# A Machine Learning Dataset Prepared From the NASA Solar Dynamics Observatory Mission

## TL;DR

This paper introduces a curated, machine learning-ready dataset derived from NASA's SDO mission, enabling advanced research in heliophysics through applications like irradiance forecasting and observation translation.

## Contribution

It provides a processed, synchronized dataset from SDO data, with baseline applications and metrics to support machine learning research in solar physics.

## Key findings

- Dataset facilitates machine learning research in heliophysics.
- Baseline models for irradiance forecasting established.
- Observation translation demonstrates potential for data augmentation.

## Abstract

In this paper we present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We illustrate the use of this dataset with two example applications: forecasting future EVE irradiance from present EVE irradiance and translating HMI observations into AIA observations. For each application we provide metrics and baselines for future model comparison. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a direct result of the 2018 NASA Frontier Development Laboratory Program. Please see the appendix for access to the dataset.