Polar Network Index as a magnetic proxy for the solar cycle studies

The Sun has a polar magnetic field which oscillates with the 11 year sunspot cycle. This polar magnetic field is an important component of the dynamo process which is operating in the solar convection zone and produces the sunspot cycle. We have systematic direct measurements of the Sun's polar magnetic field only from about mid 1970s. There are, however, indirect proxies which give us information about this field at earlier times. The Ca K spectroheliograms taken in Kodaikanal Solar Observatory during 1904 - 2007 have now been digitized with the 4k x 4k CCD and have higher resolution (0.86 arcsec) than the other available historical datasets. From these Ca-K spectroheliograms, we have developed a completely new proxy (Polar Network Index, PNI) for the Sun's polar magnetic field. We calculate the PNI from the digitized images using an automated algorithm and calibrate our measured PNI against the polar field as measured by the Wilcox Solar Observatory for the period of 1976 - 1990. This calibration allows us to estimate polar fields for the earlier period up to 1904. The dynamo calculations done with this proxy as input data reproduce the Sun's magnetic behavior for the past century reasonably well.Comment: 19 pages, 5 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)

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

The Extended Solar Cycle: Muddying the Waters of Solar/Stellar Dynamo Modeling or Providing Crucial Observational Constraints?

In 1844 Schwabe discovered that the number of sunspots increased and decreased over a period of about 11 years, that variation became known as the sunspot cycle. Almost eighty years later, Hale described the nature of the Sun's magnetic field, identifying that it takes about 22 years for the Sun's magnetic polarity to cycle. It was also identified that the latitudinal distribution of sunspots resembles the wings of a butterfly—showing migration of sunspots in each hemisphere that abruptly start at mid-latitudes (about ±35o) toward the Sun's equator over the next 11 years. These sunspot patterns were shown to be asymmetric across the equator. In intervening years, it was deduced that the Sun (and sun-like stars) possess magnetic activity cycles that are assumed to be the physical manifestation of a dynamo process that results from complex circulatory transport processes in the star's interior. Understanding the Sun's magnetism, its origin and its variation, has become a fundamental scientific objective—the distribution of magnetism, and its interaction with convective processes, drives various plasma processes in the outer atmosphere that generate particulate, radiative, eruptive phenomena, and shape the heliosphere. In the past few decades, a range of diagnostic techniques have been employed to systematically study finer scale magnetized objects, and associated phenomena. The patterns discerned became known as the “Extended Solar Cycle” (ESC). The patterns of the ESC appeared to extend the wings of the activity butterfly back in time, nearly a decade before the formation of the sunspot pattern, and to much higher solar latitudes. In this short review, we describe their observational patterns of the ESC and discuss possible connections to the solar dynamo as we depart on a multi-national collaboration to investigate the origins of solar magnetism through a blend of archived and contemporary data analysis with the goal of improving solar dynamo understanding and modeling

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Mapping research on customer centricity and sustainable organizations

Firms are increasingly organized around the client. At the same time, there is customer pressure on green and sustainable organizations. The purpose of this paper is to map the current state of the research in the domain of customer-centric organizations from a sustainability perspective. We conducted a bibliometric analysis from published documents between 1990 and 31 July 2020. Key findings indicate that research on customer centricity and sustainability has increased in recent years, finding some trends and that the topic is structured into three clusters: (1) Sustainable Development, Customer-Centric Perspective, and Sales; (2) Sustainability and Commerce; and (3) Customer-Centricity and Sustainability Trends. The implementation of a bibliometric methodology and the focus given to the definition, the relationships, and the evolution of the three main clusters within the topic are the characteristics that differentiate our study from other publications or reviews in the field of research. In addition, all the documents that refer to practical cases were identified, and the main ones were analyzed, to provide highlights to practitioners who aim to deploy the customer centricity approach in their firms from a sustainable perspective and seeking that the corporate purpose is followed

Application of historic datasets to understanding open solar flux and the 20th-century Grand Solar Maximum. 2. Solar observations

We study historic observations of solar activity from the 20th-century rise towards the peak of the Modern Grand Solar Maximum (MGSM) and compare with observations of the decline that has occurred since. The major difference in available solar observations of the rise and of the fall are accurate magnetograms from solar magnetographs: we here use synthetic magnetograms to interpret the rise and employ historic observations of Polar Crown Filaments to test them and verify their use. We show that eclipse images at sunspot minimum reveal the long-term variation of open flux deduced from geomagnetic observations in Paper 1 (Lockwood et al, 2022). We also make use of polar coronal hole fluxes derived from historic white light images of polar faculae, but have to consider the implications of the fact that these facular images do not tell us the polarity of the field. Given this caveat, the agreement between the polar coronal hole fluxes and the values derived from open flux continuity modelling based on sunspot numbers is extremely good. This comparison indicates that one possible solution to the "open flux problem" is open flux within the streamer belt that potential-based modelling of coronal fields from photospheric fields is not capturing. We take a detailed look at the solar cycle at the peak of the MGSM, cycle 19, and show the variation of the polar coronal hole fluxes and the inferred poleward flux surges are predictable from the asymmetries in flux emergence in the two hemispheres with implied transequatorial flux transfer and/or "anti-Hale" (or more general "rogue" active region flux emergence) late in the sunspot cycle

Solar EUV Spectral Irradiance by Deep Learning

Extreme UV (EUV) radiation from the Sun's transition region and corona is an important driver for the energy balance of the Earth's thermosphere and ionosphere. To characterise and monitor solar forcing on this system and associated space weather impacts, the EUV Variability Experiment (EVE) instrument onboard NASA's Solar Dynamics Observatory (SDO) was designed to measure solar spectral irradiance (SSI) in the 0.1 to 105 nm wavelength range. As the result of an electrical short, the MEGS-A component of EVE stopped delivering SSI data in the 5 - 35 nm wavelength range in May 2014. We demonstrate how a Residual Neural Network (ResNet) augmented with a Multi-Layer Perceptron (MLP) can fill this gap using narrowband UV and EUV images from the Atmospheric Imaging Assembly (AIA) on SDO. As a performance benchmark, we also show how our deep learning approach outperforms a physics model based on differential emission measure inversions. This work was performed at NASA's Frontier Development Lab, a public-private initiative to apply AI techniques to accelerate space science discovery and exploration

Solar EUV Spectral Irradiance by Deep Learning

Extreme UV (EUV) radiation from the Sun's transition region and corona is an important driver for the energy balance of the Earth's thermosphere and ionosphere. To characterise and monitor solar forcing on this system and associated space weather impacts, the EUV Variability Experiment (EVE) instrument onboard NASA's Solar Dynamics Observatory (SDO) was designed to measure solar spectral irradiance (SSI) in the 0.1 to 105 nm wavelength range. As the result of an electrical short, the MEGS-A component of EVE stopped delivering SSI data in the 5 - 35 nm wavelength range in May 2014. We demonstrate how a Residual Neural Network (ResNet) augmented with a Multi-Layer Perceptron (MLP) can fill this gap using narrowband UV and EUV images from the Atmospheric Imaging Assembly (AIA) on SDO. As a performance benchmark, we also show how our deep learning approach outperforms a physics model based on differential emission measure inversions. This work was performed at NASA's Frontier Development Lab, a public-private initiative to apply AI techniques to accelerate space science discovery and exploration