The integrated Miniaturized Electrostatic Analyzer‐Reflight (iMESA‐R) is a space weather instrument designed to measure plasma density, temperature, and spacecraft charging, along with total ionizing dose and dose rate. A constellation consisting of five nearly identical instruments has been designed and developed to act as science payloads hosted on‐board Department of Defense satellites operating in low‐Earth orbit. To validate the dosimetry component of the iMESA‐R, a radiation test study was performed to calibrate the dosimeter and ascertain the attenuation due to the instrument aluminum housing. An 80 curie Cobalt‐60 radioisotope source emitting beta, X‐ray, and gamma ray radiation decay products was used to calibrate the dosimeter response as a function of distance and instrument shielding. We present results of the calibration study along with initial on‐orbit data presented from the first operational iMESA‐R hosted on‐board Space Test Program Satellite 5 in a polar Earth orbit. The initial on‐orbit data demonstrate the ability to map the radiation environment, particularly the South Atlantic Anomaly and the auroral regions using total ionizing dose rate due to electrons of E > 1.5 MeV and protons of E > 25 MeV.
This work serves as a proof of concept for an automated tool to assist in the evaluation of experimental neutron cross section data in the resolved resonance range. The resonance characterization problem is posed as a mixed integer nonlinear program (MINLP). Since the number of resonances present is unknown, the model must be able to be determine the number of parameters to properly characterize the cross section curve as well as calculate the appropriate values for those parameters. Due to the size of the problem and the nonconvex nature of the parameterization, the optimization formulation is too difficult to solve as whole. A novel method is developed to decompose the problem into smaller, solvable windows and then stitch them back together via parameter-cardinality and parameter-value agreement routines in order to achieve a global solution. A version of quantile regression is used to provide an uncertainty estimate on the suggested cross section that is appropriate with respect to the experimental data. The results demonstrate the model's ability to find the proper number of resonances, appropriate average values for the parameters, and an uncertainty estimation that is directly reflective of the experimental conditions. The use of synthetic data allows access to the solution, this is leveraged to build-up performance statistics and map the uncertainty driven by the experimental data to an uncertainty on the true cross section.
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