Search for: All records

{"Abstract":["This data set presents processed near-bottom data from the Sentry AUV standard sensor package, with corrected NDSF navigation, for Sentry dives S597-S606 at the southern East Pacific Rise. The files contain the NDSF SCC data in Excel spreadsheet format, with one file per dive. The data files were generated as part of the projects called "Are Low-Temperature Hydrothermal Vents an Important but Overlooked Source of Stabilized Dissolved Iron to the Ocean?" and "Finding hydrothermal chimneys along the southern East Pacific Rise with machine learning approaches to AUV-based sonar data". Funding was provided by the U.S. National Science Foundation under awards OCE17-55571, OCE17-56402 and OCE20-06265."]} 

The electron density of the solar corona is a fundamental parameter in many areas of solar physics. Traditionally, routine estimates of coronal density have relied exclusively on white-light observations. However, these density estimates, obtained by inverting the white-light data, require simplifying assumptions, which may affect the robustness of the measurements. Hence, to improve the reliability of coronal density measurements, it is highly desirable to explore other complementary methods. In this study, we estimate the coronal electron densities in the middle corona, between approximately 1.7 and 3.5R_⊙, using low-frequency radio observations from the recently commissioned Long Wavelength Array at the Owens Valley Radio Observatory (OVRO-LWA). The results demonstrate consistency with those derived from white-light coronagraph data and predictions from theoretical models. We also derive a density model valid between 1.7 and 3.5r_⊙, given by $\rho \left(r^{\prime }\right)=1.27{r{}^{\prime }}^{-2}+29.02{r{}^{\prime }}^{-4}+71.18{r{}^{\prime }}^{-6}$, where $r^{\prime }=r/R_{\odot }$, withrthe heliocentric distance. OVRO-LWA is a solar-dedicated radio interferometer that provides science-ready images with low latency, making it well suited for generating regular and independent estimates of coronal densities to complement existing white-light techniques. 

Abstract Decades of solar coronal observations have provided substantial evidence for accelerated particles in the corona. In most cases, the location of particle acceleration can be roughly identified by combining high spatial and temporal resolution data from multiple instruments across a broad frequency range. In almost all cases, these nonthermal particles are associated with quiescent active regions, flares, and coronal mass ejections (CMEs). Only recently, some evidence of the existence of nonthermal electrons at locations outside these well-accepted regions has been found. Here, we report for the first time multiple cases of transient nonthermal emissions, in the heliocentric range of ∼3–7R_⊙, which do not have any obvious counterparts in other wave bands, like white-light and extreme ultraviolet. These detections were made possible by the regular availability of high dynamic-range low-frequency radio images from the Owens Valley Radio Observatory’s Long Wavelength Array. While earlier detections of nonthermal emissions at these high heliocentric distances often had comparable extensions in the plane of sky, they were primarily associated with radio CMEs, unlike the cases reported here. Thus, these results add on to the evidence that the middle corona is extremely dynamic and contains a population of nonthermal electrons, which is only becoming visible with high dynamic-range low-frequency radio images. 

Abstract Routine measurements of the magnetic field of coronal mass ejections (CMEs) have been a key challenge in solar physics. Making such measurements is important both from a space weather perspective and for understanding the detailed evolution of the CME. In spite of significant efforts and multiple proposed methods, achieving this goal has not been possible to date. Here we report the first possible detection of gyroresonance emission from a CME. Assuming that the emission is happening at the third harmonic, we estimate that the magnetic field strength ranges from 7.9 to 5.6 G between 4.9 and 7.5R_⊙. We also demonstrate that this high magnetic field is not the average magnetic field inside the CME, but most probably is related to small magnetic islands, which are also being observed more frequently with the availability of high-resolution and high-quality white-light images. 

Identifying the locations of hydrothermal chimneys across mapped areas of seafloor spreading ridges unlocks the ability to research questions about their correlations to geology, the cooling of the lithosphere, and deep-sea biogeography. We developed a Chimney Identification Tool (CIT) that utilizes a Convolutional Neural Network (CNN) to classify 1 m gridded AUV bathymetry and identify the locations of hydrothermal vent chimneys. A CNN is a type of Machine-Learning model that is able to classify raster data based on the shapes and textures in the input, making it ideal for this task. The criteria that have been used in previous manual classifications of chimneys have focused on the round base and spire shape of the features, and are not easily quantifiable. Machine-Learning techniques have previously been implemented with sonar data to classify seafloor geology, but this is the first application of these methods to hydrothermal systems. In developing the CIT, we compiled the bathymetry data from two rasters from the Endeavor Ridge—each gridded at a 1 m resolution—containing 34 locations of known hydrothermal chimneys, and from the 92° W segment of the Galapagos Spreading Center (GSC) containing 14. The CIT produced a primary group of outputs with 96% agreement with the manual classification; moreover, it correctly caught 29 of the 34 known chimneys from Endeavor and 10 of the 14 from the GSC. The CIT is trained to identify features with the characteristic shape of a hydrothermal vent chimney; therefore, it is susceptible to the misclassification of unusually shaped cases, given the limited training data. As a result, to provide the option of having a more inclusive application, the CIT also produced a secondary group of output locations with 61% agreement with the manual classification; moreover, it caught three of the four additional known chimneys from the GSC and four of the five from Endeavor. The CIT will be used in future investigations where an inventory of individual chimneys is important, such as the cataloguing of off-axis hydrothermal venting and the investigation of chimney distribution in connection to seafloor eruptions. 

Eastern Pacific Ocean swath bathymetry was collected with a Simrad EM124 multibeam sonar system on R/V Roger Revelle during PLUME RAIDERS cruise RR2106 in 2021 (investigators Joseph Resing, Scott White, and Chris German). The files were processed in MB-System following standard protocol for navigation, attitude, sound velocity, and bad beams. The data files are in MB-System-compatible format (mbio format 261) and contain processed swath bathymetry and acoustic backscatter data. The files were generated as part of the projects called Finding hydrothermal chimneys along the southern East Pacific Rise with machine learning approaches to AUV-based sonar data; and, Are Low-Temperature Hydrothermal Vents an Important but Overlooked Source of Stabilized Dissolved Iron to the Ocean? Funding was provided through NSF grants OCE20-06265, OCE17-55571, and OCE17-56402. 

The oceanic crust consists mostly of basalt, but more evolved compositions may be far more common than previously thought. To aid in distinguishing rhyolite from basaltic lava and help guide sampling and understand spatial distribution, we constructed a classifier using neural networks and fuzzy inference to recognize rhyolite from its lava morphology in sonar data. The Alarcon Rise is ideal to study the relationship between lava flow morphology and composition, because it exhibits a full range of lava compositions in a well-mapped ocean ridge segment. This study shows that the most dramatic geomorphic threshold in submarine lava separates rhyolitic lava from lower-silica compositions. Extremely viscous rhyolite erupts as jagged lobes and lava branches in submarine environments. An automated classification of sonar data is a useful first-order tool to differentiate submarine rhyolite flows from widespread basalts, yielding insights into eruption, emplacement, and architecture of the ocean crust.