In this paper, regression-based deep convolutional neural networks (CNN), with 12 layers, are developed for predicting the maximal amplitude of the southward component of the near-Earth magnetic field from a passing interplanetary coronal mass ejection (ICME).

We have analyzed 33 cavities observed between 2012 and 2018, from solar activity maximum to minimum. For each cavity we applied a differential emission measure method to obtain both a temperature distribution and a value of the average temperature.

Gibson et al. have analyzed a long-lived cavity observed from 17 March 2012 to 21 March 2012. For this cavity we applied a differential emission measure method to obtain both a temperature distribution and a value of the average temperature during all five observational days.

The magnetic field in the corona is important for understanding solar activity. Linear polarization measurements inforbidden lines in the visible/IR provide information about coronal magnetic direction and topology.

Deriving the strength and direction of the three-dimensional (3D) magnetic field in the solar atmosphere is fundamental for understanding its dynamics. Volume information on the magnetic field mostly relies on photospheric and/or chromospheric surface vector magnetic fields.

Magnetic fields suspend the relatively cool material of solar prominences in an otherwise hot corona. A comprehensive understanding of solar prominences ultimately requires complex and dynamic models, constrained and validated by observations spanning the solar atmosphere.

We explore new opportunities for solar physics that could be realized by future missions providing sustained observations from vantage points away from the Sun-Earth line.