Conceptual Design of a Solid State Telescope for Small scale magNetospheric Ionospheric Plasma Experiments

The present paper describes the design of a Solid State Telescope (SST) on board the Korea Astronomy and Space Science Institute satellite-1 (KASISat-1) consisting of four [TBD] nanosatellites. The SST will measure these radiation belt electrons from a low-Earth polar orbit satellite to study mechanisms related to the spatial resolution of electron precipitation, such as electron microbursts, and those related to the measurement of energy dispersion with a high temporal resolution in the sub-auroral regions. We performed a simulation to determine the sensor design of the SST using GEometry ANd Tracking 4 (GEANT4) simulations and the Bethe formula. The simulation was performed in the range of 100 ~ 400 keV considering that the electron, which is to be detected in the space environment. The SST is based on a silicon barrier detector and consists of two telescopes mounted on a satellite to observe the electrons moving along the geomagnetic field (pitch angle 0°) and the quasi-trapped electrons (pitch angle 90°) during observations. We determined the telescope design of the SST in view of previous measurements and the geometrical factor in the cylindrical geometry of Sullivan (1971). With a high spectral resolution of 16 channels over the 100 keV ~ 400 keV energy range, together with the pitch angle information, the designed SST will answer questions regarding the occurrence of microbursts and the interaction with energetic particles. The KASISat-1 is expected to be launched in the latter half of 2020

Validation of KREAM Based on In-Situ Measurements of Aviation Radiation in Commercial Flights

There has been increasing necessity of more precise prediction and measurements of aviation radiation in Korea. For our air crew and passengers’ radiation safety, we develop our own radiation prediction model of KREAM. In this paper, we validate the KREAM model based on comparison with Liulin observations. During early three months of this year, we perform total 25 experiments to measure aviation radiation exposure using Liulin-6K in commercial flights. We found that KREAM’s result is very well consistent with Liulin observation in general. NAIRAS shows mostly higher results than Liulin observation, while CARI-6M shows generally lower results than the observations. The percent error of KREAM compared with Liulin observation is 10.95%. In contrast, the error for NAIRAS is 43.38% and 22.03% for CARI-6M. We found that the increase of the altitude might cause sudden increase in radiation exposure, especially for the polar route. As more comprehensive and complete analysis is required to validate KREAM’s reliability to use for the public service, we plan to expand these radiation measurements with Liulin and Tissue Equivalent Proportional Counter (TEPC) in the near future

Improving the Accuracy of a Heliocentric Potential (HCP) Prediction Model for the Aviation Radiation Dose

The space radiation dose over air routes including polar routes should be carefully considered, especially when space weather shows sudden disturbances such as coronal mass ejections (CMEs), flares, and accompanying solar energetic particle events. We recently established a heliocentric potential (HCP) prediction model for real-time operation of the CARI-6 and CARI-6M programs. Specifically, the HCP value is used as a critical input value in the CARI-6/6M programs, which estimate the aviation route dose based on the effective dose rate. The CARI-6/6M approach is the most widely used technique, and the programs can be obtained from the U.S. Federal Aviation Administration (FAA). However, HCP values are given at a one month delay on the FAA official webpage, which makes it difficult to obtain real-time information on the aviation route dose. In order to overcome this critical limitation regarding the time delay for space weather customers, we developed a HCP prediction model based on sunspot number variations (Hwang et al. 2015). In this paper, we focus on improvements to our HCP prediction model and update it with neutron monitoring data. We found that the most accurate method to derive the HCP value involves (1) real-time daily sunspot assessments, (2) predictions of the daily HCP by our prediction algorithm, and (3) calculations of the resultant daily effective dose rate. Additionally, we also derived the HCP prediction algorithm in this paper by using ground neutron counts. With the compensation stemming from the use of ground neutron count data, the newly developed HCP prediction model was improved