Searching for Magnetic Monopoles with Earth’s Magnetic Field
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The fission TPC was designed for precision fission cross section ratio measurements. The high-fidelity 3D particle tracking data is used to conduct a detailed analysis of the uncertainty contributions. In this contribution a brief overview of the instrument was presented. The results of the 238 U(n,f)/ 235 U(n,f) cross section ratio measurement published in [Phys. Rev C 97, 034618 (2018)] were reviewed and the preliminary status of the 239 Pu(n,f)/ 235 U(n,f) cross section ratio measurement was shown.
In this proposal, we present a novel method of tracking the rheological regimes activated during impact cratering events that will allow researchers to gain new insights into cratering mechanics. Rheology describes the stress-strain response of rocks to different conditions. Planetary impact cratering events often occur on too large of a scale to be feasibly captured in controlled experiments. Instead, these dynamic events are primarily studied using multi-physics codes equipped with complex material models that enable calculations of the impact event at scale. However, determining which physical processes are required for the problem of interest is challenging. Because the dominant rheological regimes change with space and time during crater formation, it is difficult to link numerical simulations with observable features of craters at the end of the event. The basis of this work is the implementation of numerical flags that track the activation of each rheological regime throughout impact simulations. We demonstrate this with the ‘Rock Model’ implemented in the CTH shock-physics code. We will use this rheology tracking method to ’zoom in’ on a specific region within an event and track the conditions the rock experiences over time. This work will develop community benchmarks to validate and distribute our implemented rheological models. We will focus on improving the melt models used by the planetary impact modeling community by developing an EOS-aware rheological transition from solid to melt. Through analysis of cell and tracer-particle based tracking data, we will study the effects of different rheologies on modeled outcomes, particularly on the volume and distributions of impacts melts. We will also use this method to link observable features with the rheological mechanisms responsible for them. The deliverables (peer-reviewed papers) from the proposed work are (i) tests of the implemented rheologic processes and demonstrations of the tracking flags; (ii) the first calculations of the spatio-temporal evolution of the dominant rheologies during impact cratering events; and (iii) application to delivery of impactor iron during basin-scale impacts.
In neutrino-nucleus interactions, Final State Interactions (FSI) can cause nucleons to scatter or form heavier composite particles like deuterons and tritons. Observing these particles would provide direct evidence of FSI and help improve interaction models, which is essential for precise neutrino energy reconstruction in future experiments like DUNE. Our Goal: Develop an analysis to detect particles heavier than protons in ICARUS data to better constrain FSI models.
A preliminary analysis of magnetometer data from Apollo 15 subsatellite is concluded. Remanent magnetization is a characteristic property of the moon, and the distribution is such that a complex pattern is produced. A mapping of the distribution is feasible with the present experiment. Lunar induction fields produced by transients in interplanetary magnetic field are detectable at satellite orbit. Magnetometer data will provide estimates of the latitude and longitude dependences of interior conductivity. The plasma void extends to some altitude below the satellite orbit and probably the lunar surface. Solar wind near the limbs is usually strongly disturbed.
Techniques for air pollution source identification are reviewed, and some results obtained with them are evaluated. Described techniques include remote sensing from satellites and aircraft, on-site monitoring, and the use of injected tracers and pollutants themselves as tracers. The use of a large number of trace elements in ambient airborne particulate matter as a practical means of identifying sources is discussed in detail. Sampling and analysis techniques are described, and it is shown that elemental constituents can be related to specific source types such as those found in the earth's crust and those associated with specific industries. Source identification sytems are noted which utilize charged particle X-ray fluorescence analysis of original field data.
Proton and electron phase space density profiles are constructed from an analysis of Voyager 2 low-energy charged particle data from the magnetosphere of Uranus. The Uranus proton profiles reveal an approximately exponential decline with decreasing radius for L less than about 9 in a relatively dense thermal plasma region with intense plasma wave activity. Among the distributed loss mechanisms at Uranus are satellite sweeping, wave-particle interactions, and charge exchange of protons with an extended hydrogen corona.
An integrated fiber optic probe, comprising a monomode optical fiber fusion spliced to a short length of a graded-index multimode fiber, is fabricated for use as a coherent receiver in dynamic light scattering. The multimode fiber is cleaved to provide a gradient-index fiber lens with a focal length of 125 microns and an f-number close to unity. An integrated fiber receiver is used to measure the intensity-intensity autocorrelation data from a 0.05 percent by weight concentration of an aqueous suspension of polystyrene latex spheres. Analysis of 100 independent data sets indicates that the particle size can be recovered with an accuracy of +/- 1 percent.
The Dynamic Science investigation on the STARDUST mission has been described previously. The data delivered by the STARDUST Project is multifold, but basically it consists of radio Doppler data from the Deep Space Network (DSN) and attitude control data (ACS) from the spacecraft. Doppler data were successfully recorded by JPL's Navigation System (closed-loop data) and also by its Radio Science System (open-loop data) at DSN stations DSS43 near Canberra Australia and at DSS14 at Goldstone California. Attitude control data were also successfully delivered to the Dynamic Science Team. Here we describe a preliminary analysis of the data. Beyond a closest approach distance of 150 km, a Doppler detection of a the Wild 2 nucleus mass was not expected. The current best estimate of the closest approach distance is 236.4 km, and as expected, any mass signal in the Doppler data is hopelessly buried in the noise. We have attempted to fit the data to a mass model with no success. However, analysis of the Doppler data and the ACS data for particle impacts on the spacecraft's Whipple shields is in progress, and will be reported at the meeting. The DSS43 closed-loop Doppler residuals are plotted as a function of time from the current best estimate of the time of Wild 2 closest approach, 2 January 2004, 19:43:11.7 UTC, Earth-receive time at the station.
As the Artemis Program streams forward, organizations of scientists and engineers across the country have been coming together to solve the complex network of problems to once again achieve the milestone of successfully touching down on the moon. This project is no exception, and it has been an honor to work with the Electrostatics and Surface Physics Laboratory (ESPL), a lab within the Exploration Research and Technology Programs’ Spaceport Technologies Office (UB-G) located at the National Aeronautics and Space Administration at Kennedy Space Center (NASA KSC). The authors and their mentor James R. Phillips III3, in tandem with researchers from the Astrodynamics and Space Robotics Laboratory (ASRL) at the University of Central Florida (UCF), have been working on creating a state-of-the-art (SOA) granular gas dynamics model for particulate contamination prevention and mitigation purposes. In essence, the underlying objective for this project is to more accurately model the resulting electrodynamic interactions between lunar regolith grains with applications to dust mitigation and rocket engine plume surface interactions. To achieve this goal, the team has been expanding upon existing open source classical molecular dynamics code developed by Sandia National Laboratories (SNL). The following reports on the details of the problem at hand as well as the contributions that the authors have made towards resolution, including but not limited to the encoding of physical attributes and interactions for non-spherical polydisperse particle distributions within various bed geometries and preemptive data analysis implementations. Significant data analysis processes were utilized, and several original algorithms were created to perform critical evaluations, resulting in only a 0.006% error in discrepancy.
Introduction The SpaceX Dragon 1 spacecraft performed 20 resupply missions to the International Space Station (ISS) between 2012 and 2020. After each mission, a team from the NASA John-son Space Center Hypervelocity Impact Technology Group inspected each Dragon 1 capsule for hypervelocity impact damage features. Data from these inspections are collected into a database that includes impact feature dimensions as well as the location on the vehicle. Additional details on the type and size of particle that produced the damage site are provided when sampling data and definitive spectroscopic analysis results are available. Observation data can be compared with impact estimates from risk assessment codes as a check on the micrometeoroid and orbital debris (MMOD) environment predictions. Scope A general description of the areas of the vehicle that were inspected are provided as well as mission details such as exposure duration and launch dates. The paper documents the general inspection procedure for collection of data and the post inspection data analysis process. It also provides details of the observation data collected as well as the results of analysis of intact samples collected for spectroscopic analysis to discern the source of the impacting particle. A comparison between observed impacts and the expected number of damage features calculated by Bumper 3 with the latest MMOD environments are also presented. Findings Statistics on the >300 impact features documented in the database will provide insight into the depth to diameter ratios and other relationships. The quality of the comparison between the observations and code predictions are dependent on several factors. The paper provides de-tails of each of these variables. (1) Damage equations (2) Impact condition assumptions a. Projectile density b. Impact speed c. Impact angle (3) Analysis results a. pre-flight vs. on-orbit damage b. MMOD vs. non-MMOD Conclusions and Recommendations The ISS visiting vehicle impact database is an ongoing project. SpaceX provides crew rotations as well as resupply and cargo return with the Dragon 2 spacecraft. Sierra Space is manifested for resupply and cargo return services starting in 2023. Boeing is expected to provide crew rotations as well. All of these spacecraft will provide additional opportunities for post flight MMOD inspections of space exposed hardware. Acknowledgements Over the course of the inspection campaign various personnel at the SpaceX Texas Test Site in McGregor and at the SpaceX headquarters in Hawthorne CA provided valuable support to this activity.
Magnetic field alignments of spacecraft over large distances in the heliosphere are rare and are usually very limited in duration. Cruise phases of planetary transfers, however, are an exception to this rule, given the Hohmann-Parker effect. The transfer of the MAVEN s/c in 2014 is one such example. Multiple (~10) solar particle events occurred and were detected at both MAVEN and the Wind s/c, originating from solar activity near the foot points of both s/c. We show initial analysis results of the data collected by the solar wind and energetic particle instruments on both s/c while they were more than 0.2 AU apart, but practically Parker field aligned. Using a 1D model, we present initial simulations in qualitative agreement with energetic electron measurements. Next step is to implement the 2D model with approximate particle release from the Sun, and transport durations between Sun, Earth, and MAVEN. We will discuss implications for this data-model comparison, including the possibility to constrain particle scattering inside 1 AU, as well as its radial dependence between Earth and MAVEN.
Solar particle propagation in magnetic fields revealing anisotropies in proton flux analyzed from Pioneer VI space probe data
Abstract According to recent field studies, almost half of the New Particle Formation (NPF) events occur aloft, in a residual layer, near the top of the boundary layer. Therefore, measurements of the meteorological parameters, precursor gas concentrations, and aerosol loadings conducted at the ground level are often not representative of the conditions where the NPFs take place. This paper presents new measurements obtained during the Turbulent Flux Measurements of the Residual Layer Nucleation Particles, conducted at the Southern Great Plains research site. Vertical turbulent fluxes of 3–10 nm‐sized particles were measured using a sonic anemometer and two condensation particle counters with nominal cutoff diameters of 3 nm and 10 nm mounted at the top of the 10‐m telescoping tower. Aerosol number size distribution (5–300 nm) was determined through the ground‐based Scanning Mobility Particle Sizers. The size selected (15–50 nm) particle hygroscopicity was derived with the Humidified Tandem Differential Mobility Analyzer. The ground level observations were supplemented by vertically‐resolved measurements of horizontal and vertical wind speeds and aerosol backscatter. The data analysis suggests that (a) turbulent flux measurements of 3–10 nm particles can distinguish between near‐surface and residual‐layer small particle events; (b) sub‐50 nm particles had a hygroscopicity value of 0.2, suggesting that organic compounds dominate atmospheric nanoparticle chemical composition at the site; and (c) current methodologies are inadequate for estimating the dry deposition velocity of sub‐10 nm particles because it is not feasible to measure particle concentration very near the surface, in the diffusion sublayer.
Abstract. Ice-nucleating particles (INPs) are an essential class of aerosols found worldwide that have far-reaching but poorly quantified climate feedback mechanisms through interaction with clouds and impacts on precipitation. These particles can have highly variable physicochemical properties in the atmosphere, and it is crucial to continuously monitor their long-term concentration relative to total ambient aerosol populations at a wide variety of sites to comprehensively understand aerosol–cloud interactions in the atmosphere. Hence, our study applied an in situ forced expansion cooling device to measure ambient INP concentrations and test its automated continuous measurements at atmospheric observatories, where complementary aerosol instruments are heavily equipped. Using collocated aerosol size, number, and composition measurements from these sites, we analyzed the correlation between sources and abundance of INPs in different environments. Toward this aim, we have measured ground-level INP concentrations at two contrasting sites, one in the Southern Great Plains (SGP) region of the United States with a substantial terrestrially influenced aerosol population and one in the Eastern North Atlantic Ocean (ENA) region with a primarily marine-influenced aerosol population. These measurements examined INPs mainly formed through immersion freezing and were performed at a ≤ 12 min resolution and with a wide range of heterogeneous freezing temperatures (Ts above −31 °C) for at least 45 d at each site. The associated INP data analysis was conducted in a consistent manner. We also explored the additional offline characterization of ambient aerosol particle samples from both locations in comparison to in situ data. From our ENA data, on average, INP abundance ranges from ≈ 1 to ≈ 20 L−1 (−30 °C ≤ T ≤ −20 °C) during October–November 2020. Backward air mass trajectories reveal a strong marine influence at ENA with 75.7 % of air masses originating over the Atlantic Ocean and 96.6 % of air masses traveling over open water, but analysis of particle chemistry suggests an additional INP source besides maritime aerosols (e.g., sea spray aerosols) at ENA. In contrast, 90.8 % of air masses at the SGP location originated from the North American continent, and 96.1 % of the time, these air masses traveled over land. As a result, organic-rich SGP aerosols from terrestrial sources exhibited notably high INP abundance from ≈ 1 to ≈ 100 L−1 (−30 °C ≤ T ≤ −15 °C) during October–November 2019. The probability density function of aerosol surface area-scaled immersion freezing efficiency (ice nucleation active surface site density; ns) was assessed for selected freezing temperatures. While the INP concentrations measured at SGP are higher than those of ENA, the ns(T) values of SGP (≈ 105 to ≈ 107 m−2 for −30 °C ≤ T ≤ −15 °C) are reciprocally lower than ENA for approximately 2 orders of magnitude (≈ 107 to ≈ 109 m−2 for −30 °C ≤ T ≤ −15 °C). The observed difference in ns(T) mainly stems from varied available aerosol surface areas, Saer, from two sites (Saer,SGP > Saer,ENA). INP parameterizations were developed as a function of examined freezing temperatures from SGP and ENA for our study periods.
With the renewed commitment from NASA and other commercial entities for a presence on the Moon, the importance of understanding the characteristics of lunar regolith and how to utilize it have become the target of increasing scrutiny. Much of what is known about lunar regolith was collected during and immediately after the Apollo program, however, analytical techniques and instrumentation have advanced in leaps and bounds in the subsequent decades. Specifically, dynamic image analysis systems have advanced to the point that millions of particles can have morphological characteristics automatically determined in relatively short time frames. Particle morphological data was collected on several lunar samples and a pair of widely used regolith simulants to ascertain the accuracy of these simulants and to explore statistical analysis methods of these large datasets. It is found that these morphology datasets can vary widely depending on the particle size of the particles, and simple averaging of the data skews the results heavily towards the numerically abundant size ranges, the fines. Different reporting methods are suggested to ameliorate these problems. When applied to the lunar regolith, the particles are noted to be less morphologically complex than initially suspected. Compared to the lunar material, the simulants are found to contain some more morphological variability and angular grains. Such difference is likely due to the wildly different comminution processes that these different powder systems are subjected to.
Optimized operation of fusion devices demands detailed understanding of plasma transport, a problem that must be addressed with advances in both measurement and data analysis techniques. In this work, we adopt Bayesian inference methods to determine experimental particle transport, leveraging opportunities from high-resolution He-like ion spectra in a tokamak plasma. The Bayesian spectral fitting code is used to analyze resonance (w), forbidden (z), intercombination (x, y), and satellite (k, j) lines of He-like Ca following laser blow-off injections on Alcator C-Mod. This offers powerful transport constraints since these lines depend differently on electron temperature and density, but also differ in their relation to Li-like, He-like, and H-like ion densities, often the dominant Ca charge states over most of the C-Mod plasma radius. Using synthetic diagnostics based on the AURORA package, we demonstrate improved effectiveness of impurity transport inferences when spectroscopic data from a progressively larger number of lines are included.
Optimized operation of fusion devices demands detailed understanding of plasma transport, a problem that must be addressed with advances in both measurement and data analysis techniques. In this work, we adopt Bayesian inference methods to determine experimental particle transport, leveraging opportunities from high-resolution He-like ion spectra in a tokamak plasma. The Bayesian spectral fitting code is used to analyze resonance (w), forbidden (z), intercombination (x, y), and satellite (k, j) lines of He-like Ca following laser blow-off injections on Alcator C-Mod. This offers powerful transport constraints since these lines depend differently on electron temperature and density, but also differ in their relation to Li-like, He-like, and H-like ion densities, often the dominant Ca charge states over most of the C-Mod plasma radius. Using synthetic diagnostics based on the AURORA package, we demonstrate improved effectiveness of impurity transport inferences when spectroscopic data from a progressively larger number of lines are included.