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Eftyhia Zesta

Publications and source records attributed to Eftyhia Zesta.

14 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Rocket Measurements of Electron Energy Spectra From Earth's Photoelectron Production Layer

Photoelectrons are crucial to atmospheric physics. They heat the atmosphere, strengthen planetary ambipolar electric fields, and enhance the outflow of ions to space. However, there exist only a handful of measurements of their energy spectrum near the peak of photoproduction. We present calibrated energy spectra of pristine photoelectrons at their source by a prototype Dual Electrostatic Analyzer (DESA) instrument flown on 11 July 2021 aboard the Dynamo-2 sounding rocket (NASA № 36.357). Photopeaks arising from 30.4 nm He-II spectral line were observed throughout the flight above 120 km. DESA also successfully resolved the rarely observed N2 absorption feature. Below 10 eV observations were in good agreement with the GLOW suprathermal electron. Above 10 eV fluxes substantially deviated from the model by as much as an order of magnitude.

Glyn A. Collinson↗
NASA NTRS2022

Single-Event Effect Testing of the PNI RM3100 Magnetometer for Space Applications

The results of a destructive single-event effect susceptibility radiation test of the PNI RM3100 magnetometer sensor, specifically the MagI 2 C ASIC (application-specific integrated circuit) on the sensor board are presented. The sensor is a low-resource commercial off-the-shelf (COTS) magneto-inductive magnetometer. The device was monitored for destructive events and functional interruptions during exposure to a heavy ion beam at the Lawrence Berkeley National Laboratory's 88′′ Cyclotron. The RM3100 did not experience any destructive single-event effects when irradiated to a total fluence of 1.4 × 10 7 cm −2 at an effective linear energy transfer (LET) of 76.7 MeV cm 2 mg −1 while operated at nominal voltage (3.3 V) and elevated temperature (85 ∘C). When these results are combined with previous total ionizing dose tests showing no failures up to 150 kRad (Si), we conclude that the PNI RM3100 is extremely radiation tolerant and can be used in a variety of space environments.

Mark B. Moldwin↗
NASA NTRS2022

Investigation of the Differences in Onset Times for Magnetically Conjugate Magnetometers

We have identified nearly 1,000 onsets using two pairs of hemispheric conjugate ground magnetometers where the onset is defined based on a sharp decline in the H component of the magnetic field at a ground magnetometer station. Specifically, we used the pair of stations at West Antarctica Ice Sheet Divide and Sanikiluaq, Canada; Syowa, Antarctica; and Tjörnes, Iceland. While the onset time in the southern hemisphere is identified by eye, the value of the differences in the onset time between the northern and southern hemispheres is determined using cross covariance. We observe differences in the onset time between the two hemispheres as large as several minutes, but 53% of the events show no difference in the onset time. Using statistics, we show that the largest differences in onset time are associated with the summer and winter seasons and when the IMF By value is limited between 0.5 and 2.5 nT, which is the IMF By range when the local time difference between the northern and southern hemisphere foot points is the smallest. The results indicate that ionospheric conductivity associated with solar illumination plays a role in the differences in onset time between the northern and southern hemisphere when only non-zero differences in onset time are considered. We validate these results with two other less robust methods. The median value of the differences in onset time indicates that the onsets occur ∼23 s earlier in the winter hemisphere than that in the summer hemisphere. It has been reported that the time difference between the start of the substorm in the magnetotail and the observed auroral break up (substorm auroral onset) in the ionosphere is 30 s to 2 min in the current disruption model and the near earth neutral line model, respectively. Our results may be of interest to those two models.

James M. Weygand↗
NASA NTRSDate not supplied

Dione: A Pathfinder Mission for Understanding the Iono-sphere-Thermosphere Responses to Magnetospheric Forcing

Dione is a NASA small satellite prototype for future constellations that in different configurations will provide global and localized measurements of Ionosphere-Thermosphere responses to Magnetospheric energy input in a variety of scales, enabling their better forecast and prediction. This is done with a comprehensive sensor package which includes four instruments: a miniaturized fluxgate magnetometer providing magnetic field measurements with two sensor heads, an ion sensor that measures vector plasma drifts and deduce the in-situ electric fields, an electrostatic analyzer that measures precipitating electrons and up-going secondary electrons, and a neutral mass spectrometer measuring total neutral density and composition. Dione is 3-axis stabilized in a circular low-earth (400-600 km), high-inclination orbit, traversing the auroral precipitation and high latitude currents in every orbit. This paper focuses on the mission-level architecture, but provides contextual information on the science objectives, instruments, and space-craft.

Jaime Esper↗
NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗
NASA NTRSDate not supplied

Decoding the energy inputs that drive the upper atmosphere with NASA’s Geospace Dynamics Constellation (GDC)

NASA’s Geospace Dynamics Constellation (GDC) mission will study the effect of space weather phenomena on Earth’s upper atmosphere ion and neutral gases and, in turn, their impact on human assets. GDC will provide multi-point observations to determine how the Earth’s upper atmosphere is driven to dynamic changes and extremes by the solar wind. Magnetospheric energy input, both kinetic and electromagnetic, will be captured by the Comprehensive Auroral Precipitation Experiment (CAPE), the Thermal Plasma Sensor (TPS), and the Near Earth Magnetometer Instrument in a Small Integrated System (NEMISIS). CAPE captures the complete kinetic energy inputs with observations of the precipitating and upgoing electrons and precipitating ions. TPS captures part of the electromagnetic energy inputs with observations of the thermal plasma velocity that map E-field inputs, and NEMISIS captures the other part of the electromagnetic energy input with measurements of magnetic field variations due to field aligned currents. We use similar observations from the Defense Meteorological Space Platform (DMSP) satellites to demonstrate the balance between the different energy inputs (electron and ion Joule heating and Poynting flux) during the different phases of storms. We explore the local time and spatial structure and the impact they have on atmospheric density and winds with simulations of the Global Ionosphere and Thermosphere Model (GITM) and demonstrate how GDC will enable us to reconstruct those energy inputs locally and regionally

Eftyhia Zesta↗
NASA NTRSDate not supplied

Temporal and Spatial Dynamics of Nitric Oxide Production at High Latitudes During an ICME-driven Storm on Dec. 14, 2006

During geomagnetic storms driven by interplanetary coronal mass ejections (ICMEs), Earth’s upper atmosphere neutral mass density is directly driven by the geomagnetic storm cycle from intense driving and subsequent recovery. The neutral density heating and cooling times are shorter for stronger storms and longer for weaker storms. The influx of energy (Joule Heating and precipitation) into Earth’s upper atmosphere during the storm main phase allows for the enhanced production of nitric oxide (NO) at high latitudes, which in turn cools the thermosphere by radiating away excess energy. As a result, greater NO production results in quicker thermospheric cooling. Yet while the production of NO on a global scale has been examined in detail, the space and time evolution of NO is not well-known. In this study, we investigate the effects of a particular ICME-driven storm, which occurred on Dec. 14th, 2006, on the production of NO at high latitudes. We compare NO measurements from the Thermosphere, Ionosphere, Mesosphere Dynamics (TIMED) spacecraft to neutral mass density measurements from the Challenging Minisatellite Payload (CHAMP) spacecraft and find that the impact of the shock prior to the storm, not just the onset of the storm itself, is responsible for an increase in NO production. We also observe a dawn-dusk asymmetry in high-latitude NO production and discuss how the geometry of the solar wind driver may play a role in creating this asymmetry. Finally, we discuss how the improved hemispheric and local time coverage provided by the GDC and DYNAMIC missions will enable more robust studies of the spatiotemporal evolution of NO during storms. Since NO is an important factor in regulating the neutral density of the thermosphere, such studies will be relevant for more accurate orbital drag calculations.

Kevin Delano↗
NASA NTRSDate not supplied

Enabling Intelligent Data Downlink Prioritization of In-Situ Observations through Generalizable and Computationally Inexpensive Anomaly Detection

High-fidelity measurements of magnetic fields and other observed properties, such as energetic particle fluxes, are a necessary component to our understanding of the highly dynamic near-Earth space environment. As our desire to study smaller-scale phenomena such as shocks and dipolorizations has increased, we have been driven to take and telemeter measurements at higher cadences. Unfortunately, many missions are unable to downlink all their captured data due to the well-known data transmission bottleneck at the DSN. These missions must then prioritize their high-cadence data such that the most scientifically useful intervals are transmitted. One simple prioritization technique uses the spacecraft position to telemeter data from only the region of interest. Although easy to implement, this method does not leverage the available scientific data and can omit intervals of useful scientific data when they lie outside the region of interest. The Magnetospheric Multiscale Mission (MMS) uses mission-specific parameterization of several data products to automatically prioritize scientifically useful intervals. Then, MMS verifies the automatically selected intervals by having a domain expert manually select intervals for downlink. The overall complexity required by this technique make it prohibitive for deployment on low-cost platforms (i.e., CubeSats) or on future missions featuring large constellations of satellites such as the Geospace Dynamics Constellation (GDC). We present preliminary results for a simple, generic, and data-driven method of downlink prioritization for magnetic field (and other) measurements. Specifically, Principal Components Analysis (PCA) and One-Class Support Vector Machines (OC-SVMs) are used to detect intervals containing anomalous activity, which can then be prioritized for subsequent downlink. The computational simplicity of this algorithm makes it an excellent candidate for implementation on spaceflight hardware, as well as provide generalizability to a broad range of missions and data products. Initial analysis of this technique has been performed using magnetic field measurements from the Magnetospheric Multiscale Mission and CASSIOP, where it automatically identified scientifically interesting intervals containing Alfvén waves and EMIC activity.

Matthew G. Finley↗
NASA NTRSDate not supplied

The HERMES Payload for Gateway: Heliophysics Enabled by Lunar Exploration

At launch, the first two modules of the Gateway space station will carry external payloads from NASA and ESA designed to observe the space environment. The NASA payload is the Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES). The HERMES instrumentation includes an ion mass spectrometer, an electron electrostatic analyzer, a proton and electron telescope for energetic particles, and a set of magnetometers. After an approximately one year transit to the Moon, HERMES will begin a science campaign that addresses heliophysics and space-weather objectives. Analyses of the in-situ measurements from HERMES will leverage observations from other Heliophysics spacecraft missions to enable multipoint studies of structure in the solar wind and in the magnetotail. Gateway’s polar lunar orbit is advantageous for this purpose. HERMES also is a pathfinder for future payloads on human-exploration vessels, for which there will be pragmatic interest in the variable radiation environment. Thus, with observations enabled by the ARTEMIS Program, HERMES is expected to be enabling of future exploration missions. Additionally, although HERMES objectives have a space-weather focus, the measurements also can be useful for studies of the Moon. Data, algorithms, calibrations, and related software produced by the project will be fully open and accessible through a Science Operations Center. In this presentation we provide an overview of science plans, including expectations for collaboration with other HSO missions and with international partners. Plain-Language Summary: The HERMES scientific payload will be attached to the HALO module of the Artemis Program's Gateway lunar outpost. HERMES will observe space weather conditions from lunar orbit. The data will be freely available for studies of the Moon and its space environment.

William R Paterson↗
NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗