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Solar filaments are well-known tracers of polarity inversion lines that separate two opposite magnetic polarities on the solar photosphere. Because observations of filaments began long before the systematic observations of solar magnetic fields, historical filament catalogs can facilitate the reconstruction of magnetic polarity maps at times when direct magnetic observations were not yet available. In practice, this reconstruction is often ambiguous and typically performed manually. We propose an automatic approach based on a machine-learning model that generates a variety of magnetic polarity maps consistent with filament observations. To evaluate the model and discuss the results we use the catalog of solar filaments and polarity maps compiled by McIntosh. We realize that the process of manual compilation of polarity maps includes not only information on filaments, but also a large amount of prior information, which is difficult to formalize. In order to compensate for the lack of prior knowledge for the machine-learning model, we provide it with polarity information at several reference points. We demonstrate that this process, which can be considered as the user-guided reconstruction or super-resolution, leads to polarity maps that are reasonably close to hand-drawn ones, and additionally allows for uncertainty estimation.

Graphene is a promising material for applications as a channel in graphene field-effect transistors (GFETs) which may be used as a building block for optoelectronics, high-frequency devices and sensors. However, these devices require gate insulators which ideally should form atomically flat interfaces with graphene and at the same time contain small densities of traps to maintain high device stability. Previously used amorphous oxides, such as SiO2 and Al2O3, however, typically suffer from oxide dangling bonds at the interface, high surface roughness and numerous border oxide traps. In order to address these challenges, here we use for the first time 2nm thick epitaxial CaF2 as a gate insulator in GFETs. By analyzing device-to-device variability for over 200 devices fabricated in two batches, we find that tens of them show similar gate transfer characteristics. Our statistical analysis of the hysteresis up to 175C has revealed that while an ambient-sensitive counterclockwise hysteresis can be present in some devices, the dominant mechanism is thermally activated charge trapping by border defects in CaF2 which results in the conventional clockwise hysteresis. We demonstrate that both the hysteresis and bias-temperature instabilities in our GFETs with CaF2 are comparable to similar devices with SiO2 and Al2O3. In particular, we achieve a small hysteresis below 0.01 V for equivalent oxide thickness (EOT) of about 1 nm at the electric fields up to 15 MV/cm and sweep times in the kilosecond range. Thus, our results demonstrate that crystalline CaF2 is a promising insulator for highly-stable GFETs.

We propose a method for including the effects of non-Gaussian velocity field distribution in the estimation of growth rate of the magnetic energy in a random flow with finite memory time. The method allows a reduction to the Gaussian case that was investigates earlier. For illustration we consider the multivariate Laplace distribution and compare it against the Gaussian one.

French astronomer Honor\'e Flaugergues compiled astronomical observations in a series of hand-written notebooks for 1782$\unicode{x2013}$1830, which are preserved at Paris Observatory. We reviewed these manuscripts and encoded the records that contain sunspot measurements into a numerical table for further analysis. All measurements are timings and we found three types of measurements allowing the reconstruction of heliographic coordinates. In the first case, the Sun and sunspots cross vertical and horizontal wires, in the second case, one vertical and two mirror-symmetric oblique wires, and in the third case, a rhombus-shaped set of wires. Additionally, timings of two solar eclipses also provided a few sunspot coordinates. As a result, we present the time--latitude (butterfly) diagram of the reconstructed sunspot coordinates, which covers the period of the Dalton Minimum and confirms consistency with those of Derfflinger and Prantner. We identify four solar cycles in this diagram and discuss the observed peculiarities as well as the data reliability.

We consider a model of a random media with fixed and finite memory time with abrupt losses of memory (renovation model). Within the memory intervals we can observe either amplification or oscillation of the vector field in a given particle. The cumulative effect of amplifications in many subsequent intervals leads to amplification of the mean field and mean energy. Similarly, the cumulative effect of intermittent amplifications or oscillations also leads to amplification of the mean field and mean energy, however, at a lower rate. Finally, the random oscillations alone can resonate and yield the growth of the mean field and energy. These are the three mechanisms that we investigate and compute analytically and numerically the growth rates based on the Jacobi equation with the random curvature parameter.

The flux of energetic particles originating from the Sun fluctuates during the solar cycles. It depends on the number and properties of Active Regions (ARs) present in a single day and associated solar activities, such as solar flares and coronal mass ejections (CMEs). Observational records of the Space Weather Prediction Center (SWPC NOAA) enable the creation of time-indexed databases containing information about ARs and particle flux enhancements, most widely known as Solar Energetic Particle events (SEPs). In this work, we utilize the data available for Solar Cycles 21-24, and the initial phase of Cycle 25 to perform a statistical analysis of the correlation between SEPs and properties of ARs inferred from the McIntosh and Hale classifications. We find that the complexity of the magnetic field, longitudinal location, area, and penumbra type of the largest sunspot of ARs are most correlated with the production of SEPs. It is found that most SEPs ($\approx$60\%, or 108 out of 181 considered events) were generated from an AR classified with the 'k' McIntosh subclass as the second component, and these ARs are more likely to produce SEPs if they fall in a Hale class containing $\delta$ component. The resulting database containing information about SEP events and ARs is publicly available and can be used for the development of Machine Learning (ML) models to predict the occurrence of SEPs., Comment: 27 pages, 7 figures, 1 table

Solar Energetic Particle (SEP) events and their major subclass, Solar Proton Events (SPEs), can have unfavorable consequences on numerous aspects of life and technology, making them one of the most harmful effects of solar activity. Garnering knowledge preceding such events by studying operational data flows is essential for their forecasting. Considering only Solar Cycle (SC) 24 in our previous study, Sadykov et al. 2021, we found that it may be sufficient to utilize only proton and soft X-ray (SXR) parameters for SPE forecasts. Here, we report a catalog recording $\geq$ 10 MeV $\geq$ 10 particle flux unit SPEs with their properties, spanning SCs 22-24, using NOAA's Geostationary Operational Environmental Satellite flux data. We report an additional catalog of daily proton and SXR flux statistics for this period, employing it to test the application of machine learning (ML) on the prediction of SPEs using a Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost). We explore the effects of training models with data from one and two SCs, evaluating how transferable a model can be across different time periods. XGBoost proved to be more accurate than SVMs for almost every test considered, while outperforming operational SWPC NOAA predictions and a persistence forecast. Interestingly, training done with SC 24 produces weaker TSS and HSS2, even when paired with SC 22 or SC 23, indicating transferability issues. This work contributes towards validating forecasts using long-spanning data -- an understudied area in SEP research that should be considered to verify the cross-cycle robustness of ML-driven forecasts.

Catalogs of the Zurich Observatory contain positional information on sunspots, prominences and faculae in late 19th and early 20th centuries. This database is given in handwritten tabular form and was not systematically analysed earlier. It is different from the sunspot number time series made in Zurich and was obtained with a larger telescope. We trained a neural-network model for handwritten text recognition and present the database of reconstructed coordinates. The database obtained connects the earlier observations by Sp\"orer with later programs of the 20th century and supplements the sunspot-group catalogs of the Royal Greenwich Observatory. We also expect that the presented machine-learning approach and its deep capabilities will motivate the processing of a wide bulk of astronomical data, which is still given in non-digitized form or as plain scanned images.

Sunspot groups observed in white-light appear as complex structures. Analysis of these structures is usually based on simple morphological descriptors which capture only generic properties and miss information about fine details. We present a machine learning approach to introduce a complete yet compact description of sunspot groups. The idea is to map sunspot group images into an appropriate lower-dimensional (latent) space. We apply a combination of Variational Autoencoder and Principal Component Analysis to obtain a set of 285 latent descriptors. We demonstrate that the standard descriptors are embedded into the latent ones. Thus, latent features can be considered as an extended description of sunspot groups and, in our opinion, can expand the possibilities for the research on sunspot groups. In particular, we demonstrate an application for estimation of the sunspot group complexity. The proposed parametrization model is generic and can be applied to investigation of other traces of solar activity observed in various spectrum lines. Key components of this work, which are the parametrization model, dataset of sunspot groups and latent vectors, are available in the public GitHub repository github.com/observethesun/sunspot_groups and can be used to reproduce the results and for further research.

The dynamo mechanism is a process of magnetic field self-excitation in a moving electrically conducting fluid. One of the most interesting applications of this mechanism related to the astrophysical systems is the case of a random motion of plasma. For the very first stage of the process, the governing dynamo equation can be reduced to a system of first order ordinary differential equations. For this case we suggest a regular method to calculate the growth rate of magnetic energy. Based on this method we calculate the growth rate for random flow with finite memory time and anisotropic statistical distribution of the stretching matrix and compare the results with corresponding ones for isotropic case and for short-correlated approximation. We find that for moderate Strouhal numbers and moderate anisotropy the analytical results reproduce the numerically estimated growth rates reasonably well, while for larger governing parameters the quantitative difference becomes substantial. In particular, analytical approximation is applicable for the Strouhal numbers s<0.6 and we find some numerical models and observational examples for which this region might be relevant. Rather unexpectedly, we find that the mirror asymmetry does not contribute to the growth rates obtained, although the mirror asymmetry effects are known to be crucial for later stages of dynamo action.

Solar Energetic Particle events (SEPs) are among the most dangerous transient phenomena of solar activity. As hazardous radiation, SEPs may affect the health of astronauts in outer space and adversely impact current and future space exploration. In this paper, we consider the problem of daily prediction of Solar Proton Events (SPEs) based on the characteristics of the magnetic fields in solar Active Regions (ARs), preceding soft X-ray and proton fluxes, and statistics of solar radio bursts. The machine learning (ML) algorithm uses an artificial neural network of custom architecture designed for whole-Sun input. The predictions of the ML model are compared with the SWPC NOAA operational forecasts of SPEs. Our preliminary results indicate that 1) for the AR-based predictions, it is necessary to take into account ARs at the western limb and on the far side of the Sun; 2) characteristics of the preceding proton flux represent the most valuable input for prediction; 3) daily median characteristics of ARs and the counts of type II, III, and IV radio bursts may be excluded from the forecast without performance loss; and 4) ML-based forecasts outperform SWPC NOAA forecasts in situations in which missing SPE events is very undesirable. The introduced approach indicates the possibility of developing robust "all-clear" SPE forecasts by employing machine learning methods., Comment: 22 pages, 8 figures, 4 tables

Despite the enormous progress achieved during the past decade, nanoelectronic devices based on two-dimensional (2D) semiconductors still suffer from a limited electrical stability. This limited stability has been shown to result from the interaction of charge carriers originating from the 2D semiconductors with defects in the surrounding insulating materials. The resulting dynamically trapped charges are particularly relevant in field effect transistors (FETs) and can lead to a large hysteresis, which endangers stable circuit operation. Based on the notion that charge trapping is highly sensitive to the energetic alignment of the channel Fermi-level with the defect band in the insulator, we propose to optimize device stability by deliberately tuning the channel Fermi-level. Our approach aims to minimize the amount of electrically active border traps without modifying the total number of traps in the insulator. We demonstrate the applicability of this idea by using two differently doped graphene layers in otherwise identical FETs with Al$_2$O$_3$ as a gate oxide mounted on a flexible substrate. Our results clearly show that by increasing the distance of the Fermi-level to the defect band, the hysteresis is significantly reduced. Furthermore, since long-term reliability is also very sensitive to trapped charges, a corresponding improvement in reliability is both expected theoretically and demonstrated experimentally. Our study paves the way for the construction of more stable and reliable 2D FETs in which the channel material is carefully chosen and tuned to maximize the energetic distance between charge carriers in the channel and the defect bands in the insulator employed., Comment: 28 pages, 6 figures

Coronal holes are the observational manifestation of the solar magnetic field open to the heliosphere and are of pivotal importance for our understanding of the origin and acceleration of the solar wind. Observations from space missions such as the Solar Dynamics Observatory now allow us to study coronal holes in unprecedented detail. Instrumental effects and other factors, however, pose a challenge to automatically detect coronal holes in solar imagery. The science community addresses these challenges with different detection schemes. Until now, little attention has been paid to assessing the disagreement between these schemes. In this COSPAR ISWAT initiative, we present a comparison of nine automated detection schemes widely-applied in solar and space science. We study, specifically, a prevailing coronal hole observed by the Atmospheric Imaging Assembly instrument on 2018 May 30. Our results indicate that the choice of detection scheme has a significant effect on the location of the coronal hole boundary. Physical properties in coronal holes such as the area, mean intensity, and mean magnetic field strength vary by a factor of up to 4.5 between the maximum and minimum values. We conclude that our findings are relevant for coronal hole research from the past decade, and are therefore of interest to the solar and space research community., Comment: Accepted for publication in The Astrophysical Journal. (Received January 20, 2021; Accepted March 25, 2021)

In this study we extract the deep features and investigate the compression of the Mg II k spectral line profiles observed in quiet Sun regions by NASA's IRIS satellite. The data set of line profiles used for the analysis was obtained on April 20th, 2020, at the center of the solar disc, and contains almost 300,000 individual Mg II k line profiles after data cleaning. The data are separated into train and test subsets. The train subset was used to train the autoencoder of the varying embedding layer size. The early stopping criterion was implemented on the test subset to prevent the model from overfitting. Our results indicate that it is possible to compress the spectral line profiles more than 27 times (which corresponds to the reduction of the data dimensionality from 110 to 4) while having a 4 DN average reconstruction error, which is comparable to the variations in the line continuum. The mean squared error and the reconstruction error of even statistical moments sharply decrease when the dimensionality of the embedding layer increases from 1 to 4 and almost stop decreasing for higher numbers. The observed occasional improvements in training for values higher than 4 indicate that a better compact embedding may potentially be obtained if other training strategies and longer training times are used. The features learned for the critical four-dimensional case can be interpreted. In particular, three of these four features mainly control the line width, line asymmetry, and line dip formation respectively. The presented results are the first attempt to obtain a compact embedding for spectroscopic line profiles and confirm the value of this approach, in particular for feature extraction, data compression, and denoising., Comment: 6 pages, 3 figures, submitted to CBMI 2021 (Special session: Mining and indexing multimedia data for remote sensing of the environment and our changing planet)

Reservoir simulation and adaptation (also known as history matching) are typically considered as separate problems. While a set of models are aimed at the solution of the forward simulation problem assuming all initial geological parameters are known, the other set of models adjust geological parameters under the fixed forward simulation model to fit production data. This results in many difficulties for both reservoir engineers and developers of new efficient computation schemes. We present a unified approach to reservoir simulation and adaptation problems. A single neural network model allows a forward pass from initial geological parameters of the 3D reservoir model through dynamic state variables to well's production rates and backward gradient propagation to any model inputs and variables. The model fitting and geological parameters adaptation both become the optimization problem over specific parts of the same neural network model. Standard gradient-based optimization schemes can be used to find the optimal solution. Using real-world oilfield model and historical production rates we demonstrate that the suggested approach allows reservoir simulation and history matching with a benefit of several orders of magnitude simulation speed-up. Finally, to propagate this research we open-source a Python-based framework DeepField that allows standard processing of reservoir models and reproducing the approach presented in this paper.

Complementary metal oxide semiconductor (CMOS) logic circuits at the ultimate scaling limit place the utmost demands on the properties of all materials involved. The requirements for semiconductors are well explored and could possibly be satisfied by a number of layered two-dimensional (2D) materials, like for example transition-metal dichalcogenides or black phosphorus. The requirements for the gate insulator are arguably even more challenging and difficult to meet. In particular the combination of insulator to semiconductor which forms the central element of the metal oxide semiconductor field effect transistor (MOSFET) has to be of superior quality in order to build competitive devices. At the moment, hexagonal boron nitride (hBN) is the most common two-dimensional insulator and widely considered to be the most promising gate insulator in nanoscaled 2D material-based transistors. Here, we critically assess the material parameters of hBN and conclude that while its properties render hBN an ideal candidate for many applications in 2D nanoelectronics, hBN is most likely not suitable as a gate insulator for ultrascaled CMOS devices., Comment: 10 pages, 4 figures

The authors of this white paper met on 16-17 January 2020 at the New Jersey Institute of Technology, Newark, NJ, for a 2-day workshop that brought together a group of heliophysicists, data providers, expert modelers, and computer/data scientists. Their objective was to discuss critical developments and prospects of the application of machine and/or deep learning techniques for data analysis, modeling and forecasting in Heliophysics, and to shape a strategy for further developments in the field. The workshop combined a set of plenary sessions featuring invited introductory talks interleaved with a set of open discussion sessions. The outcome of the discussion is encapsulated in this white paper that also features a top-level list of recommendations agreed by participants., Comment: Workshop Report

Identification of solar coronal holes (CHs) provides information both for operational space weather forecasting and long-term investigation of solar activity. Source data for the first problem are typically most recent solar disk observations, while for the second problem it is convenient to consider solar synoptic maps. Motivated by the idea that the concept of CHs should be similar for both cases we investigate universal models that can learn a CHs segmentation in disk images and reproduce the same segmentation in synoptic maps. We demonstrate that Convolutional Neural Networks (CNN) trained on daily disk images provide an accurate CHs segmentation in synoptic maps and their pole-centric projections. Using this approach we construct a catalog of synoptic maps for the period of 2010-20 based on SDO/AIA observations in the 193 Angstrom wavelength. The obtained CHs synoptic maps are compared with magnetic synoptic maps in the time-latitude and time-longitude diagrams. The initial results demonstrate that while in some cases the CHs are associated with magnetic flux transport events there are other mechanisms contributing to the CHs formation and evolution. To stimulate further investigations the catalog of synoptic maps is published in open access.

Results of testing modified components for the cryomodule of the International Linear Collider (ILC) are summarized. To reduce the ILC project cost, it is proposed to replace titanium cryomodule components with stainless steel (SS) ones. New bimetallic transitions Ti_SS, Nb_SS have been produced by a unique method based on explosion welding. Successive upgrading of these components to the latest version of the Nb/Ti/SS transition element has led to improvement of the ILC cryomodule. This new component resolves problems of residual stress, and its specific design prevents the possibility of a shift due to the difference in the linear expansion coefficients of the constituent metals. Leak tests with the He gas revealed no leaks at the background rate of 0.2x10-10atmxcc-1s. The test results are very encouraging. The up-to-date design of trimetallic Nb_Ti_SS element promises technologically simpler and less expensive manufacture. Investigations have shown that explosion welding allows unique trimetallic components to be made not only for cryogenic units of accelerators but also for laboratory equipment and for general engineering applications., Comment: 17 figures, 16 pages, 1 Table

Two-dimensional (2D) semiconductors have been suggested both for ultimately-scaled field-effect transistors (FETs) and More-than-Moore nanoelectronics. However, these targets can not be reached without accompanying gate insulators which are scalable into the nanometer regime. Despite the considerable progress in the search for channel materials with high mobilities and decent bandgaps, finding high-quality insulators compatible with 2D technologies has remained a challenge. Typically used oxides (e.g. SiO2, Al2O3 and HfO2) are amorphous when scaled, while two-dimensional hBN exhibits excessive gate leakages. To overcome this bottleneck, we extend the natural stacking properties of 2D heterostructures to epitaxial fluorite (CaF2), which forms a quasi van der Waals interface with 2D semiconductors. We report scalable single-layer MoS2 FETs with a crystalline CaF2 insulator of about 2 nm thickness, which corresponds to an equivalent oxide thickness of less than 1 nm. While meeting the stringent requirements of low leakage currents, our devices exhibit highly competitive performance and record-small hysteresis. As such, our results present a breakthrough for very large scale integration towards commercially competitive nano-electronic devices., Comment: The final publisher of this manuscript does not bare any responsibility for possible errors in this document

Current coronal holes segmentation methods typically rely on image thresholding and require non-trivial image pre- and post-processing. We have trained a neural network that accurately isolates CHs from SDO/AIA 193 Angstrom solar disk images without additional complicated steps. We compare results with publicly available catalogues of CHs and demonstrate stability of the neural network approach. In our opinion, this approach can outperform hand-engineered solar image analysis and will have a wide application to solar data. In particular, we investigate long-term variations of CH indices within the solar cycle 24 and observe increasing of CH areas in about three times from minimal values in the maximum of the solar cycle to maximal values during the declining phase of the solar cycle.

State-of-the-art machine learning algorithms demonstrate close to absolute performance in selected challenges. We provide arguments that the reason can be in low variability of the samples and high effectiveness in learning typical patterns. Due to this fact, standard performance metrics do not reveal model capacity and new metrics are required for the better understanding of state-of-the-art.