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At least 1,279 records · Page 71

The effect of inverse Compton losses on particle acceleration in three-dimensional relativistic reconnection

Relativistic magnetic reconnection is a key mechanism for dissipating magnetic energy and accelerating particles in astrophysics. In the absence of radiative cooling, recent particle-in-cell (PIC) simulations have shown that high-energy particles gain most of their energy in the upstream region, during a short-lived "free phase" where they meander between the two sides of the layer; when they get captured/trapped by the downstream flux ropes, they undergo a "trapped phase", where no significant energization occurs. Here, we perform a suite of 3D PIC simulations of relativistic reconnection including inverse Compton (IC) losses in the weakly cooled regime in which the radiation-reaction-limited Lorentz factor $γ_{\rm rad}$ exceeds the magnetization $σ$. We show that electron cooling losses do not appreciably alter the reconnection rate, the structure of the layer, and the physics of particle acceleration in the free phase, so the spectrum of free electrons is $dN_{\rm free}/dγ\propto γ^{-1}$, as in the uncooled case. The spectrum of trapped electrons above the cooling break $γ_{\rm cool}$ (in the range $γ_{\rm cool}<γ<γ_{\rm rad}$) is $dN/dγ\propto γ^{-3}$, steeper than the scaling $dN/dγ\propto γ^{-2}$ of uncooled simulations. This confirms that no significant particle energization occurs during the trapped phase. Our results validate the model by arXiv:2302.12269 for particle acceleration in 3D relativistic reconnection, and imply that radiative emission models of reconnection-powered astrophysical sources should employ a two-zone structure, that differentiates between free, rapidly accelerating particles and trapped, passively cooling particles.

FOS: Physical sciences↗

Impacts of long-range transport of aerosols on marine-boundary-layer clouds in the eastern North Atlantic

Vertical profiles of aerosols are inadequately observed and poorly represented in climate models, contributing to the current large uncertainty associated with aerosol–cloud interactions. The US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) aircraft field campaign near the Azores islands provided ample observations of vertical distributions of aerosol and cloud properties. Here we utilize the in situ aircraft measurements from the ACE-ENA and ground-based remote-sensing data along with an aerosol-aware Weather Research and Forecast (WRF) model to characterize the aerosols due to long-range transport over a remote region and to assess their possible influence on marine-boundary-layer (MBL) clouds. The vertical profiles of aerosol and cloud properties measured via aircraft during the ACE-ENA campaign provide detailed information revealing the physical contact between transported aerosols and MBL clouds. The European Centre for Medium-Range Weather Forecasts Copernicus Atmosphere Monitoring Service (ECMWF-CAMS) aerosol reanalysis data can reproduce the key features of aerosol vertical profiles in the remote region. The cloud-resolving WRF sensitivity experiments with distinctive aerosol profiles suggest that the transported aerosols and MBL cloud interactions (ACIs) require not only aerosol plumes to get close to the marine-boundary-layer top but also large cloud top height variations. Based on those criteria, the observations show that the occurrence of ACIs involving the transport of aerosol over the eastern North Atlantic (ENA) is about 62 % in summer. For the case with noticeable long-range-transport aerosol effects on MBL clouds, the susceptibilities of droplet effective radius and liquid water content are -0.11 and +0.14, respectively. When varying by a similar magnitude, aerosols originating from the boundary layer exert larger microphysical influence on MBL clouds than those entrained from the free troposphere.

54 ENVIRONMENTAL SCIENCES↗

On the circulation, water mass distribution, and nutrient concentrations of the western Chukchi Sea

Substantial amounts of nutrients and carbon enter the Arctic Ocean from the Pacific Ocean through the Bering Strait, distributed over three main pathways. Water with low salinities and nutrient concentrations takes an eastern route along the Alaskan coast, as Alaskan Coastal Water. A central pathway exhibits intermediate salinity and nutrient concentrations, while the most nutrient-rich water enters the Bering Strait on its western side. Towards the Arctic Ocean, the flow of these water masses is subject to strong topographic steering within the Chukchi Sea with volume transport modulated by the wind field. In this contribution, we use data from several sections crossing Herald Canyon collected in 2008 and 2014 together with numerical modelling to investigate the circulation and transport in the western part of the Chukchi Sea. We find that a substantial fraction of water from the Chukchi Sea enters the East Siberian Sea south of Wrangel Island and circulates in an anticyclonic direction around the island. This water then contributes to the high-nutrient waters of Herald Canyon. The bottom of the canyon has the highest nutrient concentrations, likely as a result of addition from the degradation of organic matter at the sediment surface in the East Siberian Sea. The flux of nutrients (nitrate, phosphate, and silicate) and dissolved inorganic carbon in Bering Summer Water and Winter Water is computed by combining hydrographic and nutrient observations with geostrophic transport referenced to lowered acoustic Doppler current profiler (LADCP) and surface drift data. Even if there are some general similarities between the years, there are differences in both the temperature–salinity and nutrient characteristics. To assess these differences, and also to get a wider temporal and spatial view, numerical modelling results are applied. According to model results, high-frequency variability dominates the flow in Herald Canyon. This leads us to conclude that this region needs to be monitored over a longer time frame to deduce the temporal variability and potential trends.

54 ENVIRONMENTAL SCIENCES↗

Dark Energy Survey Supernova 5YR Data Release

This data set was generated by the Dark Energy Survey (DES) team, and was used for the cosmological analysis in the DES key supernova cosmology paper (arXiv:2401.02929). When using this data please cite both the data release paper (Sanchez et al. 2024, ApJ, arXiv:2406.05046) and the DES key paper (Dark Energy Survey Collaboration 2024, ApJL, arXiv:2401.02929). At DES-SN5YR Github Repo (https://github.com/des-science/DES-SN5YR), we provide a comprehensive list of references for the different data products (SALT3 light-curve model, calibration, DCR corrections, SN classification, SN distances and uncertainties covariance matrix). If you have questions, post an issue on the DES-SN5YR Github repository and/or get in touch with the contact people indicated on the Github repository.

Contractor, Fermilab [Fermilab]↗

Code associated with "Assessing the Effect of a Deep-Rooted Grass on Belowground Carbon Storage in Cultivated Land: Insights from a Multi-Site US Study"

R code associated with "Assessing the Effect of a Deep-Rooted Grass on Belowground Carbon Storage in Cultivated Land: Insights from a Multi-Site US Study" These scripts can be used to analyze the accompanying dataset: https://doi.org/10.5281/zenodo.16620529. (1) resampling_functions.R : Creates functions for resampling data for nonparametric bootstrapping (2) bootstrap_depth_profiles.R : Applies resampling functions to the dataset (3) gapfill_masscalcs.R : Prepares data for equivalent mass calculations (4) get_14C_EM.R : Applies soilR to obtain the 14C end member for mixing model calculations (5) mass_aggregate.R : Performs equivalent mass and mass weighted averaging calculations.

Carbon↗

Wind and Temperature Consensus at Horn Point, HU-Beltsville, Piney Run (Maryland) in support of CoURAGE

The Maryland Department of the Environment (MDE) operates a ground-based atmospheric profiling network consisting of collocated radar wind profilers (RWP) and radio acoustic sounding systems (RASS) as part of its Ambient Air Monitoring Program. This network provides continuous observations of wind and temperature structure in the lower troposphere to support air quality forecasting, regulatory analysis, and atmospheric research. The network currently includes three fixed sites across Maryland: Horn Point (HP, lower eastern shore) [38.587525°,-76.141006°], Howard University-Beltsville (HUB, central Maryland) [39.055277°, -76.878632°], and Piney Run (PR, western Maryland) [39.705950°, -79.012000°] The network is designed to capture regional variability in atmospheric transport and boundary-layer processes. These systems measure vertical profiles of horizontal wind speed and direction using Doppler radar techniques, with observations typically spanning from ~100 m above ground level up to approximately 2.5–4 km. Measurements are derived from the Doppler shift of backscattered electromagnetic signals, enabling retrieval of wind vectors at multiple altitudes with high temporal resolution (e.g., 30-minute averages reported every 6 minutes). Each radar wind profiler is paired with a Radio Acoustic Sounding System (RASS) to provide profiles of virtual temperature in the lower atmosphere (~100–200 m AGL) by measuring the propagation speed of acoustic waves. Together, the RWP/RASS system yields a coupled data set of thermodynamic and kinematic atmospheric structure, including additional parameters such as vertical velocity, radial velocity, signal-to-noise ratio, and spectral width for advanced analysis. There are two types of files for each station: wind data (files with a "w" prefix) and virtual temperature RASS data (files with a "t" prefix). The wind data files are in the format wYYDDD.cns, where YY is the 2-digit year and DDD is the day of the year. The RASS virtual temperature data files are in the format tYYDDD.cns. Each record has the following header structure: Line 1 : Station Name RASS files Line 2 : RASS rev DeTect_2.0, WINDS files Line 2 : WINDS rev ATI 5.1 Line 3 : N latitude, W longitude, and site elevation (m) Line 4 : Date and begin time of consensus: yy mm dd hh mn ss plus # minutes to add to get UTC Line 5 : Consensus averaging time (minutes); number of beams; number of range gates Line 6 : Number of records required to make consensus (num) total number of records (tot) and the consensus window size (m/s) in the format: num:tot (window) RASS files Line 7 : no. of coded cells, no. of spec, pulse width (ns), and inter-pulse period (µs), WINDS files Line 7 : No. of coded cells, no. of spectra, pulse width (ns), and inter-pulse period (µs), each with a pair of values: first value is for oblique beams, second for vertical RASS files Line 8 : Full scale Doppler value (m/s) Delay to first gate (ns) Number of gates Spacing of gates (ns), WINDS files Line 8 : Full scale Doppler velocity (m/s), oblique and vertical Vertical correction applied to oblique beams? (0 = no, 1 = yes) Delay to first gate (ns), oblique and vertical Number of gates, oblique and vertical Spacing of gates (ns), oblique and vertical Line 9 : Azimuth and elevation (9s indicate vertical beam not used) RASS files Line 10, values : HT = Height above ground (km), T = Uncorrected virtual temperature consensus (deg C), Tc = Corrected virtual temperature consensus (deg C), W = Vertical wind consensus (9s indicate vertical beam not used, w-component, positive upward, m/s), CNT = Number of records that made consensus (for the 3 values in same order), SNR = Average signal to noise ratio (dB) of records in consensus (same order) WINDS files Line 10, values : HT = Height above ground (km), SPD = Wind speed (m/s), DIR = Wind direction (deg E of N from N), RAD = Radial velocities for each beam (m/s) in order given in azimuth and elevation line (positive toward radar; 9s indicate vertical beam not used, CNT = Number of records that made consensus, SNR = Average signal to noise ratio (dB) of records in consensus

{"wind speed and direction",temperature}↗

IM3 + EPRI Data Center Load Projections

This dataset contains scenarios of hourly total electricity demand with and without projected loads from data centers over the period 2022-2040. The root projections without data center demands are identical to those documented in Burleyson et al. 2024. In short, those projections encompass hourly electricity demands for 54 Balancing Authorities (BAs) in the United States across a range of eight of weather and socioeconomic scenarios. Refer to the root dataset and accompanying publication, Burleyson et al. 2025, for information about how those projections were generated. For this derivative dataset we used the base loads from the following scenarios: rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 The root load projections did not reflect the drastic expansion of data centers that has occurred in the last several years to support artificial intelligence and cloud computing. To reflect growth in data center demand, a second set of load projections were created in which we layered in additional data center load projections based on the data center load growth scenarios described in a 2024 report by the Electric Power Research Institute (EPRI): "Powering Intelligence: Analyzing Artificial Intelligence and Data Center Energy Consumption". The EPRI projections from the report are included in this dataset (EPRI_2024_Projections.xlsx). That report contained annual state-level data center load projections for four year-over-year growth rates for data center demands: Low (3.71% annual growth) Moderate (5% annual growth) High (10% annual growth) Higher (15% annual growth) To homogenize the load projections with and without data centers we had to get them to a common scale. The first step was to take the EPRI annual state-level data center energy consumption values and convert them to 8760-hr loads for each year. We did that by assuming a flat (e.g., not weather- or time-sensitive) load profile and distributing the data center loads in each state evenly across all hours in a year. From there the loads were downscaled from the state-level to the county-level using 2019 county-level populations as weights. Finally, the county-level hourly data center loads were summed to the BA-level using the county-to-BA mapping underpinning the root load projections. The net result is 16 (4 weather and socioeconomic scenarios crossed with 4 data center load growth scenarios) unique load projections for the period 2022-2040. The file format follows that of the root dataset with a single additional column "Scaled_TELL_BA_Load_with_DC_MWh" that contains the hourly loads with the added data center loads for a given BA-year-scenario combination. Please refer to the readme file in the root dataset for more information on the file format.

Burleyson, Casey [Pacific Northwest National Labor↗

Testing convolutional neural network based deep learning systems: a statistical metamorphic approach

Machine learning technology spans many areas and today plays a significant role in addressing a wide range of problems in critical domains,i.e., healthcare, autonomous driving, finance, manufacturing, cybersecurity,etc. Metamorphic testing (MT) is considered a simple but very powerful approach in testing such computationally complex systems for which either an oracle is not available or is available but difficult to apply. Conventional metamorphic testing techniques have certain limitations in verifying deep learning-based models (i.e., convolutional neural networks (CNNs)) that have a stochastic nature (because of randomly initializing the network weights) in their training. In this article, we attempt to address this problem by using a statistical metamorphic testing (SMT) technique that does not require software testers to worry about fixing the random seeds (to get deterministic results) to verify the metamorphic relations (MRs). We propose seven MRs combined with different statistical methods to statistically verify whether the program under test adheres to the relation(s) specified in the MR(s). We further use mutation testing techniques to show the usefulness of the proposed approach in the healthcare space and test two CNN-based deep learning models (used for pneumonia detection among patients). The empirical results show that our proposed approach uncovers 85.71% of the implementation faults in the classifiers under test (CUT). Furthermore, we also propose an MRs minimization algorithm for the CUT, thus saving computational costs and organizational testing resources.

Computer Science↗

EFFECT OF STERILIZATION IN SPACECRAFT DESIGN

Modern spacecraft design no longer involves only the Engineering Sciences. To get "the bugs" out of a system must now be taken literally to signify eradication of actual free-living microorganisms. A severe impact of a non-sterilized space vehicle could result in overt, reckless contamination, virtually eliminating a celestial body as a scientific base for the exobiological phases of space exploration. Recognizing the potentially damaging effects of septic launches, the National Aeronautics and Space Administration in December, 1960 authorized spacecraft sterilization and decontamination as National policy, thereby elevating the space effort onto a higher scientific plane. In the NASA directive, the basic reasons for sterilization were established, viz., "to preserve clues to the origin of life and of the universe, which may be hidden beneath the lunar strata or under the atmospheres of strange planets, to prevent inadvertent seeding of extraterrestrial surfaces by earth-life cultures, and to protect the earth from mutual contamination." In light of the foregoing, spacecraft sterilization should be regarded as the most significant single mission as well as design requirement of planetary space exploration --- especially if our objectives are primarily scientific.

Ranger project↗

SYNCHRONOUS-ORBIT COMMUNICATIONS SATELLITES

This paper will present (1) a discussion of some of the attractive features of a system using stationary or synchronous satellites, (2) some of the problem areas that we see in getting from where we are today to such a system, (3) a description of the NASA Syncom program, and (4) a very brief description of a more advanced synchronous satellite system.

Synchronous satellite↗

The cosmic ray anisotropy.

Cosmic ray anisotropy may be due to existence of trapped orbits in interplanetary field, sunward flux density gradient or Compton-Getting effect

RADIATION INTENSITY↗

Apollo 7 to 11 - Medical Concerns and Results

The goal of the Apollo Program is to land men on the moon and safely return them to earth. The medical task thus outlined required confirmation of the Gemini findings and definition and solution of any problems encountered in the four Apollo flights prior to the Apollo 11 lunar landing. The medical concerns included the following: 1. The effect of decreased red blood cell mass and decreased exercise capacity and of cardiovascular de conditioning on the ability of the crew to do lunar-surface activity; 2. The capability to work effectively in one-sixth the force of gravity and the energy cost of such work; 3. The ability to get adequate rest and sleep in flight and on the lunar surface; 4. The prevention of preflight, inflight, and post-flight illness by proper preventive medicine; 5. The possible development of motion sickness of vestibular origin; 6. The conduct of a post-flight quarantine of crew and lunar samples. The results of the Apollo 7 to 11 missions, demonstrating the ability of man to handle this difficult task and the environment successfully, are discussed in detail and are related to the future of manned flight.

Berry, C. A.↗

A self-consistent model for Jupiter's radiation belts

The solar wind is assumed to be a sufficient source for the radiation belts of Jupiter, with radial diffusion as the process for getting the solar wind fluxes into the inner regions of the radiation belts. The process of a radial diffusion source is combined with pitch-angle diffusion losses to estimate an upper limit to the proton and electron fluxes. The upper limit to the integral proton and electron fluxes in the outer zone scale as L to the -6th power throughout the outer zone, assuming that the magnetic moment is conserved. It is important that these fluxes are above the stably trapped flux levels in the outer regions of the belts; the instability will work because the energies of the particles are above the critical value required.

Thorne, R. M.↗

Estimation of sea surface temperature from remote measurements in the 11-13 micron window region

The Nimbus-4 IRIS data was examined in the spectral region 775 to 1250/cm (8-13 microns) for useful information to determine the sea surface temperature. The high spectral resolution data of IRIS was degraded to low resolution by averaging to simulate a multi-channel radiometer in the window region. These simulated data show that within the region 775-975/cm (12.9-10.25 microns) the brightness temperatures are linearly related to the absorption parameters. Such a linear relationship is observed over cloudy as well as clear regions and over a wide range of latitudes. From this linear relationship it is feasible to correct for the atmospheric attenuation and get the sea surface temperature, accurate to within 1 K, in a cloud free field of view. The information about the cloud cover is taken from the TV pictures and BUV albedo measurements on board the Nimbus-4 satellite.

Prabhakara, C.↗

TLD measurements of gamma heating in heavy elements.

Measurements and calculations of gamma heating in polyethylene and lead containers were done and compared. The objective was to provide a workable method of getting good values for gamma heating in in-pile experiments containing materials of high atomic numbers. It was inferred that a combination of thermoluminescent dosimeter measurements, using Bragg-Gray theory, with photon transport calculations using the ANISN computer program, would meet this objective.

Reilly, H. J.↗