Spherical Harmonic Representation of Energetic Neutral Atom Flux Components Observed by IBEX

The Interstellar Boundary Explorer (IBEX) images the heliosphere by observing energetic neutral atoms (ENAs). The IBEX-Hi instrument onboard IBEX provides full-sky maps of ENA fluxes produced in the heliosphere and very local interstellar medium (VLISM) through charge exchange of suprathermal ions with interstellar neutral atoms. The first IBEX-Hi results showed that in addition to the anticipated globally distributed flux (GDF), a narrow and bright emission from a circular region in the sky, dubbed the IBEX ribbon, is visible in all energy steps. While the GDF is mainly produced in the inner heliosheath, ample evidence indicates that the ribbon forms outside the heliopause in the regions where the interstellar magnetic field is perpendicular to the lines of sight. The IBEX maps produced by the mission team distribute the observations into $6\deg\times6\deg$ rectangle pixels in ecliptic coordinates. The overlap of the GDF and ribbon components complicates qualitative analyses of each source. Here, we find the spherical harmonic representation of the IBEX maps, separating the GDF and ribbon components. This representation describes the ENA flux components in the sky without relying on any pixelization scheme. Using this separation, we discuss the temporal evolution of each component over the solar cycle. We find that the GDF is characterized by larger spatial scale structures than the ribbon. However, we identify two isolated, small-scale signals in the GDF region that require further study.Comment: 27 pages, 13 figures, v2 accepted for publication in ApJ

Earth‐Moon‐Mars Radiation Environment Module framework

[1] We are preparing to return humans to the Moon and setting the stage for exploration to Mars and beyond. However, it is unclear if long missions outside of low-Earth orbit can be accomplished with acceptable risk. The central objective of a new modeling project, the Earth-Moon-Mars Radiation Exposure Module (EMMREM), is to develop and validate a numerical module for characterizing time-dependent radiation exposure in the Earth-Moon-Mars and interplanetary space environments. EMMREM is being designed for broad use by researchers to predict radiation exposure by integrating over almost any incident particle distribution from interplanetary space. We detail here the overall structure of the EMMREM module and study the dose histories of the 2003 Halloween storm event and a June 2004 event. We show both the event histories measured at 1 AU and the evolution of these events at observer locations beyond 1 AU. The results are compared to observations at Ulysses. The model allows us to predict how the radiation environment evolves with radial distance from the Sun. The model comparison also suggests areas in which our understanding of the physics of particle propagation and energization needs to be improved to better forecast the radiation environment. Thus, we introduce the suite of EMMREM tools, which will be used to improve risk assessment models so that future human exploration missions can be adequately planned for

First IBEX observations of the terrestrial plasma sheet and a possible disconnection event

The Interstellar Boundary Explorer (IBEX) mission has recently provided the first all-sky maps of energetic neutral atoms (ENAs) emitted from the edge of the heliosphere as well as the first observations of ENAs from the Moon and from the magnetosheath stagnation region at the nose of the magnetosphere. This study provides the first IBEX images of the ENA emissions from the nightside magnetosphere and plasma sheet. We show images from two IBEX orbits: one that displays typical plasma sheet emissions, which correlate reasonably well with a model magnetic field, and a second that shows a significant intensification that may indicate a near-Earth (similar to 10 R(E) behind the Earth) disconnection event. IBEX observations from similar to 0.5-6 keV indicate the simultaneous addition of both a hot (several keV) and colder (similar to 700 eV) component during the intensification; if IBEX directly observed magnetic reconnection in the magnetotail, the hot component may signify the plasma energization

Homogenising SoHO/EIT and SDO/AIA 171\AA ~ Images: A Deep Learning Approach

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 ApJ

Evolving outer heliosphere: Large-scale stability and time variations observed by the Interstellar Boundary Explorer

The first all-sky maps of Energetic Neutral Atoms (ENAs) from the Interstellar Boundary Explorer (IBEX) exhibited smoothly varying, globally distributed flux and a narrow ribbon of enhanced ENA emissions. In this study we compare the second set of sky maps to the first in order to assess the possibility of temporal changes over the 6 months between views of each portion of the sky. While the large-scale structure is generally stable between the two sets of maps, there are some remarkable changes that show that the heliosphere is also evolving over this short timescale. In particular, we find that (1) the overall ENA emissions coming from the outer heliosphere appear to be slightly lower in the second set of maps compared to the first, (2) both the north and south poles have significantly lower (similar to 10-15%) ENA emissions in the second set of maps compared to the first across the energy range from 0.5 to 6 keV, and (3) the knot in the northern portion of the ribbon in the first maps is less bright and appears to have spread and/or dissipated by the time the second set was acquired. Finally, the spatial distribution of fluxes in the southernmost portion of the ribbon has evolved slightly, perhaps moving as much as 6 degrees (one map pixel) equatorward on average. The observed large-scale stability and these systematic changes at smaller spatial scales provide important new information about the outer heliosphere and its global interaction with the galaxy and help inform possible mechanisms for producing the IBEX ribbon

MEMPSEP III. A machine learning-oriented multivariate data set for forecasting the Occurrence and Properties of Solar Energetic Particle Events using a Multivariate Ensemble Approach

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)

MEMPSEP I : Forecasting the Probability of Solar Energetic Particle Event Occurrence using a Multivariate Ensemble of Convolutional Neural Networks

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 forecastComment: 17 pages, 8 figures, 1 table, accepted for publication in Space Weather journa

Solar Cycle Variation of 0.3-1.29 MeV/nucleon Heavy Ion Composition during Quiet Times near 1 AU in Solar Cycles 23 and 24

We report on the annual variation of quiet-time suprathermal ion composition for C through Fe using Advanced Composition Explorer (ACE)/Ultra-Low Energy Isotope Spectrometer (ULEIS) data over the energy range 0.3 MeV/nuc to 1.28 MeV/nuc from 1998 through 2019, covering solar cycle 23's rising phase through Solar Cycle 24's declining phase. Our findings are (1) quiet time suprathermal abundances resemble CIR-associated particles during solar minima; (2) quiet time suprathermals are M/Q fractionated in a manner that is consistent with M/Q fractionation in large gradual solar energetic particle events (GSEP) during solar maxima; and (3) variability within the quiet time suprathermal pool increases as a function of M/Q and is consistent with the analogous variability in GSEP events. From these observations, we infer that quiet time suprathermal ions are remnants of CIRs in solar minima and GSEP events in solar maxima. Coincident with these results, we also unexpectedly show that S behaves like a low FIP ion in the suprathermal regime and therefore drawn from low FIP solar sources.Comment: Accepted in Astrophysical Journal. 19 pages, 10 figures, 4 table

MEMPSEP II. -- Forecasting the Properties of Solar Energetic Particle Events using a Multivariate Ensemble Approach

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

Strong tuning of Rashba spin orbit interaction in single InAs nanowires

A key concept in the emerging field of spintronics is the gate voltage or electric field control of spin precession via the effective magnetic field generated by the Rashba spin orbit interaction. Here, we demonstrate the generation and tuning of electric field induced Rashba spin orbit interaction in InAs nanowires where a strong electric field is created either by a double gate or a solid electrolyte surrounding gate. In particular, the electrolyte gating enables six-fold tuning of Rashba coefficient and nearly three orders of magnitude tuning of spin relaxation time within only 1 V of gate bias. Such a dramatic tuning of spin orbit interaction in nanowires may have implications in nanowire based spintronic devices.Comment: Nano Letters, in pres