Automated Coronal Hole Identification via Multi-Thermal Intensity Segmentation

Coronal holes (CH) are regions of open magnetic fields that appear as dark areas in the solar corona due to their low density and temperature compared to the surrounding quiet corona. To date, accurate identification and segmentation of CHs has been a difficult task due to their comparable intensity to local quiet Sun regions. Current segmentation methods typically rely on the use of single EUV passband and magnetogram images to extract CH information. Here, the Coronal Hole Identification via Multi-thermal Emission Recognition Algorithm (CHIMERA) is described, which analyses multi-thermal images from the Atmospheric Image Assembly (AIA) onboard the Solar Dynamics Observatory (SDO) to segment coronal hole boundaries by their intensity ratio across three passbands (171 \AA, 193 \AA, and 211 \AA). The algorithm allows accurate extraction of CH boundaries and many of their properties, such as area, position, latitudinal and longitudinal width, and magnetic polarity of segmented CHs. From these properties, a clear linear relationship was identified between the duration of geomagnetic storms and coronal hole areas. CHIMERA can therefore form the basis of more accurate forecasting of the start and duration of geomagnetic storms

Machine Learning Applications to Kronian Magnetospheric Reconnection Classification

The products of magnetic reconnection in Saturn’s magnetotail are identified in magnetometer observations primarily through characteristic deviations in the north–south component of the magnetic field. These magnetic deflections are caused by traveling plasma structures created during reconnection rapidly passing over the observing spacecraft. Identification of these signatures have long been performed by eye, and more recently through semi-automated methods, however these methods are often limited through a required human verification step. Here, we present a fully automated, supervised learning, feed forward neural network model to identify evidence of reconnection in the Kronian magnetosphere with the three magnetic field components observed by the Cassini spacecraft in Kronocentric radial–theta–phi coordinates as input. This model is constructed from a catalog of reconnection events which covers three years of observations with a total of 2093 classified events, categorized into plasmoids, traveling compression regions and dipolarizations. This neural network model is capable of rapidly identifying reconnection events in large time-span Cassini datasets, tested against the full year 2010 with a high level of accuracy (87%), true skill score (0.76), and Heidke skill score (0.73). From this model, a full cataloging and examination of magnetic reconnection events in the Kronian magnetosphere across Cassini's near Saturn lifetime is now possible

Classification of Cassini’s Orbit Regions as Magnetosphere, Magnetosheath, and Solar Wind via Machine Learning

Several machine learning algorithms and feature subsets from a variety of particle and magnetic field instruments on-board the Cassini spacecraft were explored for their utility in classifying orbit segments as magnetosphere, magnetosheath or solar wind. Using a list of manually detected magnetopause and bow shock crossings from mission scientists, random forest (RF), support vector machine (SVM), logistic regression (LR) and recurrent neural network long short-term memory (RNN LSTM) classification algorithms were trained and tested. A detailed error analysis revealed a RNN LSTM model provided the best overall performance with a 93.1% accuracy on the unseen test set and MCC score of 0.88 when utilizing 60 min of magnetometer data (|B|, Bθ, Bϕ and BR) to predict the region at the final time step. RF models using a combination of magnetometer and particle data, spanning H+, He+, He++ and electrons at a single time step, provided a nearly equivalent performance with a test set accuracy of 91.4% and MCC score of 0.84. Derived boundary crossings from each model’s region predictions revealed that the RNN model was able to successfully detect 82.1% of labeled magnetopause crossings and 91.2% of labeled bow shock crossings, while the RF model using magnetometer and particle data detected 82.4 and 74.3%, respectively

Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade

Machine learning (ML) methods can expand our ability to construct, and draw insight from large datasets. Despite the increasing volume of planetary observations, our field has seen few applications of ML in comparison to other sciences. To support these methods, we propose ten recommendations for bolstering a data-rich future in planetary science.Comment: 10 pages (expanded citations compared to 8 page submitted version for decadal survey), 3 figures, white paper submitted to the Planetary Science and Astrobiology Decadal Survey 2023-203

Automated coronal hole identification via multi-thermal intensity segmentation

Coronal holes (CH) are regions of open magnetic fields that appear as dark areas in the solar corona due to their low density and temperature compared to the surrounding quiet corona. To date, accurate identification and segmentation of CHs has been a difficult task due to their comparable intensity to local quiet Sun regions. Current segmentation methods typically rely on the use of single Extreme Ultra-Violet passband and magnetogram images to extract CH information. Here, the coronal hole identification via multi-thermal emission recognition algorithm (CHIMERA) is described, which analyses multi-thermal images from the atmospheric image assembly (AIA) onboard the solar dynamics observatory (SDO) to segment coronal hole boundaries by their intensity ratio across three passbands (171 Å, 193 Å, and 211 Å). The algorithm allows accurate extraction of CH boundaries and many of their properties, such as area, position, latitudinal and longitudinal width, and magnetic polarity of segmented CHs. From these properties, a clear linear relationship was identified between the duration of geomagnetic storms and coronal hole areas. CHIMERA can therefore form the basis of more accurate forecasting of the start and duration of geomagnetic storms

Machine learning applications to Kronian magnetospheric reconnection classification

The products of magnetic reconnection in Saturn’s magnetotail are identified in magnetometer3 observations primarily through characteristic deviations in the north-south component of the4 magnetic field. These magnetic deflections are caused by travelling plasma structures created5 during reconnection rapidly passing over the observing spacecraft. Identification of these6 signatures have long been performed by eye, and more recently through semi-automated7 methods, however these methods are often limited through a required human verification step.8 Here, we present a fully automated, supervised learning, feed forward neural network model9 to identify evidence of reconnection in the Kronian magnetosphere with the three magnetic10 field components observed by the Cassini spacecraft in Kronocentric radial-theta-phi (KRTP)11 coordinates as input. This model is constructed from a catalogue of reconnection events which12 covers three years of observations with a total of 2093 classified events, categorized into13 plasmoids, travelling compression regions and dipolarizations. This neural network model is14 capable of rapidly identifying reconnection events in large time-span Cassini datasets, tested15 against the full year 2010 with a high level of accuracy (87%), true skill score (0.76), and Heidke16 skill score (0.73). From this model, a full cataloguing and examination of magnetic reconnection17 events in the Kronian magnetosphere across Cassini’s near Saturn lifetime is now possibl

The observational uncertainty of coronal hole boundaries in automated detection schemes

Abstract Coronal holes are the observational manifestation of the solar magnetic field open to the heliosphere and are of pivotal importance for our understanding of the origin and acceleration of the solar wind. Observations from space missions such as the Solar Dynamics Observatory now allow us to study coronal holes in unprecedented detail. Instrumental effects and other factors, however, pose a challenge to automatically detect coronal holes in solar imagery. The science community addresses these challenges with different detection schemes. Until now, little attention has been paid to assessing the disagreement between these schemes. In this COSPAR ISWAT initiative, we present a comparison of nine automated detection schemes widely applied in solar and space science. We study, specifically, a prevailing coronal hole observed by the Atmospheric Imaging Assembly instrument on 2018 May 30. Our results indicate that the choice of detection scheme has a significant effect on the location of the coronal hole boundary. Physical properties in coronal holes such as the area, mean intensity, and mean magnetic field strength vary by a factor of up to 4.5 between the maximum and minimum values. We conclude that our findings are relevant for coronal hole research from the past decade, and are therefore of interest to the solar and space research community

A community dataset for comparing automated coronal hole detection schemes

Automated detection schemes are nowadays the standard approach for locating coronal holes in extreme-UV images from the Solar Dynamics Observatory (SDO). However, factors such as the noisy nature of solar imagery, instrumental effects, and others make it challenging to identify coronal holes using these automated schemes. While discrepancies between detection schemes have been noted in the literature, a comprehensive assessment of these discrepancies is still lacking. The contribution of the Coronal Hole Boundary Working Team in the COSPAR ISWAT initiative to close this gap is threefold. First, we present the first community data set for comparing automated coronal hole detection schemes. This data set consists of 29 SDO images, all of which were selected by experienced observers to challenge automated schemes. Second, we use this community data set as input to 14 widely applied automated schemes to study coronal holes and collect their detection results. Third, we study three SDO images from the data set that exemplify the most important lessons learned from this effort. Our findings show that the choice of the automated detection scheme can have a significant effect on the physical properties of coronal holes, and we discuss the implications of these findings for open questions in solar and heliospheric physics. We envision that this community data set will serve the scientific community as a benchmark data set for future developments in the field.Peer reviewe