SuNeRF: Validation of a 3D Global Reconstruction of the Solar Corona Using Simulated EUV Images

Extreme Ultraviolet (EUV) light emitted by the Sun impacts satellite operations and communications and affects the habitability of planets. Currently, EUV-observing instruments are constrained to viewing the Sun from its equator (i.e., ecliptic), limiting our ability to forecast EUV emission for other viewpoints (e.g. solar poles), and to generalize our knowledge of the Sun-Earth system to other host stars. In this work, we adapt Neural Radiance Fields (NeRFs) to the physical properties of the Sun and demonstrate that non-ecliptic viewpoints could be reconstructed from observations limited to the solar ecliptic. To validate our approach, we train on simulations of solar EUV emission that provide a ground truth for all viewpoints. Our model accurately reconstructs the simulated 3D structure of the Sun, achieving a peak signal-to-noise ratio of 43.3 dB and a mean absolute relative error of 0.3\% for non-ecliptic viewpoints. Our method provides a consistent 3D reconstruction of the Sun from a limited number of viewpoints, thus highlighting the potential to create a virtual instrument for satellite observations of the Sun. Its extension to real observations will provide the missing link to compare the Sun to other stars and to improve space-weather forecasting.Comment: Accepted at Machine Learning and the Physical Sciences workshop, NeurIPS 202

Sun Neural Radiance Fields (SuNeRFs): From Images to 4D Models of the Solar Atmosphere

EUV-observing instruments are limited in their numbers and have mainly been constrained to viewing the Sun from the ecliptic. For example, the Solar Dynamics Observatory (SDO; 2010-present) provides images of the Sun in EUV from the perspective of the Earth-Sun line. Two additional viewpoints are provided by the STEREO twin satellites pulling Ahead (STEREO-A; 2006-present) and falling Behind (STEREO-B; 2006-2014) of Earth's orbit. No satellites observe the solar poles directly. However, a complete image of the 3D Sun is required to fully understand the dynamics of the Sun (from eruptive events to space weather in the solar system), to forecast EUV radiation to protect our assets in space, to relate the Sun to other stars in the universe, and to generalize our knowledge of the Sun-Earth system to other host stars. To maximize the science return of multiple viewpoints, we propose a novel approach that unifies and smoothly integrates data from multiple perspectives into a consistent 3D representation of the solar corona. More specifically, we leverage Neural Radiance Fields (NeRFs) which are neural networks that achieve state-of-the-art 3D scene representation and generate novel views from a limited number of input images. We adapted a Sun NeRF (SuNeRF) to generate a physically-consistent representation of the 3D Sun, with the inclusion of radiative transfer and geometric ray sampling that matches the physical reality of optically thin plasma in the solar atmosphere. SuNeRFs leverage existing multi-viewpoint observations and act as virtual instruments that can fly out of the ecliptic, that can view the poles, and that can be placed anywhere in the solar system to generate novel views. Our pipeline is an example of how novel deep learning techniques can be used to significantly enhance observational capabilities by the creation of virtual instruments

Diatom modulation of select bacteria through use of two unique secondary metabolites

Unicellular eukaryotic phytoplankton, such as diatoms, rely on microbial communities for survival despite lacking specialized compartments to house microbiomes (e.g., animal gut). Microbial communities have been widely shown to benefit from diatom excretions that accumulate within the microenvironment surrounding phytoplankton cells, known as the phycosphere. However, mechanisms that enable diatoms and other unicellular eukaryotes to nurture specific microbiomes by fostering beneficial bacteria and repelling harmful ones are mostly unknown. We hypothesized that diatom exudates may tune microbial communities and employed an integrated multiomics approach using the ubiquitous diatom Asterionellopsis glacialis to reveal how it modulates its naturally associated bacteria. We show that A. glacialis reprograms its transcriptional and metabolic profiles in response to bacteria to secrete a suite of central metabolites and two unusual secondary metabolites, rosmarinic acid and azelaic acid. While central metabolites are utilized by potential bacterial symbionts and opportunists alike, rosmarinic acid promotes attachment of beneficial bacteria to the diatom and simultaneously suppresses the attachment of opportunists. Similarly, azelaic acid enhances growth of beneficial bacteria while simultaneously inhibiting growth of opportunistic ones. We further show that the bacterial response to azelaic acid is numerically rare but globally distributed in the world's oceans and taxonomically restricted to a handful of bacterial genera. Our results demonstrate the innate ability of an important unicellular eukaryotic group to modulate select bacteria in their microbial consortia, similar to higher eukaryotes, using unique secondary metabolites that regulate bacterial growth and behavior inversely across different bacterial populations.publishe