The proposed work will improve on past studies a majority of which focus on the near-surface (>20 MM) layer by probing deeper layers of the convection zone (up to 60 MM of depth) using both…

The proposed work will improve on past studies a majority of which focus on the near-surface (>20 MM) layer by probing deeper layers of the convection zone (up to 60 MM of depth) using both established and novel helioseismic techniques. This will study the flux emergence process using the previously described subsurface sound speed and flow maps generated from HMI dopplergrams. The team will generate maps up to 60 MM beneath the surface with time-distance methods in the deep-focusing regime using both traditional and modified techniques which preliminary testing has shown to have a greater signal-to-noise ratio. The team will also use the shallower sound speed and flow maps provided by jsoc whose advantage lies in their extensive testing and validation. This will allow to study the flux emergence process from the upper convection zone to very near to the solar surface. We will use a sample of around 100 active regions to evaluate the statistical reliability of pre-emergence signatures a figure suggested by past work as being sufficient for this task. For the machine learning portion of this investigation the team will initially use convolutional neural networks (CNNS) which excel at image analysis and feature recognition. The determination of parameters for the CNN will be a sub-task of this investigation and other appropriate ML methods such as long short-term memory (LSTM) models will be explored.