The X-Ray Spectrometer on the MESSENGER Spacecraft

NASA’s MESSENGER (MErcury Surface, Space ENvironment, GEochemistry, and Ranging) mission will further the understanding of the formation of the planets by examining the least studied of the terrestrial planets, Mercury. During the one-year orbital phase (beginning in 2011) and three earlier flybys (2008 and 2009), the X-Ray Spectrometer (XRS) onboard the MESSENGER spacecraft will measure the surface elemental composition. XRS will measure the characteristic X-ray emissions induced on the surface of Mercury by the incident solar flux. The Kα lines for the elements Mg, Al, Si, S, Ca, Ti, and Fe will be detected. The 12° field-of-view of the instrument will allow a spatial resolution that ranges from 42 km at periapsis to 3200 km at apoapsis due to the spacecraft’s highly elliptical orbit. XRS will provide elemental composition measurements covering the majority of Mercury’s surface, as well as potential high-spatial-resolution measurements of features of interest. This paper summarizes XRS’s science objectives, technical design, calibration, and mission observation strategy.     

Neural network prediction of the topside electron content over the Euro-African sector derived from Swarm-A measurements

This study presents the first prediction results of a neural network model for the vertical total electron content of the topside ionosphere based on Swarm-A measurements. The model was trained on 5 years of Swarm-A data over the Euro-African sector spanning the period 1 January 2014 to 31 December 2018. The Swarm-A data was combined with solar and geomagnetic indices to train the NN model. The Swarm-A data of 1 January to 30 September 2019 was used to test the performance of the neural network. The data was divided into two main categories: most quiet and most disturbed days of each month. Each category was subdivided into two sub-categories according to the Swarm-A trajectory i.e. whether it was ascending or descending in order to accommodate the change in local time when the satellite traverses the poles. Four pairs of neural network models were implemented, the first of each pair having one hidden layer, and the second of each pair having two hidden layers, for the following cases: 1) quiet day-ascending, 2) quiet day-descending, 3) disturbed day-ascending, and 4) disturbed day-descending. The topside vertical total electron content predicted by the neural network models compared well with the measurements by Swarm-A. The model that performed best was the one hidden layer model in the case of quiet days for descending trajectories, with RMSE = 1.20 TECU, R = 0.76. The worst performance occurred during the disturbed descending trajectories where the one hidden layer model had the worst RMSE = 2.12 TECU, (R = 0.54), and the two hidden layer model had the worst correlation coefficient R = 0.47 (RMSE = 1.57).In all cases, the neural network models performed better than the IRI2016 model in predicting the topside total electron content. The NN models presented here is the first such attempt at comparing NN models for the topside VTEC based on Swarm-A measurements.