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

Interchange Injections at Saturn: Statistical Survey of Energetic H+ Sudden Flux Intensifications

We present a statistical study of interchange injections in Saturn’s inner and middle magnetosphere focusing on the dependence of occurrence rate and properties on radial distance, partial pressure, and local time distribution. Events are evaluated from over the entirety of the Cassini mission’s equatorial orbits between 2005 and 2016. We identified interchange events from CHarge Energy Mass Spectrometer (CHEMS) H+ data using a trained and tested automated algorithm, which has been compared with manual event identification for optimization. We provide estimates of interchange based on intensity, which we use to investigate current inconsistencies in local time occurrence rates. This represents the first automated detection method of interchange, estimation of injection event intensity, and comparison between interchange injection survey results. We find that the peak rates of interchange occur between 7 and 9 Saturn radii and that this range coincides with the most intense events as defined by H+ partial particle pressure. We determine that nightside occurrence dominates as compared to the dayside injection rate, supporting the hypothesis of an inversely dependent instability growth rate on local Pedersen ionospheric conductivity. Additionally, we observe a slight preference for intense events on the dawnside, supporting a triggering mechanism related to large‐scale injections from downtail reconnection. Our observed local time dependence paints a dynamic picture of interchange triggering due to both the large‐scale injection‐driven process and ionospheric conductivity.Plain Language SummaryStudying high‐energy particles around magnetized planets is essential to understanding processes behind mass transport in planetary systems. Saturn’s magnetic environment, or magnetosphere, is sourced from a large amount of low‐energy water particles from Enceladus, a moon of Saturn. Saturn’s magnetosphere also undergoes large rotational forces from Saturn’s short day and massive size. The rotational forces and dense internal mass source drive interchange injections, or the injection of high‐energy particles closer to the planet as low‐energy water particles from the inner magnetosphere are transported outward. There have been many strides toward understanding the occurrence rates of interchange injections, but it is still unknown how interchange events are triggered. We present a computational method to identify and rank interchange injections using high‐energy particle fluxes from the Cassini mission to Saturn. These events have never been identified computationally, and the resulting database is now publically available. We find that the peak rates of interchange occur between 7 and 9 Saturn radii and that this range coincides with the highest intensity events. We also find that interchange occurrence rates peak on the nightside of Saturn. Through this study, we identify the potential mechanisms behind interchange events and advance our understanding of mass transport around planets.Key PointsWe developed a novel classification and identification algorithm for interchange injection based on Cassini CHEMS 3–220 keV H+ energetic ionsRadial occurrence rates and maximum partial H+ pressure in interchange peaked between 7 and 9 Saturn radii for all intensity categoriesOccurrence rates peak on the nightside (1800–0600 LT) as compared to the dayside (0600–1800 LT)Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145315/1/jgra54283.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145315/2/jgra54283_am.pd

MESSENGER observations of suprathermal electrons in Mercury's magnetosphere

The X‐Ray Spectrometer (XRS) on the MErcury Surface, Space ENvironment, GEochemistry, and Ranging spacecraft regularly detected fluorescent X‐rays near Mercury induced by low‐energy (1–10 keV) or suprathermal electrons. We devised an algorithm to select these events from XRS records between April 2011 and March 2015 on the basis of their duration, location, and spectral slope. We identified 3102 events during 3900 orbits around Mercury, sampling all Mercury longitudes multiple times over the 4 year period. These suprathermal electrons were present near the planet at all local times, but the majority were on the nightside of the planet, and a dawn‐dusk asymmetry is seen in the data. When the event locations are plotted in a coordinate system based on a simplified magnetic field model, several distinct clusters of events are evident. We infer that all are signatures of accelerated electrons that were injected from Mercury's tail region to form a quasi‐trapped electron population at Mercury

Comprehensive survey of energetic electron events in Mercury's magnetosphere with data from the MESSENGER Gamma‐Ray and Neutron Spectrometer

Data from the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) Gamma‐Ray and Neutron Spectrometer have been used to detect and characterize energetic electron (EE) events in Mercury's magnetosphere. This instrument detects EE events indirectly via bremsstrahlung photons that are emitted when instrument and spacecraft materials stop electrons having energies of tens to hundreds of keV. From Neutron Spectrometer data taken between 18 March 2011 and 31 December 2013 we have identified 2711 EE events. EE event amplitudes versus energy are distributed as a power law and have a dynamic range of a factor of 400. The duration of the EE events ranges from tens of seconds to nearly 20 min. EE events may be classified as bursty (large variation with time over an event) or smooth (small variation). Almost all EE events are detected inside Mercury's magnetosphere on closed field lines. The precise occurrence times of EE events are stochastic, but the events are located in well‐defined regions with clear boundaries that persist in time and form what we call “quasi‐permanent structures.” Bursty events occur closer to dawn and at higher latitudes than smooth events, which are seen near noon‐to‐dusk local times at lower latitudes. A subset of EE events shows strong periodicities that range from hundreds of seconds to tens of milliseconds. The few‐minute periodicities are consistent with the Dungey cycle timescale for the magnetosphere and the occurrence of substorm events in Mercury's magnetotail region. Shorter periods may be related to phenomena such as north‐south bounce processes for the energetic electrons

ISTP-Next Metadata Guidelines: Updates and Update Process Needed

The existing ISTP guidelines for global and variable metadata attributes in CDF files (and analogous conventions for non-CDF file types) have seen widespread adoption among data providers and archival services. This has enabled the development of general-purpose plotting and analysis tools that can handle a wide variety of data sets, as long as they are reasonably compliant with the current standards. Yet, in some respects, the current standard tries to impose a "one size fits all" structure (e.g., DEPEND_N attributes) that fail to capture the nuances of how many data sets are organized. Data set designers sometimes resort to unconventional metadata structures to get around these limitations, making it difficult to implement a truly general CDF reader without resorting to mission-specific hacks. In this presentation, the authors expand on some discussions from the 2023 ISTP-Next workshop, present concrete examples from various data sets illustrating some of the difficulties with the existing standard, and propose some enhancements that would solve these issues in a next generation metadata standard. We also discuss the need for a versioning scheme, and a formal process for adopting and evolving the standard in a way that provides both stability (e.g., a data set pinning their metadata scheme to a particular version of the standard), and flexibility (e.g., introducing a new standard convention to accommodate a novel data set organization)