitions into multi-ignition complexes is a key process amplifying fire size and destructive potential across temperate and boreal ecoregions. Multi-ignition fires account for 31% of the burned area in California and 59% in the Arctic-boreal domain, spread faster and persist longer than single-ignition fires, and disproportionately contribute to extreme fire years in California, Canada, and Siberia. They also generate stronger atmospheric feedbacks, produce more pyrocumulonimbus events, and strain firefighting capacity by dispersing suppression resources. Recognizing and accounting for fire-merging dynamics are critical for improving wildfire prediction, risk assessment, and management.
In this study, we present a comprehensive quantitative analysis of stability bands for dendritic arrays during directional solidification of a transparent succinonitrile-0.46 wt % camphor alloy, spanning a broad range of pulling velocities. Taking advantage of the microgravity environment aboard the International Space Station where most convection effects are suppressed, we obtain unique measurements that quantify the stable primary spacing range of spatially extended three-dimensional dendritic array structures under purely diffusive growth conditions. Through carefully designed velocity jump experiments and detailed examination of sub-grain boundary dynamics, we characterize key instabilities, including elimination and tertiary branching, shedding new light on the mechanisms governing dynamic dendritic spacing selection in extended 3D arrays. Phase field simulations are performed to characterize the stability limits of dendritic array structures for quantitative comparison with the flight experiments. Although the simulations capture general trends, significant deviations are noted at the upper stability boundary, indicating the influence of additional, unexplored factors. These findings contribute to a deeper understanding of dendritic growth dynamics and offer valuable benchmark data that could aid in refining predictive models and improving control of dendritic microstructures in metallurgical applications.
The extragalactic background light (EBL) from ultraviolet to infrared comprises the emission from all stars, galaxies, and actively accreting black holes in the observable Universe. A precise measurement of the EBL is critically important to probe models of star formation and galaxy evolution. The EBL can be measured via the absorption imprint left on the spectra of γ-ray blazars. In this work, we rely on 15 yr of Fermi-LAT data and 1576 blazars to measure the EBL optical depth in the 0 < z < 4.3 range. We detect the EBL attenuation with ≅23σ significance and measure the optical depth in 19 redshift bins, extending the coverage and improving on our previous results. This allows us to reconstruct the evolution of the EBL and find general consistency with recent EBL models. These results represent the most precise determination of the EBL with GeV γ rays to date.
The FFA@CEBAF energy upgrade study aims to approximately double the final energy of the electron beam at the Continuous Electron Beam Accelerator Facility (CEBAF). It will do this by replacing the highest-energy recirculating arcs with fixed-field alternating gradient (FFA) arcs, allowing for several more passes to circulate through the machine. This upgrade necessitates the re-design of the vertical spreader sections, which separates each pass into different recirculation arcs. Additionally, the FFA arcs will need horizontal splitter lines to correct for time of flight and R56. This work will present the current state of the spreader re-design and splitter design.
On 2015 October 2, MMS spacecraft observed an electron micro-injection event near the southern hemispheric high-altitude cusp, coinciding with intense wave activity across several frequency bands. Here, we investigated the MMS magnetic field and plasma during this event to explore cross-scale coupling among the wave modes. Employing the Hilbert-Huang transform, we perform an empirical mode decomposition to extract frequencies and amplitudes of the intrinsic mode functions (IMFs). In this analysis, we establish both linear and nonlinear relationships and examine the information transfer between the IMFs. Notably, the transfer entropy suggests that high frequency ion cyclotron waves may be driven by the mirror mode structures. Our case study effectively demonstrates the utility of the information theory based tools for studying cross-scale wave coupling phenomena.
The Arctic is warming at over twice the rate of the rest of the Earth, resulting in significant changes in vegetation seasonality that regulates annual carbon, water, and energy fluxes. However, a crucial knowledge gap exists regarding the intricate interplay among climate, permafrost, and vegetation that generates high phenology variability across extensive tundra landscapes. This oversight has led to significant discrepancies in phenological patterns observed across warming experiments, long-term ecological observations, and satellite and modeling studies, undermining our ability to understand and forecast plant responses to climate change in the Arctic. To address this problem, we assessed plant phenology across three low-Arctic tundra landscapes on the Seward Peninsula, Alaska, using a combination of in-situ phenocam observations and high-resolution PlanetScope CubeSat data. We examined the patterns and drivers of phenological diversity across the landscape by (1) quantifying phenological diversity among dominant plant function types (PFTs) and (2) modeling the interrelations between plant phenology and fine-scale landscape features, such as topography, snowmelt, and vegetation. Our findings reveal that both spring and fall phenology varied significantly across Arctic PFTs, accounting for about 25%–44% and 34%–59% of the landscape-scale variation in the start of spring [SOS] and start of fall [SOF], respectively. Deciduous tall shrubs (e.g. alder and willow) had a later SOS (~7 d behind the mean of other PFTs), but completed leaf expansion (within 2 weeks) considerably faster compared to other PFTs. We modeled the landscape-scale variation in SOS and SOF using Random Forest, which showed that plant phenology can be accurately captured by a suite of variables related to vegetation composition, topographic characteristics, and snowmelt timing (variance explained: 53%–68% for SOS and 59%–82% for SOF). Notably, snowmelt timing was a crucial determinant of SOS, a factor often neglected in most spring phenology models. Our study highlights the impact of fine-scale vegetation composition, snow seasonality, and landscape features on tundra phenological heterogeneity. Improved understanding of such considerable intra-site phenological variability and associated proximate controls across extensive Arctic landscapes offers critical insights for representation of tundra phenology in process models and associated impact assessments with climate change.
We conduct a systematic search for astrophysical transients using data from the Atacama Cosmology Telescope. The data were taken from 2017 to 2022 in three frequency bands spanning 77 to 277 GHz. In this paper, we present a pipeline for transient detection using single-observation maps where each pixel of a map contains one observation with an integration time of approximately 4 minutes. We detect 34 transient events at 27 unique locations. All but two of the transients are associated with Galactic stars and exhibit a wide range of properties. We also detect an event coincident with the classical nova YZ Ret and one event consistent with a flaring active galactic nucleus. We notably do not detect any reverse shock emission from gamma-ray bursts, a nondetection that may be in tension with current models.
The microphysical impacts of aerosol particles on scattered isolated deep convective cells near Houston, Texas, on 19 June 2013, are examined using multiple cloud-resolving model (CRM) simulations initialized with vertical profiles of low and high concentrations of cloud droplet–nucleating aerosols. These simulations formed part of the Model Intercomparison Project (MIP) conducted by the Deep Convective Working Group of the Aerosol, Cloud, Precipitation and Climate (ACPC) initiative. Each CRM generated a field of convective cells representing those observed during the case study with varying degrees of accuracy. The Tracking and Object-Based Analysis of Clouds (tobac) cell-tracking algorithm was applied to each MIP CRM simulation to track relatively long-lived convective cells (20–60 min). Most of the CRMs produced similar aerosol loading impacts on the warm phase of tracked cell properties with reduced autoconversion and accretion growth of rain, increased cloud water, reduced rainfall, and reduced near-surface evaporation of rain. The sign of aerosol impacts on the warm-phase properties of the convective cells was also quite consistent over cell lifetimes with the greatest magnitude of influence in the first half of the life cycle in most CRMs. In contrast, the ice-phase response to aerosol loading was highly variable among CRMs and included increases or decreases in ice amounts at inconsistent stages of the cell life cycle and midlevel versus upper-level changes in ice. This intermodel variability in ice is indicative both of the complex indirect interactions between aerosols and ice-phase processes in deep convection and their associated parameterizations.
Global weather forecast models are vital tools with numerous applications, including public safety, agriculture, and transportation. Recent advancements in artificial intelligence (AI) and deep learning (DL) have shown the potential to enhance weather forecasting accuracy and speed. In this study, we developed a short-term hourly weather forecast framework with a wavelet transform function for data preprocessing and a spatiotemporal DL model, PredRNN, for predicting five surface atmospheric variables, including wind speed and direction, mean sea level pressure (MSLP), temperature, and precipitation. The framework demonstrated promising results. It produces global forecasts at 0.25° (∼25 km) with a 1-day lead time RMSE of 1.8 m/s for wind components, 180 Pa for MSLP, and 1.8 K for temperature. Although our model does not surpass state-of-the-art AI weather forecast models across all metrics, it outperforms these models in precipitation forecasting and wind prediction at short lead times and achieves comparable accuracy for MSLP. Its native hourly forecasting capability, together with training on widely accessible GPU hardware, contributes meaningfully to the advancement of accessible DL weather forecasting methods. Our work highlights the importance of integrating temporal components and data transformation techniques to improve the predictability and accuracy of weather forecasts.
Hydrological extremes are intensifying globally, increasing the complexity of decisions required to ensure water security. Advances in hydrological science, modeling, and data systems have expanded the technical frontier of water research, yet uptake of scientific insights in policy and management decisions remains limited. This persistent science–policy gap is not primarily a failure of knowledge generation or robustness, but an institutional challenge shaped by how scientific and governance systems are organized, coordinated, and connected to support the effective use of scientific knowledge. These challenges are particularly pronounced in multi-level and transboundary water governance, where decisions span jurisdictions and require coordination across institutional and political boundaries. We synthesize research at the science–policy interface and evidence from water security initiatives to show how institutional arrangements, scientific tool development, and research practices enable or constrain the sustained use of scientific knowledge in water-security governance processes. Building on these insights, we develop ‘shared decision infrastructure’ as a framing to describe how scientific knowledge is embedded within the institutional, relational, and procedural arrangements that connect science to decision-making processes over time. We translate this framing into a practical intervention roadmap centered on institutional design, tool translation, sustained co-production, and outcome-oriented evaluation to support the integration of science into ongoing governance processes. By positioning science as shared decision infrastructure, the roadmap clarifies how researchers can design scientific efforts that support more coordinated, accountable, and adaptive water security decisions amid deepening uncertainty.
Assembly of biomolecules at solid-water interfaces requires molecules to traverse complex orientation-dependent energy landscapes through processes that are poorly understood, largely due to the dearth of in-situ single molecule measurements and statistical analyses of the rotational dynamics that define directional selection. Emerging capabilities in high-speed atomic force microscopy and machine learning have allowed us to directly determine the orientational energy landscape and observe and quantify the rotational dynamics for protein nanorods on the surface of muscovite mica under a variety of conditions. Comparisons with kinetic Monte Carlo simulations show that the transition rates between adjacent orientation-specific energetic minima can largely be understood through traditional models of in-plane Brownian rotation across a biased energy landscape, with resulting transition rates that are exponential in the energy-barriers between states. However, transitions between more distant angular states are decoupled from barrier height, with jump-size distributions showing a power-law decay that is characteristic of a non-classical Levy-flight random walk, indicating that large jumps are enabled by alternative modes of motion via activated states. The findings provide new insights into the dynamics of biomolecules at solid-liquid interfaces that lead to self-assembly, epitaxial matching and other orientationally anisotropic outcomes and define a general procedure for exploring such dynamics with implications for hybrid biomolecular-inorganic materials design.
The long-term durability prospects of halide perovskite solar cells are rapidly improving; however, the interface between the hole transport layer (HTL) and the perovskite remains a source of degradation. Alone, polymer- or carbazole-based HTLs suffer from incomplete coverage of the underlying indium tin oxide glass, leading to degradation and compromised performance. Here, we show a multi-HTL approach whereby a polymer HTL is reinforced using a phosphonic acid modification leading to better protection of the buried perovskite interface and more columnar growth of perovskite film, resulting in an ~40-mV open-circuit voltage (VOC) improvement indicative of suppressed interfacial recombination across multiple p-i-n device architectures. Solar cells with this reinforced HTL show higher tolerance to several accelerated stress tests. We report, among the best durabilities for unencapsulated cells, a T90 ~3,000 h (T80 ~5,900 h) at 65 degrees C under continuous 1.2 sun AM 1.5G illumination and maximum power point tracking, representing a nearly 4-fold increase compared with [2-(9H-carbazol-9-yl)ethyl]phosphonic acid (2PACz)-only devices. Furthermore, we deployed a device with this reinforced HTL on a cube satellite, with long-duration operational space testing results exceeding T80 for the complete mission duration of ~100 days in low Earth orbit.