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The Frequency Agile Solar Radiotelescope (FASR) has been strongly endorsed as a top community priority by both Astronomy & Astrophysics Decadal Surveys and Solar & Space Physics Decadal Surveys in the past two decades. Although it was developed to a high state of readiness in previous years (it went through a CATE analysis and was declared “doable now”), the NSF has not had the funding mechanisms in place to fund this mid-scale program. Now it does, and the community must seize this opportunity to modernize the FASR design and build the instrument in this decade. The concept and its science potential have been abundantly proven by the pathfinding Expanded Owens Valley Solar Array (EOVSA), which has demonstrated a small subset of FASR’s key capabilities such as dynamically measuring the evolving magnetic field in eruptive flares, the temporal and spatial evolution of the electron energy distribution in flares, and the extensive coupling among dynamic components (flare, flux rope, current sheet). The FASR concept, which is orders of magnitude more powerful than EOVSA, is low-risk and extremely high reward, exploiting a fundamentally new research domain in solar and space weather physics. Utilizing dynamic broadband imaging spectropolarimetry at radio wavelengths, with its unique sensitivity to coronal magnetic fields and to both thermal plasma and nonthermal electrons from large flares to extremely weak transients, the ground-based FASR will make synoptic measurements of the coronal magnetic field and map emissions from the chromosphere to the middle corona in 3D. With its high spatial, spectral, and temporal resolution, as well as its superior imaging fidelity and dynamic range, FASR is poised to provide a system-wide perspective on myriad coupled phenomena. FASR will be a highly complementary and synergistic component of solar and heliospheric observing capabilities that is critically needed to support the next generation of solar science.

The authors of this white paper met on 16-17 January 2020 at the New Jersey Institute of Technology,Newark, NJ, for a 2-day workshop that brought together a group of heliophysicists, data providers,expert modelers, and computer/data scientists. Their objective was to discuss critical developments and prospects of the application of machine and/or deep learning techniques for data analysis, modeling and forecasting in Heliophysics, and to shape a strategy for further developments in the field. The workshop combined a set of plenary sessions featuring invited introductory talks interleaved with a set of open discussion sessions. The outcome of the discussion is encapsulated in this white paper that also features a top-level list of recommendations agreed by participants