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H. K. Connor

Publications and source records attributed to H. K. Connor.

4 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Mshpy23: A User-Friendly, Parameterized Model of Magnetosheath Conditions

Lunar Environment heliospheric X-ray Imager (LEXI) and Solar wind – Magnetosphere - Ionosphere Link Explorer (SMILE) will observe magnetosheath and its boundary motion in soft X-rays for understanding magnetopause reconnection modes under varioussolar wind conditions after their respective launches in 2024 and 2025. Magnetosheath conditions, namely, plasma density, velocity, and temperature, are key parameters for predicting and analyzing soft X-ray images from the LEXI and SMILE missions. We developed a user-friendly model of magnetosheath that parameterizes number density, velocity, temperature, and magnetic field by utilizing the global Magnetohydrodynamics (MHD) model as well as the pre-existing gas-dynamic and analytic models. Using this parameterized magnetosheath model, scientists can easily reconstruct expected soft X-ray images and utilize them for analysis of observed images of LEXI and SMILE without simulating the complicated global magnetosphere models. First, we created an MHD based magnetosheath model by running a total of 14 OpenGGCM global MHD simulations under 7 solar wind densities (1, 5, 10, 15, 20, 25, and 30cm−3) and 2 interplanetary magnetic field BZ components (± 4nT), and then parameterizing the results in new magnetosheath conditions. We compared the magnetosheath model result with THEMIS statistical data and it showed good agreement with a weighted Pearson correlation coefficient greater than 0.77, especially for plasma density and plasma velocity. Second, we compiled a suite of magnetosheath models incorporating previous magnetosheath models (gas-dynamic, analytic), and did two case studies to test the performance. The MHD based model was comparable to or better than the previous models while providing self consistency among the magnetosheath parameters. Third, we constructed a tool to calculate a soft X-ray image from any given vantage point, which can support the planning and data analysis of the aforementioned LEXI and SMILE missions. A release of the code has been uploaded to a Github repository.

Magnetosheath↗
NASA NTRS2023

Hybrid-Vlasov Simulation of Soft X-Ray Emissions at the Earth’S Dayside Magnetospheric Boundaries

Solar wind charge exchange produces emissions in the soft X-ray energy range which can enable the study of near-Earth space regions such as the magnetopause, the magnetosheath and the polar cusps by remote sensing techniques. The Solar wind–Magnetosphere–Ionosphere Link Explorer (SMILE) and Lunar Environment heliospheric X-ray Imager (LEXI) missions aim to obtain soft X-ray images of near-Earth space thanks to their Soft X-ray Imager (SXI) instruments. While earlier modeling works have already simulated soft X-ray images as might be obtained by SMILE SXI during its mission, the numerical models used so far are all based on the magnetohydrodynamics description of the space plasma. To investigate the possible signatures of ion-kinetic-scale processes in soft X-ray images, we use for the first time a global hybrid-Vlasov simulation of the geospace from the Vlasiator model. The simulation is driven by fast and tenuous solar wind conditions and purely southward interplanetary magnetic field. We first produce global X-ray images of the dayside near-Earth space by placing a virtual imaging satellite at two different locations, providing meridional and equatorial views. We then analyze regional features present in the images and show that they correspond to signatures in soft X-ray emissions of mirror mode wave structures in the magnetosheath and flux transfer events (FTEs) at the magnetopause. Our results suggest that, although the time scales associated with the motion of those transient phenomena will likely be significantly smaller than the integration time of the SMILE and LEXI imagers, mirror-mode structures and FTEs can cumulatively produce detectable signatures in the soft X-ray images. For instance, a local increase by 30% in the proton density at the dayside magnetopause resulting from the transit of multiple FTEs leads to a 12% enhancement in the line-of-sight- and time integrated soft X-ray emissivity originating from this region. Likewise, a proton density increase by 14% in the magnetosheath associated with mirror-mode structures can result in an enhancement in the soft X-ray signal by 4%. These are likely conservative estimates, given that the solar wind conditions used in the Vlasiator run can be expected to generate weaker soft X-ray emissions than the more common denser solar wind. These results will contribute to the preparatory work for the SMILE and LEXI missions by providing the community with quantitative estimates of the effects of small-scale, transient phenomena occurring on the dayside.

magnetosphere↗
NASA NTRSDate not supplied

Prediction of High-Latitude Ionospheric Electrodynamics Using the Machine Learning Based Auroral Ionospheric Electrodynamics Model

We introduce a new framework for Machine-Learning (ML) based Auroral Ionosphere Model (ML-AIM). ML-AIM solves a current continuity equation by utilizing the ML model of Field Aligned Currents (FACs) of Kunduri et al., 2020 (https://doi.org/10.1029/2020JA027908), the FAC-derived aurora conductance model of Robinson et al., 2020 (https://doi.org/10.1029/2020JA028008), and the solar irradiance conductance model of Moen & Brekke (1993). The ML-AIM inputs are 60min time histories of solar wind plasma, interplanetary magnetic fields (IMF), and geomagnetic indices, and its outputs are ionospheric electric potential, electric fields, Pederson/Hall currents, and Joule Heating. We conduct two ML-AIM simulations for a weak geomagnetic activity on 14 May 2013 and a geomagnetic storm on 7-8 September 2017. ML-AIM produces reasonable ionospheric potential patterns such as two cell convection patterns and the enhancement of electric potentials during active times. The cross polar cap potential drop from ML-AIM is also comparable to the ones from the Weimer 2005 model, Super Dual Auroral Radar Network (SuperDARN), and Defense Meteorological Satellite Program (DMSP) F17 observations. ML-AIM is unique in a sense that it predicts ionospheric responses to the time-varying solar wind and geomagnetic conditions, while other traditional empirical model like Weimer 2005 is designed to provide static ionospheric conditions under steady solar wind/IMF conditions. In future, ML-AIM will include ML-based models of aurora precipitation and ionospheric conductance, improving its performance during active times.

H. K. Connor↗