Polytropic behavior in the structures of Interplanetary Coronal Mass Ejections

The polytropic process characterizes the thermodynamics of space plasma particle populations. The polytropic index, ${\gamma}$, is particularly important as it describes the thermodynamic behavior of the system by quantifying the changes in temperature as the system is compressed or expanded. Using Wind spacecraft plasma and magnetic field data during $01/1995 - 12/2018$, we investigate the thermodynamic evolution in 336 Interplanetary Coronal Mass Ejection (ICME) events. For each event, we derive the index ${\gamma}$ in the sheath and magnetic ejecta structures, along with the pre- and post- event regions. We then examine the distributions of all ${\gamma}$ indices in these four regions and derive the entropic gradient of each, which is indicative of the ambient heating. We find that in the ICME sheath region, where wave turbulence is expected to be highest, the thermodynamics takes longest to recover into the original quasi-adiabatic process, while it recovers faster in the quieter ejecta region. This pattern creates a thermodynamic cycle, featuring a near adiabatic value ${\gamma}$ ~ ${\gamma}$${_a}$ (=5/3) upstream of the ICMEs, ${\gamma}$${_a}$ - ${\gamma}$ ~ 0.26 in the sheaths, ${\gamma}$${_a}$ - ${\gamma}$ ~ 0.13 in the ICME ejecta, and recovers again to ${\gamma}$ ~ ${\gamma}$${_a}$ after the passage of the ICME. These results expose the turbulent heating rates in the ICME plasma: the lower the polytropic index from its adiabatic value and closer to its isothermal value, the larger the entropic gradient, and thus, the rate of turbulent heating that heats the ICME plasma.Comment: 9 pages, 3 figure

A Discrete Mathematical Model for the Aggregation of \u3ci\u3eβ-Amyloid\u3c/i\u3e

Dementia associated with the Alzheimer\u27s disease is thought to be correlated with the conversion of the β − Amyloid (Aβ) peptides from soluble monomers to aggregated oligomers and insoluble fibrils. We present a discrete-time mathematical model for the aggregation of Aβ monomers into oligomers using concepts from chemical kinetics and population dynamics. Conditions for the stability and instability of the equilibria of the model are established. A formula for the number of monomers that is required for producing oligomers is also given. This may provide compound designers a mechanism to inhibit the Aβ aggregation

Investigating the IBEX Ribbon Structure a Solar Cycle Apart

A Ribbon of enhanced energetic neutral atom (ENA) emissions was discovered by the Interstellar Boundary Explorer (IBEX) in 2009, redefining our understanding of the heliosphere boundaries and the physical processes occurring at the interstellar interface. The Ribbon signal is intertwined with that of a globally distributed flux (GDF) that spans the entire sky. To a certain extent, Ribbon separation methods enabled examining its evolution independent of the underlying GDF. Observations over a full solar cycle revealed the Ribbon's evolving nature, with intensity variations closely tracking those of the solar wind (SW) structure after a few years delay accounting for the SW-ENA recycling process. In this work, we examine the Ribbon structure, namely, its ENA fluxes, angular extent, width, and circularity properties for two years, 2009 and 2019, representative of the declining phases of two adjacent solar cycles. We find that, (i) the Ribbon ENA fluxes have recovered in the nose direction and south of it down to ~ 25{\deg} (for energies below 1.7 keV) and not at mid and high ecliptic latitudes; (ii) The Ribbon width exhibits significant variability as a function of azimuthal angle; (iii) Circularity analysis suggests that the 2019 Ribbon exhibits a statistically consistent radius with that in 2009. The Ribbon's partial recovery is aligned with the consensus of a heliosphere with its closest point being southward of the nose region. The large variability of the Ribbon width as a function of Azimuth in 2019 compared to 2009 is likely indicative of small-scale processes within the Ribbon.Comment: 5 figure

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

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)

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 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

On the seed population of solar energetic particles in the inner heliosphere

Particles measured in large gradual solar energetic particle (SEP) events are believed to be predominantly accelerated at shocks driven by coronal mass ejections (CMEs). Ion charge state and composition analyses suggest that the origin of the seed particle population for the mechanisms of particle acceleration at CME-driven shocks is not the bulk solar wind thermal material, but rather a suprathermal population present in the solar wind. This suprathermal population could result from remnant material accelerated in prior solar flares and/or preceding CME-driven shocks. In this work, we examine the distribution of this suprathermal particle population in the inner heliosphere by combining a magnetohydrodynamic (MHD) simulation of the solar wind and a Monte-Carlo simulation of particle acceleration and transport. Assuming that the seed particles are uniformly distributed near the Sun by solar flares of various magnitudes, we study the longitudinal distribution of the seed population at multiple heliocentric distances. We consider a non-uniform background solar wind, consisting of fast and slow streams that lead to compression and rarefaction regions within the solar wind. Our simulations show that the seed population at a particular location (e.g., 1 au) is strongly modulated by the underlying solar wind configuration. Corotating interaction regions (CIRs) and merged interactions regions (MIRs) can strongly alter the energy spectra of the seed particle populations. In addition, cross-field diffusion plays an important role in mitigating strong variations of the seed population in both space and energy.Comment: 20 pages, 7 figure