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Current state-of-the-art spectrographs cannot resolve the fundamental spatial (sub-arcseconds) and temporal scales (less than a few tens of seconds) of the coronal dynamics of solar flares and eruptive phenomena. The highest resolution coronal data to date are based on imaging, which is blind to many of the processes that drive coronal energetics and dynamics. As shown by IRIS for the low solar atmosphere, we need high-resolution spectroscopic measurements with simultaneous imaging to understand the dominant processes. In this paper: (1) we introduce the Multi-slit Solar Explorer (MUSE), a spaceborne observatory to fill this observational gap by providing high-cadence (<20 s), sub-arcsecond resolution spectroscopic rasters over an active region size of the solar transition region and corona; (2) using advanced numerical models, we demonstrate the unique diagnostic capabilities of MUSE for exploring solar coronal dynamics, and for constraining and discriminating models of solar flares and eruptions; (3) we discuss the key contributions MUSE would make in addressing the science objectives of the Next Generation Solar Physics Mission (NGSPM), and how MUSE, the high-throughput EUV Solar Telescope (EUVST) and the Daniel K Inouye Solar Telescope (and other ground-based observatories) can operate as a distributed implementation of the NGSPM. This is a companion paper to De Pontieu et al. (2021; arXiv:2106.15584), which focuses on investigating coronal heating with MUSE., Comment: 42 pages, 22 figures, submitted to ApJ

The young magnetically active solar-like stars are efficient generators of ionizing radiation in the form of X-ray and Extreme UV (EUV) flux, stellar wind and eruptive events. These outputs are the critical factors affecting atmospheric escape and chemistry of (exo)planets around active stars. While X-ray fluxes and surface magnetic fields can be derived from observations, the EUV emission and wind mass fluxes, Coronal Mass Ejections and associated Stellar Energetic Particle events cannot be directly observed. Here, we present the results of a three-dimensional magnetohydrodynamic (MHD) model with inputs constrained by spectropolarimetric data, HST/STIS Far UV, X-ray data data and stellar magnetic maps reconstructed at two epochs separated by 11 months. The simulations show that over the course of the year, the global stellar corona had undergone a drastic transition from a simple dipole-like to a tilted dipole with multipole field components, and thus, provided favorable conditions for Corotating Interaction Events (CIRs) that drive strong shocks. The dynamic pressure exerted by CIRs are 1300 times larger than those observed from the Sun and can contribute to the atmospheric erosion of early Venus, Earth, Mars and young Earth-like exoplanets. Our data-constrained MHD model provides the framework to model coronal environments of G-M planet hosting dwarfs. The model outputs can serve as a realistic input for exoplanetary atmospheric models to evaluate the impact of stellar coronal emission, stellar winds and CIRs on their atmospheric escape and chemistry that can be tested in the upcoming JWST and ground-based observations., Comment: 28 pages, 8 figures

Solar activity plays a quintessential role in influencing the interplanetary medium and space-weather around the Earth. Remote sensing instruments onboard heliophysics space missions provide a pool of information about the Sun's activity via the measurement of its magnetic field and the emission of light from the multi-layered, multi-thermal, and dynamic solar atmosphere. Extreme UV (EUV) wavelength observations from space help in understanding the subtleties of the outer layers of the Sun, namely the chromosphere and the corona. Unfortunately, such instruments, like the Atmospheric Imaging Assembly (AIA) onboard NASA's Solar Dynamics Observatory (SDO), suffer from time-dependent degradation, reducing their sensitivity. Current state-of-the-art calibration techniques rely on periodic sounding rockets, which can be infrequent and rather unfeasible for deep-space missions. We present an alternative calibration approach based on convolutional neural networks (CNNs). We use SDO-AIA data for our analysis. Our results show that CNN-based models could comprehensively reproduce the sounding rocket experiments' outcomes within a reasonable degree of accuracy, indicating that it performs equally well compared with the current techniques. Furthermore, a comparison with a standard "astronomer's technique" baseline model reveals that the CNN approach significantly outperforms this baseline. Our approach establishes the framework for a novel technique to calibrate EUV instruments and advance our understanding of the cross-channel relation between different EUV channels., Comment: 12 pages, 7 figures, 8 tables. This is a pre-print of an article submitted and accepted by A&A Journal

Lithium has confused scientists for decades at almost each scale of the universe. Lithium-rich giants are peculiar stars with lithium abundances over model prediction. A large fraction of lithium-rich low-mass evolved stars are traditionally supposed to be red giant branch (RGB) stars. Recent studies, however, report that red clump (RC) stars are more frequent than RGB. Here, we present a uniquely large systematic study combining the direct asteroseismic analysis with the spectroscopy on the lithium-rich stars. The majority of lithium-rich stars are confirmed to be RCs, whereas RGBs are minor. We reveal that the distribution of lithium-rich RGBs steeply decline with the increasing lithium abundance, showing an upper limit around 2.6 dex, whereas the Li abundances of RCs extend to much higher values. We also find that the distributions of mass and nitrogen abundance are notably different between RC and RGB stars. These findings indicate that there is still unknown process that significantly affects surface chemical composition in low-mass stellar evolution., Comment: 27 pages, 10 figures, 3 tables

The recent discovery of an Earth-sized planet (TOI-700 d) in the habitable zone of an early-type M-dwarf by the Transiting Exoplanet Survey Satellite constitutes an important advance. In this Letter, we assess the feasibility of this planet to retain an atmosphere -- one of the chief ingredients for surface habitability -- over long timescales by employing state-of-the-art magnetohydrodynamic models to simulate the stellar wind and the associated rates of atmospheric escape. We take two major factors into consideration, namely, the planetary atmospheric composition and magnetic field. In all cases, we determine that the atmospheric ion escape rates are potentially a few orders of magnitude higher than the inner Solar system planets, but TOI-700 d is nevertheless capable of retaining a $1$ bar atmosphere over gigayear timescales for certain regions of the parameter space. The simulations show that the unmagnetized TOI-700 d with a 1 bar Earth-like atmosphere could be stripped away rather quickly ($<$ 1 gigayear), while the unmagnetized TOI-700 d with a 1 bar CO$_2$-dominated atmosphere could persist for many billions of years; we find that the magnetized Earth-like case falls in between these two scenarios. We also discuss the prospects for detecting radio emission of the planet (thereby constraining its magnetic field) and discerning the presence of an atmosphere., Comment: ApJ Letters, in press, 8 pages, 3 figures, and 1 table

Solar coronal dimmings have been observed extensively in the past two decades and are believed to have close association with coronal mass ejections (CMEs). Recent study found that coronal dimming is the only signature that could differentiate powerful ares that have CMEs from those that do not. Therefore, dimming might be one of the best candidates to observe the stellar CMEs on distant Sun-like stars. In this study, we investigate the possibility of using coronal dimming as a proxy to diagnose stellar CMEs. By simulating a realistic solar CME event and corresponding coronal dimming using a global magnetohydrodynamics model (AWSoM: Alfven-wave Solar Model), we first demonstrate the capability of the model to reproduce solar observations. We then extend the model for simulating stellar CMEs by modifying the input magnetic flux density as well as the initial magnetic energy of the CME flux rope. Our result suggests that with improved instrument sensitivity, it is possible to detect the coronal dimming signals induced by the stellar CMEs., Comment: 8 pages, 4 figures, to appear in the Proceedings of IAU Symposium No. 354 - Solar and Stellar Magnetic Fields: Origins and Manifestations

A deep learning network, Long-Short Term Memory (LSTM) network, is used in this work to predict whether the maximum flare class an active region (AR) will produce in the next 24 hours is class $\Gamma$. We considered $\Gamma$ are $\ge M$, $\ge C$ and any flare class. The essence of using LSTM, which is a recurrent neural network, is its capability to capture temporal information of the data samples. The input features are time sequences of 20 magnetic parameters from SHARPs - Space-weather HMI Active Region Patches. We analyzed active regions from June 2010 to Dec 2018, using the Geostationary Operational Environmental Satellite (GOES) X-ray flare catalogs and label the data samples with identified ARs in the GOES X-ray flare catalogs. Our results (i) shows consistent skill scores with recently published results using LSTMs and better than the previous work using single time input (eg. DeFN) (ii) The skill scores from the model show essential differences when different years of data was chosen for training and testing.

Understanding and monitoring the complex and dynamic processes of the Sun is important for a number of human activities on Earth and in space. For this reason, NASA's Solar Dynamics Observatory (SDO) has been continuously monitoring the multi-layered Sun's atmosphere in high-resolution since its launch in 2010, generating terabytes of observational data every day. The synergy between machine learning and this enormous amount of data has the potential, still largely unexploited, to advance our understanding of the Sun and extend the capabilities of heliophysics missions. In the present work, we show that deep learning applied to SDO data can be successfully used to create a high-fidelity virtual telescope that generates synthetic observations of the solar corona by image translation. Towards this end we developed a deep neural network, structured as an encoder-decoder with skip connections (U-Net), that reconstructs the Sun's image of one instrument channel given temporally aligned images in three other channels. The approach we present has the potential to reduce the telemetry needs of SDO, enhance the capabilities of missions that have less observing channels, and transform the concept development of future missions., Comment: 5 pages, 6 figures, Accepted at the NeurIPS 2019 Workshop ML4PS

As a part of NASA's Heliophysics System Observatory (HSO) fleet of satellites,the Solar Dynamics Observatory (SDO) has continuously monitored the Sun since2010. Ultraviolet (UV) and Extreme UV (EUV) instruments in orbit, such asSDO's Atmospheric Imaging Assembly (AIA) instrument, suffer time-dependent degradation which reduces instrument sensitivity. Accurate calibration for (E)UV instruments currently depends on periodic sounding rockets, which are infrequent and not practical for heliophysics missions in deep space. In the present work, we develop a Convolutional Neural Network (CNN) that auto-calibrates SDO/AIA channels and corrects sensitivity degradation by exploiting spatial patterns in multi-wavelength observations to arrive at a self-calibration of (E)UV imaging instruments. Our results remove a major impediment to developing future HSOmissions of the same scientific caliber as SDO but in deep space, able to observe the Sun from more vantage points than just SDO's current geosynchronous orbit.This approach can be adopted to perform autocalibration of other imaging systems exhibiting similar forms of degradation, Comment: 6 pages, 3 figures, Accepted at NeurIPS 2019 Workshop ML4PS