Engineering topics

D. Gershman

Publications and source records attributed to D. Gershman.

8 records

NASA NTRS2020

Upstream Ultra-Low Frequency Waves Observed by MESSENGER's Magnetometer: Implications for Particle Acceleration at Mercury's Bow Shock

We perform the first statistical analysis of the main properties of waves observed in the 0.05–0.41 Hz frequency range in the Hermean foreshock by the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) Magnetometer. Although we find similar polarization properties to the “30 second” waves observed at the Earth's foreshock, the normalized wave amplitude (δ B /|B0| ∼0.2) and occurrence rate (∼0.5%) are much smaller. This could be associated with relatively lower backstreaming proton fluxes, the smaller foreshock size and/or less stable solar wind (SW) conditions around Mercury. Furthermore, we estimate that the speed of resonant backstreaming protons in the SW reference frame (likely source for these waves) ranges between 0.95 and 2.6 times the SW speed. The closeness between this range and what is observed at other planetary foreshocks suggests that similar acceleration processes are responsible for this energetic population and might be present in the shocks of exoplanets.

N. Romanelli↗

NASA NTRS2020

Neural Network Repair of Lossy Compression Artifacts in the Sept 2015 – March 2016 Duration of the MMS/FPI Dataset

During the Sept 2015 –March 2016 duration (sometimes referred to as Phase 1A) of the Magnetospheric Multiscale Mission (MMS), the Dual Electron Spectrometers (DES) were configured to generously utilize lossy compression. While this maximized the number of velocity distribution functions downlinked, it cameat the expense of lost information content for a fractionof the frames. Following this period of lossy compression, the DES was re-configured in a way that allowed for 95% of the framesto arrive to the ground without loss. Using this high-quality set offrameson-orbit observations, we compressed and decompressed the frameson the ground to create a side-by-side record of the compression effect. This record was used to drive an optimization method that (a) derived basis functions capable of approximating the lossless sample space and with non-negative coefficients and (b) fitted a function which maps the lossy framesto basis weights that recreate the framewithout compression artifacts. This methodis introduced and evaluated in this paper.Data users should expect a higher level of confidence in the absolute scale of density/temperature measurements andnotice less sinusoidal bias in the velocity X and Y components(GSE).

Magnetosphere, Neural Networks, Image Processing, ↗

NASA NTRS2018