Nick Murphy

In astronomical imaging and adaptive optics (AO), when the atmosphere is modeled as a collection of wind-driven, frozen turbulent layers, it is possible to recover wavefront estimates that are more accurate than those obtained without accounting for temporal correlations. To make use of this frozen flow model, however, it is necessary to know the intrinsic details about turbulent layers, specifically how fast one is moving. Traditionally, this information comes from pupil plane wavefront sensor data, but that approach brings its own set of challenges, including susceptibility to noise and latency. Developing a method to estimate the characteristics of frozen turbulent layers, i.e., speed and direction, from focal plane images can improve restoration of archival speckle data lacking wavefront sensor input and reduce AO system latency by en-abling wavefront prediction from focal plane observations alone. Therefore, as an alternative to traditional methods, this work investigates a data-driven framework to predict wind profiles from the focal plane images themselves. We show the viability of using focal plane data, rather than relying on pupil plane measurements, by predicting the speed and direction of a single dominant atmospheric layer in synthetic data produced by a 1-layer turbulence model. Our predictive model design utilizes an autoencoder architecture augmented with VI CReg in the latent space. We demonstrate that the use of this regularization enhances robustness, and reduces the number of consecutive image frames needed for an accurate prediction.

Forecasting the relevant characteristics of central space weather events such as coronal mass ejections and solar flares is of utmost interest due to their potential near-Earth impact on our technological infrastructure. A key solar feature that is successfully utilized for predicting these solar events is magnetic polarity inversion lines (MPILs). Derived from magnetic field rasters, MPILs represent the shear layers between two opposing (i.e., positive and negative) polarity regions, and the complexity of these separating lines is shown to be highly relevant precursors for these solar events. In this paper, we present our data ecosystem for detecting and serving metadata for MPILs, along with important spatial, temporal and spatial-temporal search capabilities. The MPILs are detected using our detection framework and are served through our public APIs. Our APIs provide active region-based, spatial, and temporal search capabilities. The MPIL metadata consists of a series of binary rasters and metadata parameters. The rasters show the polarity inversion lines, regions of polarity inversion, the unsigned negative and positive polarity regions (both positive and negative) and convex hull of polarity inversion lines, while metadata features include physical and shape-based image parameters. We organize the metadata series as spatial and temporal time series derived from active region trajectories. This data resource is heterogeneous in nature and is designed to be easily extensible. MPIL-derived features are currently used in various deployed operational systems. We envision that our near-real time detection module and API service will be used as the backbone for operational space weather forecasting tools.