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In this study, a detailed metric survey on the “Galaxy 15” (April 2010) space weather event is conducted to validate MAGNetosphere–Ionosphere–Thermosphere (MAGNIT), a semi-physical auroral ionospheric conductance model characterizing four precipitation sources, against AMPERE measurements via field-aligned current (FAC) characteristics. As part of this study, the comparative performance of three ionosphere electrodynamic specifications involving auroral conductance models, MAGNIT, Ridley Legacy Model (RLM) (empirical), and Conductance Model for Extreme Events (CMEE) (empirical), within the Space Weather Modeling Framework (SWMF), is demonstrated. Overall, MAGNIT exhibits marginally improved predictions; root mean square error values in upward and downward FACs of MAGNIT predictions compared to AMPERE data are smaller than those of CMEE and Ridley Ionosphere Model (RIM) by ∼12.7% and ∼6.24% before the storm, ∼4.52% and ∼2.13% better during the main phase, ∼1.98% and ∼1.27% worse during the second minimum, and better by ∼1.84% and ∼1.49% by the beginning of the recovery, respectively. In all three model configurations, the dusk and night magnetic local time (MLT) sectors over-predict throughout the storm, while the day and dawn MLT sectors under-predict in response to interplanetary magnetic field (IMF) conditions. In addition to accuracy and bias, similar results and conclusions are drawn from additional metrics, including in the categories of correlation, precision, extremes, and skill, and recommendations are made for the best-performing model configuration in each metric category. Visual data–model comparisons conducted by studying the FAC location and latitude/MLT spread throughout various phases of the storm suggest that the spatial extent of the FACs is captured relatively well in the night-side auroral oval, unlike in the day-side oval. The spread in latitude of the FACs matches that in the previous literature on other model performances. This information on auroral precipitation sources and their weight on FACs, along with metrics from model–data comparisons, can be used to modify MAGNIT settings to optimize SWMF model performance.

To mitigate the issues of inequity, exclusion, and a lack of diversity in the solar and space physics research community, a Research Topic collection gathered articles of how scientists in this discipline are taking strides to make this community more welcoming. This review summarizes the key advice from those articles and offers practical actions for both immediate and long-term implementation. There are six major topical categories into which the collection’s article can be grouped: early-year improvements (pre-college and undergraduate); inclusive project teams; diversity in awards; equity in hiring and promotion; leadership development; and the workplace environment. The scope of suggestions ranges from the very localized, such as the language choices we make in everyday conversations, to the institutional, such as the establishment of codes of conduct with a definition and enforcement of consequences for inappropriate behavior regarding inclusion and equity issues. It is hoped that the recommendations are applicable not only to the space physics community but also to others.

A major obstacle in cultivating a robust Heliophysics (and broader scientific) community is the lack of diversity throughout science, technology, engineering, and mathematics (STEM) fields. For many years, this has been understood as a “leaky pipeline” analogy, in which predominately minority students initially interested in STEM gradually fall (or are pushed) out of the field on their way to a scientific research position. However, this ignores critical structural and policy issues which drive even later career Ph.D.s out of a career in Heliophysics. We identify here several systemic problems that inhibit many from participating fully in the Heliophysics community, including soft money pressure, lack of accessibility and equity, power imbalances, lack of accountability, friction in collaboration, and difficulties in forming mentorship bonds. We present several recommendations to empower research-supporting organizations to help create a culture of inclusion, openness, and innovative science.

The space physics research community is not diverse. This is especially true at the senior experience levels, but is even true for our student populations, which are also not matching the demographics of the general public. Striving towards a demographic shift to match the general population promotes equity and inclusion. In addition, diversity increases research productivity. Unfortunately, bias exists, including within the space physics research community, and this negatively impacts hiring practices and perpetuates the demographic mismatch. Yet there are many strategies and tactics that can be adopted to counter this problem. A number of these methods are presented and discussed, specifically those regarding the search process for hiring new research group members. The key methods for achieving an equitable search process are as follows: develop a holistic rubric early, even before the job ad is posted; slow down the downselect from the full applicant pool to the short list of finalists so that the rubric can be carefully applied to each candidate; make the interview process as equitable as possible by considering the ways in which it could be biased; and conduct a fair decision-making process that focuses on the job-relevant criteria and avoids global rankings until the final vote.

A large number of heliophysicists from across career levels, institution types, and job titles came together to support a poster at Heliophysics 2050 and the position papers for the 2024 Heliophysics decadal survey titled “Cultivating a Culture of Inclusivity in Heliophysics,” “The Importance of Policies: It’s not just a pipeline problem,” and “Mentorship within Heliophysics.” While writing these position papers, the number of people who privately shared disturbing stories and experiences of bullying and harassment was shocking. The number of people who privately expressed how burned out they were was staggering. The number of people who privately spoke about how they considered leaving the field for their and their family’s health was astounding. And for as much good there is in our community, it is still a toxic environment for many. If we fail to do something now, our field will continue to suffer. While acknowledging the ongoing growth that we as individuals must work toward, we call on our colleagues to join us in working on organizational, group, and personal levels toward a truly inclusive culture, for the wellbeing of our colleagues and the success of our field. This work includes policies, processes, and commitments to promote: accountability for bad actors; financial security through removing the constant anxiety about funding; prioritization of mental health and community through removing constant deadlines and constant last-minute requests; a collaborative culture rather than a hyper-competitive one; and a community where people can thrive as whole persons and do not have to give up a healthy or well-rounded life to succeed.

Owing to the spatial overlap of the ion plasma sheet (ring current) with the Earth’s neutral-hydrogen geocorona, there is a significant rate of occurrence of charge-exchange collisions in the dipolar portion of the Earth’s magnetosphere. During a charge-exchange collision between an energetic proton and a low-energy hydrogen atom, a low-energy proton is produced. These “byproduct” cold protons are trapped in the Earth’s magnetic field where they advect via E×B drift. In this report, the number density and behavior of this cold-proton population are assessed. Estimates of the rate of production of byproduct cold protons from charge exchange are in the vicinity of 1.14 cm−3 per day at geosynchronous orbit or about 5 tons per day for the entire dipolar magnetosphere. The production rate of cold protons owing to electron-impact ionization of the geocorona by the electron plasma sheet at geosynchronous orbit is about 12% of the charge-exchange production rate, but the production rate by solar photoionization of the neutral geocorona is comparable or larger than the charge-exchange production rate. The byproduct-ion production rates are smaller than observed early time refilling rates for the outer plasmasphere. Numerical simulations of the production and transport of cold charge-exchange byproduct protons find that they have very low densities on the nightside of geosynchronous orbit, and they can have densities of 0.2–0.3 cm−3 at geosynchronous orbit on the dayside. These dayside byproduct-proton densities might play a role in shortening the early phase of plasmaspheric refilling.

The performance of three global magnetohydrodynamic (MHD) models in estimating the Earth's magnetopause location and ionospheric cross polar cap potential (CPCP) have been presented. Using the Community Coordinated Modeling Center's Run-on-Request system and extensive database on results of various magnetospheric scenarios simulated for a variety of solar weather patterns, the aforementioned model predictions have been compared with magnetopause standoff distance estimations obtained from six empirical models, and with cross polar cap potential estimations obtained from the Assimilative Mapping of Ionospheric Electrodynamics (AMIE) Model and the Super Dual Auroral Radar Network (SuperDARN) observations. We have considered a range of events spanning different space weather activity to analyze the performance of these models. Using a fit performance metric analysis for each event, the models' reproducibility of magnetopause standoff distances and CPCP against empirically-predicted observations were quantified, and salient features that govern the performance characteristics of the modeled magnetospheric and ionospheric quantities were identified. Results indicate mixed outcomes for different models during different events, with almost all models underperforming during the extreme-most events. The quantification also indicates a tendency to underpredict magnetopause distances in the absence of an inner magnetospheric model, and an inclination toward over predicting CPCP values under general conditions.

Abstract A new model validation and performance assessment tool is introduced, the sliding threshold of observation for numeric evaluation (STONE) curve. It is based on the relative operating characteristic (ROC) curve technique, but instead of sorting all observations in a categorical classification, the STONE tool uses the continuous nature of the observations. Rather than defining events in the observations and then sliding the threshold only in the classifier/model data set, the threshold is changed simultaneously for both the observational and model values, with the same threshold value for both data and model. This is only possible if the observations are continuous and the model output is in the same units and scale as the observations, that is, the model is trying to exactly reproduce the data. The STONE curve has several similarities with the ROC curve—plotting probability of detection against probability of false detection, ranging from the (1,1) corner for low thresholds to the (0,0) corner for high thresholds, and values above the zero‐intercept unity‐slope line indicating better than random predictive ability. The main difference is that the STONE curve can be nonmonotonic, doubling back in both the x and y directions. These ripples reveal asymmetries in the data‐model value pairs. This new technique is applied to modeling output of a common geomagnetic activity index as well as energetic electron fluxes in the Earth's inner magnetosphere. It is not limited to space physics applications but can be used for any scientific or engineering field where numerical models are used to reproduce observations.

The near-Earth plasma sheet is the source for electrons in the inner magnetosphere. The coupling between the solar wind and the near-Earth plasma sheet is dominated by non-linear processes, making any relationship difficult to infer. We report on the development of a neural network to capture the non-linear behavior between solar wind variations and the response of energetic electron flux in the plasma sheet. To train the neural network algorithm, we developed a data set with inputs from solar wind monitoring spacecraft. The targets come from three probes of the Time History of Events and Macroscale Interactions during Substorms mission as the spacecraft traversed the plasma sheet from years 2008–2019. Preliminary findings during the development of the neural network model show that tuning input parameters based on previously known physical properties is conducive to improving model performance.

Recent improvements in data collection volume from planetary and space physics missions have allowed the application of novel data science techniques. The Cassini mission for example collected over 600 gigabytes of scientific data from 2004 to 2017. This represents a surge of data on the Saturn system. In comparison, the previous mission to Saturn, Voyager over 20 years earlier, had onboard a ~70 kB 8-track storage ability. Machine learning can help scientists work with data on this larger scale. Unlike many applications of machine learning, a primary use in planetary space physics applications is to infer behavior about the system itself. This raises three concerns: first, the performance of the machine learning model, second, the need for interpretable applications to answer scientific questions, and third, how characteristics of spacecraft data change these applications. In comparison to these concerns, uses of “black box” or un-interpretable machine learning methods tend toward evaluations of performance only either ignoring the underlying physical process or, less often, providing misleading explanations for it. The present work uses Cassini data as a case study as these data are similar to space physics and planetary missions at Earth and other solar system objects. We build off a previous effort applying a semi-supervised physics-based classification of plasma instabilities in Saturn's magnetic environment, or magnetosphere. We then use this previous effort in comparison to other machine learning classifiers with varying data size access, and physical information access. We show that incorporating knowledge of these orbiting spacecraft data characteristics improves the performance and interpretability of machine leaning methods, which is essential for deriving scientific meaning. Building on these findings, we present a framework on incorporating physics knowledge into machine learning problems targeting semi-supervised classification for space physics data in planetary environments. These findings present a path forward for incorporating physical knowledge into space physics and planetary mission data analyses for scientific discovery.