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Register a new userA Machine-Learning-Ready Dataset Prepared from the Solar and Heliospheric Observatory Mission
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Carl Shneider
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We present a Python tool to generate a standard dataset from solar images that allows for user-defined selection criteria and a range of pre-processing steps. Our Python tool works with all image products from both the Solar and Heliospheric Observatory (SoHO) and Solar Dynamics Observatory (SDO) missions. We discuss a dataset produced from the SoHO missions multi-spectral images which is free of missing or corrupt data as well as planetary transits in coronagraph images, and is temporally synced making it ready for input to a machine learning system. Machine-learning-ready images are a valuable resource for the community because they can be used, for example, for forecasting space weather parameters. We illustrate the use of this data with a 3-5 day-ahead forecast of the north-south component of the interplanetary magnetic field (IMF) observed at Lagrange point one (L1). For this use case, we apply a deep convolutional neural network (CNN) to a subset of the full SoHO dataset and compare with baseline results from a Gaussian Naive Bayes classifier.
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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.
We provide a large image parameter dataset extracted from the Solar Dynamics Observatory (SDO) missions AIA instrument, for the period of January 2011 through the current date, with the cadence of six minutes, for nine wavelength channels. The volume of the dataset for each year is just short of 1 TiB. Towards achieving better results in the region classification of active regions and coronal holes, we improve upon the performance of a set of ten image parameters, through an in depth evaluation of various assumptions that are necessary for calculation of these image parameters. Then, where possible, a method for finding an appropriate settings for the parameter calculations was devised, as well as a validation task to show our improved results. In addition, we include comparisons of JP2 and FITS image formats using supervised classification models, by tuning the parameters specific to the format of the images from which they are extracted, and specific to each wavelength. The results of these comparisons show that utilizing JP2 images, which are significantly smaller files, is not detrimental to the region classification task that these parameters were originally intended for. Finally, we compute the tuned parameters on the AIA images and provide a public API (http://dmlab.cs.gsu.edu/dmlabapi) to access the dataset. This dataset can be used in a range of studies on AIA images, such as content-based image retrieval or tracking of solar events, where dimensionality reduction on the images is necessary for feasibility of the tasks.
Solar Orbiter is the first space mission observing the solar plasma both in situ and remotely, from a close distance, in and out of the ecliptic. The ultimate goal is to understand how the Sun produces and controls the heliosphere, filling the Solar System and driving the planetary environments. With six remote-sensing and four in-situ instrument suites, the coordination and planning of the operations are essential to address the following four top-level science questions: (1) What drives the solar wind and where does the coronal magnetic field originate? (2) How do solar transients drive heliospheric variability? (3) How do solar eruptions produce energetic particle radiation that fills the heliosphere? (4) How does the solar dynamo work and drive connections between the Sun and the heliosphere? Maximising the missions science return requires considering the characteristics of each orbit, including the relative position of the spacecraft to Earth (affecting downlink rates), trajectory events (such as gravitational assist manoeuvres), and the phase of the solar activity cycle. Furthermore, since each orbits science telemetry will be downloaded over the course of the following orbit, science operations must be planned at mission level, rather than at the level of individual orbits. It is important to explore the way in which those science questions are translated into an actual plan of observations that fits into the mission, thus ensuring that no opportunities are missed. First, the overarching goals are broken down into specific, answerable questions along with the required observations and the so-called Science Activity Plan (SAP) is developed to achieve this. The SAP groups objectives that require similar observations into Solar Orbiter Observing Plans (SOOPs), resulting in a strategic, top-level view of the optimal opportunities for science observations during the mission lifetime.
The Rosse Solar-Terrestrial Observatory (RSTO; www.rosseobservatory.ie) was established at Birr Castle, Co. Offaly, Ireland (53 0538.9, 7 5512.7) in 2010 to study solar radio bursts and the response of the Earths ionosphere and geomagnetic field. To date, three Compound Astronomical Low-cost Low-frequency Instrument for Spectroscopy and Transportable Observatory (CALLISTO) spectrometers have been installed, with the capability of observing in the frequency range 10-870 MHz. The receivers are fed simultaneously by biconical and log-periodic antennas. Nominally, frequency spectra in the range 10-400 MHz are obtained with 4 sweeps per second over 600 channels. Here, we describe the RSTO solar radio spectrometer set-up, and present dynamic spectra of a sample of Type II, III and IV radio bursts. In particular, we describe fine-scale structure observed in Type II bursts, including band splitting and rapidly varying herringbone features.
Solar Orbiter, the first mission of ESAs Cosmic Vision 2015-2025 programme and a mission of international collaboration between ESA and NASA, will explore the Sun and heliosphere from close up and out of the ecliptic plane. It was launched on 10 February 2020 04:03 UTC from Cape Canaveral and aims to address key questions of solar and heliospheric physics pertaining to how the Sun creates and controls the Heliosphere, and why solar activity changes with time. To answer these, the mission carries six remote-sensing instruments to observe the Sun and the solar corona, and four in-situ instruments to measure the solar wind, energetic particles, and electromagnetic fields. In this paper, we describe the science objectives of the mission, and how these will be addressed by the joint observations of the instruments onboard. The paper first summarises the mission-level science objectives, followed by an overview of the spacecraft and payload. We report the observables and performance figures of each instrument, as well as the trajectory design. This is followed by a summary of the science operations concept. The paper concludes with a more detailed description of the science objectives. Solar Orbiter will combine in-situ measurements in the heliosphere with high-resolution remote-sensing observations of the Sun to address fundamental questions of solar and heliospheric physics. The performance of the Solar Orbiter payload meets the requirements derived from the missions science objectives. Its science return will be augmented further by coordinated observations with other space missions and ground-based observatories.
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