Using Sun's Far-Side Images Inferred by the Time-Distance Helioseismic Imaging to Improve Synoptic Maps of Magnetic Field: Importance and Methodology
Abstract
Synoptic map of magnetic field is an important piece of data used for many space weather models. Currently solar observation can only provide magnetic field measurement on the earth-side surface. To generate magnetic field synoptic maps, the magnetic field measured about 13 days ago is used for the far-side surface when it was at the earth-side. This kind of synoptic maps was improved later on by evolving the measured magnetic field to the day of interest using a flux transfer model. It takes into account of evolution of magnetic field, but fails to include newly emerging magnetic flux, especially emerging active regions, that start to emerge at the far-side surface.
In this presentation, we first demonstrate that the newly emerging fluxes in the far-side change the coronal magnetic field structure, and this change can be global, far reaching to the earth-side. Because coronal magnetic field is related to the solar wind property and CMEs' speed, this change has potential to impact space weather forecast. We then present examples that convert far-side images into magnetic flux distribution using deep learning. The far-side images are inferred by the time-distance helioseismic method. Finally we propose to improve the synoptic maps of magnetic field by combining the far-size images and the machine learning technique.- Publication:
-
Solar Heliospheric and INterplanetary Environment (SHINE 2018)
- Pub Date:
- July 2018
- Bibcode:
- 2018shin.confE.147L