Self−supervised Deep Learning for Reducing Data Transmission Needs in Multi−Wavelength Space Instruments: a case study based on the Solar Dynamics Observatory

Abstract

The Solar Dynamics Observatory(SDO), a NASA mission that has been producing terabytes of observational data every day for more than ten years, has been used as a use-case to demonstrate the potential of particular methodologies and pave the way for future deep-space mission planning. In deep space, multispectral high-resolution missions like SDO would face two major challenges: 1- a low rate of telemetry 2- constrained hardware (i.e.limited number of observational channels). This project investigates the potential, and the limitations, of using a deep learning approach to reduce data transmission needs and data latency of a multi-wavelength satellite instrument. Namely, we use multi-channel data from the SDO's Atmospheric Imaging Assembly(AIA) to show how self-supervised deep learning models can be used to synthetically produce, via image-to-image translation, images of the solar corona, and how this can be leveraged to reduce the downlink requirements of similar space missions. In this regards, we focus on encoder-decoder based architectures and we study how morphological traits and brightness of the solar surface affects the neural network predictions. We also investigate the limitations that these virtual observations might have and the impact on science. Finally we discuss how the method we propose can be used to create a data transmission schema that is both efficient and automated.