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We introduce a new multivariate data set that utilizes multiple spacecraft collecting in-situ and remote sensing heliospheric measurements shown to be linked to physical processes responsible for generating solar energetic particles (SEPs). Using the Geostationary Operational Environmental Satellites (GOES) flare event list from Solar Cycle (SC) 23 and part of SC 24 (1998-2013), we identify 252 solar events (flares) that produce SEPs and 17,542 events that do not. For each identified event, we acquire the local plasma properties at 1 au, such as energetic proton and electron data, upstream solar wind conditions, and the interplanetary magnetic field vector quantities using various instruments onboard GOES and the Advanced Composition Explorer (ACE) spacecraft. We also collect remote sensing data from instruments onboard the Solar Dynamic Observatory (SDO), Solar and Heliospheric Observatory (SoHO), and the Wind solar radio instrument WAVES. The data set is designed to allow for variations of the inputs and feature sets for machine learning (ML) in heliophysics and has a specific purpose for forecasting the occurrence of SEP events and their subsequent properties. This paper describes a dataset created from multiple publicly available observation sources that is validated, cleaned, and carefully curated for our machine-learning pipeline. The dataset has been used to drive the newly-developed Multivariate Ensemble of Models for Probabilistic Forecast of Solar Energetic Particles (MEMPSEP; see MEMPSEP I (Chatterjee et al., 2023) and MEMPSEP II (Dayeh et al., 2023) for associated papers).

The Sun continuously affects the interplanetary environment through a host of interconnected and dynamic physical processes. Solar flares, Coronal Mass Ejections (CMEs), and Solar Energetic Particles (SEPs) are among the key drivers of space weather in the near-Earth environment and beyond. While some CMEs and flares are associated with intense SEPs, some show little to no SEP association. To date, robust long-term (hours-days) forecasting of SEP occurrence and associated properties (e.g., onset, peak intensities) does not effectively exist and the search for such development continues. Through an Operations-2-Research support, we developed a self-contained model that utilizes a comprehensive dataset and provides a probabilistic forecast for SEP event occurrence and its properties. The model is named Multivariate Ensemble of Models for Probabilistic Forecast of Solar Energetic Particles (MEMPSEP). MEMPSEP workhorse is an ensemble of Convolutional Neural Networks that ingests a comprehensive dataset (MEMPSEP III - (Moreland et al., 2023)) of full-disc magnetogram-sequences and in-situ data from different sources to forecast the occurrence (MEMPSEP I - this work) and properties (MEMPSEP II - Dayeh et al. (2023)) of a SEP event. This work focuses on estimating true SEP occurrence probabilities achieving a 2.5% improvement in reliability and a Brier score of 0.14. The outcome provides flexibility for the end-users to determine their own acceptable level of risk, rather than imposing a detection threshold that optimizes an arbitrary binary classification metric. Furthermore, the model-ensemble, trained to utilize the large class-imbalance between events and non-events, provides a clear measure of uncertainty in our forecast, Comment: 17 pages, 8 figures, 1 table, accepted for publication in Space Weather journal

Solar Energetic Particles (SEPs) form a critical component of Space Weather. The complex, intertwined dynamics of SEP sources, acceleration, and transport make their forecasting very challenging. Yet, information about SEP arrival and their properties (e.g., peak flux) is crucial for space exploration on many fronts. We have recently introduced a novel probabilistic ensemble model called the Multivariate Ensemble of Models for Probabilistic Forecast of Solar Energetic Particles (MEMPSEP). Its primary aim is to forecast the occurrence and physical properties of SEPs. The occurrence forecasting, thoroughly discussed in a preceding paper (Chatterjee et al., 2023), is complemented by the work presented here, which focuses on forecasting the physical properties of SEPs. The MEMPSEP model relies on an ensemble of Convolutional Neural Networks, which leverage a multi-variate dataset comprising full-disc magnetogram sequences and numerous derived and in-situ data from various sources. Skill scores demonstrate that MEMPSEP exhibits improved predictions on SEP properties for the test set data with SEP occurrence probability above 50%, compared to those with a probability below 50%. Results present a promising approach to address the challenging task of forecasting SEP physical properties, thus improving our forecasting capabilities and advancing our understanding of the dominant parameters and processes that govern SEP production.

Space weather is a multidisciplinary research area connecting scientists from across heliophysics domains seeking a coherent understanding of our space environment that can also serve modern life and society's needs. COSPAR's ISWAT (International Space Weather Action Teams) 'clusters' focus attention on different areas of space weather study while ensuring the coupled system is broadly addressed via regular communications and interactions. The ISWAT cluster "H3: Radiation Environment in the Heliosphere" (https://www.iswat-cospar.org/h3) has been working to provide a scientific platform to understand, characterize and predict the energetic particle radiation in the heliosphere with the practical goal of mitigating radiation risks associated with areospace activities, satellite industry and human space explorations. In particular, present approaches help us understand the physical phenomena at large, optimizing the output of multi-viewpoint observations and pushing current models to their limits. In this paper, we review the scientific aspects of the radiation environment in the heliosphere covering four different radiation types: Solar Energetic Particles (SEPs), Ground Level Enhancement (GLE, a type of SEP events with energies high enough to trigger the enhancement of ground-level detectors), Galactic Cosmic Rays (GCRs) and Anomalous Cosmic Rays (ACRs). We focus on related advances in the research community in the past 10-20 years and what we still lack in terms of understanding and predictive capabilities. Finally we also consider some recommendations related to the improvement of both observational and modeling capabilities in the field of space radiation environment.

Extreme Ultraviolet images of the Sun are becoming an integral part of space weather prediction tasks. However, having different surveys requires the development of instrument-specific prediction algorithms. As an alternative, it is possible to combine multiple surveys to create a homogeneous dataset. In this study, we utilize the temporal overlap of SoHO/EIT and SDO/AIA 171~\AA ~surveys to train an ensemble of deep learning models for creating a single homogeneous survey of EUV images for 2 solar cycles. Prior applications of deep learning have focused on validating the homogeneity of the output while overlooking the systematic estimation of uncertainty. We use an approach called `Approximate Bayesian Ensembling' to generate an ensemble of models whose uncertainty mimics that of a fully Bayesian neural network at a fraction of the cost. We find that ensemble uncertainty goes down as the training set size increases. Additionally, we show that the model ensemble adds immense value to the prediction by showing higher uncertainty in test data that are not well represented in the training data., Comment: 20 pages, 8 figures, accepted for publication in ApJS

In order to utilize solar imagery for real-time feature identification and large-scale data science investigations of solar structures, we need maps of the Sun where phenomena, or themes, are labeled. Since solar imagers produce observations every few minutes, it is not feasible to label all images by hand. Here, we compare three machine learning algorithms performing solar image classification using extreme ultraviolet and Hydrogen-alpha images: a maximum likelihood model assuming a single normal probability distribution for each theme from Rigler et al. (2012), a maximum-likelihood model with an underlying Gaussian mixtures distribution, and a random forest model. We create a small database of expert-labeled maps to train and test these algorithms. Due to the ambiguity between the labels created by different experts, a collaborative labeling is used to include all inputs. We find the random forest algorithm performs the best amongst the three algorithms. The advantages of this algorithm are best highlighted in: comparison of outputs to hand-drawn maps; response to short-term variability; and tracking long-term changes on the Sun. Our work indicates that the next generation of solar image classification algorithms would benefit significantly from using spatial structure recognition, compared to only using spectral, pixel-by-pixel brightness distributions.

Ground-based solar observations provide key contextual data (i.e., the 'big picture') to produce a complete description of the only astrosphere we can study in situ: our Sun's heliosphere. The next decade will see the beginning of operations of the Daniel K. Inouye Solar Telescope (DKIST). DKIST will join NASA's Parker Solar Probe and the NASA/ESA Solar Orbital mission, which together will study our Sun's atmosphere with unprecedented detail. This white paper outlines the current paradigm for ground-based solar synoptic observations, and indicates those areas that will benefit from focused attention.

The initiation phase of CMEs is a very important aspect of solar physics, as these phenomena ultimately drive space weather in the heliosphere. This phase is known to occur between the photosphere and low corona, where many models introduce an instability and/or magnetic reconnection that triggers a CME, often with associated flaring activity. To this end, it is important to obtain a variety of observations of the low corona in order to build as clear a picture as possible of the dynamics that occur therein. Here, we combine the EUV imagery of the SWAP instrument on board PROBA2 with the white-light imagery of the ground-based Mk4 coronameter at MLSO in order to bridge the observational gap that exists between the disk imagery of AIA on board SDO and the coronal imagery of LASCO on board SOHO. Methods of multiscale image analysis were applied to the observations to better reveal the coronal signal while suppressing noise and other features. This allowed an investigation into the initiation phase of a CME that was driven by a rising flux rope structure from a "two-stage" flaring active region underlying an extended helmet streamer. It was found that the initial outward motion of the erupting loop system in the EUV observations coincided with the first X-ray flare peak, and led to a plasma pile-up of the white-light CME core material. The characterized CME core then underwent a strong jerk in its motion, as the early acceleration increased abruptly, simultaneous with the second X-ray flare peak. The overall system expanded into the helmet streamer to become the larger CME structure observed in the LASCO coronagraph images, which later became concave-outward in shape. Theoretical models for the event are discussed in light of these unique observations, and it is concluded that the formation of either a kink-unstable or torus-unstable flux rope may be the likeliest scenario., Comment: 21 pages, 9 figures, 3 movies

We present observational results of a type II burst associated with a CME-CME interaction observed in the radio and white-light wavelength range. We applied radio direction-finding techniques to observations from the STEREO and Wind spacecraft, the results of which were interpreted using white-light coronagraphic measurements for context. The results of the multiple radio-direction finding techniques applied were found to be consistent both with each other and with those derived from the white-light observations of coronal mass ejections (CMEs). The results suggest that the Type II burst radio emission is causally related to the CMEs interaction., Comment: 7 pages, 6 figures, Accepted to ApJ: January 16, 2012

The balloon-borne Gamma-Ray Imager/Polarimeter for Solar flares (GRIPS) instrument will provide a near-optimal combination of high-resolution imaging, spectroscopy, and polarimetry of solar-flare gamma-ray/hard X-ray emissions from approximately 20 keV to greater than approximately 10 MeV. GRIPS will address questions raised by recent solar flare observations regarding particle acceleration and energy release, such as: What causes the spatial separation between energetic electrons producing hard X-rays and energetic ions producing gamma-ray lines? How anisotropic are the relativistic electrons, and why can they dominate in the corona? How do the compositions of accelerated and ambient material vary with space and time, and why? The spectrometer/polarimeter consists of sixteen 3D position-sensitive germanium detectors (3D-GeDs), where each energy deposition is individually recorded with an energy resolution of a few keV FWHM and a spatial resolution of less than 0.1 cubic millimeter. Imaging is accomplished by a single multi-pitch rotating modulator (MPRM), a 2.5-centimeter thick tungsten alloy slit/slat grid with pitches that range quasi-continuously from 1 to 13 millimeters. The MPRM is situated 8 meters from the spectrometer to provide excellent image quality and unparalleled angular resolution at gamma-ray energies (12.5 arcsec FWHM), sufficient to separate 2.2 MeV footpoint sources for almost all flares. Polarimetry is accomplished by analyzing the anisotropy of reconstructed Compton scattering in the 3D-GeDs (i.e., as an active scatterer), with an estimated minimum detectable polarization of a few percent at 150-650 keV in an X-class flare. GRIPS is scheduled for a continental-US engineering test flight in fall 2013, followed by long or ultra-long duration balloon flights in Antarctica.