arXiv subjects

Meng Jin

Publications and source records attributed to Meng Jin.

At least 55 records · Page 3Linked to original sources

arXiv2021

Measurements of the magnetic field strengths at the bases of stellar coronae using the magnetic-field-induced transition theory

Measurements of the magnetic field in the stellar coronae are extremely difficult. Recently, it was proposed that the magnetic-field-induced transition (MIT) of the Fe X 257 Å line can be used to measure the coronal magnetic field of the Sun. We performed forward modeling with a series of global stellar magnetohydrodynamics models to investigate the possibility of extending this method to other late-type stars. We first synthesized the emissions of several Fe X lines for each stellar model, then calculated the magnetic field strengths using the intensity ratios of Fe X 257 Å to several other Fe X lines based on the MIT theory. Finally, we compared the derived field strengths with those in the models, and concluded that this method can be used to measure at least the magnetic field strengths at the coronal bases of stars with a mean surface magnetic flux density about one order of magnitude higher than that of the Sun. Our investigation suggests the need of an extreme ultraviolet spectrometer to perform routine measurements of the stellar coronal magnetic field.

astro-ph.SR↗

arXiv2021

Probing the Physics of the Solar Atmosphere with the Multi-slit Solar Explorer (MUSE): II. Flares and Eruptions

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.

astro-ph.SR↗

arXiv2021

One Year in the Life of Young Suns: Data Constrained Corona-Wind Model of kappa1 Ceti

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.

astro-ph.SR↗

arXiv2021

Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly using Machine Learning

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.

astro-ph.SR↗

arXiv2020

Threaded-Field-Lines Model for the Low Solar Corona Powered by the Alfven Wave Turbulence

We present an updated global model of the solar corona, including the transition region. We simulate the realistic tree-dimensional (3D) magnetic field using the data from the photospheric magnetic field measurements and assume the magnetohydrodynamic (MHD) Alfvén wave turbulence and its non-linear dissipation to be the only source for heating the coronal plasma and driving the solar wind. In closed field regions the dissipation efficiency in a balanced turbulence is enhanced. In the coronal holes we account for a reflection of the outward propagating waves, which is accompanied by generation of weaker counter-propagating waves. The non-linear cascade rate degrades in strongly imbalanced turbulence, thus resulting in colder coronal holes. The distinctive feature of the presented model is the description of the low corona as almost-steady-state low-beta plasma motion and heat flux transfer along the magnetic field lines. We trace the magnetic field lines through each grid point of the lower boundary of the global corona model, chosen at some heliocentric distance, $R=R_{b}\sim1.1\ R_\odot$ well above the transition region. One can readily solve the plasma parameters along the magnetic field line from 1D equations for the plasma motion and heat transport together with the Alfvén wave propagation, which adequately describe physics within the heliocentric distances range, $R_{\odot}<R<R_{b}$, in the low solar corona. By interfacing this threaded-field-lines model with the full MHD global corona model at $r=R_{b}$, we find the global solution and achieve a faster-than-real-time performance of the model on $\sim200$ cores.

astro-ph.SR↗

arXiv2020

Atmospheric Escape From TOI-700 d: Venus versus Earth Analogs

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.

astro-ph.EP↗

arXiv2020

Coronal Dimming as a Proxy for Stellar Coronal Mass Ejections

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.

astro-ph.SR↗

arXiv2020

Predicting solar flares with machine learning: investigating solar cycle dependence

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 $Γ$. We considered $Γ$ 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.

astro-ph.SR↗

arXiv2019

Using U-Nets to Create High-Fidelity Virtual Observations of the Solar Corona

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.

astro-ph.SR↗

arXiv2019

Auto-Calibration of Remote Sensing Solar Telescopes with Deep Learning

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

astro-ph.SR↗

arXiv2019

Ensemble Neural Networks (ENN): A gradient-free stochastic method

In this study, an efficient stochastic gradient-free method, the ensemble neural networks (ENN), is developed. In the ENN, the optimization process relies on covariance matrices rather than derivatives. The covariance matrices are calculated by the ensemble randomized maximum likelihood algorithm (EnRML), which is an inverse modeling method. The ENN is able to simultaneously provide estimations and perform uncertainty quantification since it is built under the Bayesian framework. The ENN is also robust to small training data size because the ensemble of stochastic realizations essentially enlarges the training dataset. This constitutes a desirable characteristic, especially for real-world engineering applications. In addition, the ENN does not require the calculation of gradients, which enables the use of complicated neuron models and loss functions in neural networks. We experimentally demonstrate benefits of the proposed model, in particular showing that the ENN performs much better than the traditional Bayesian neural networks (BNN). The EnRML in ENN is a substitution of gradient-based optimization algorithms, which means that it can be directly combined with the feed-forward process in other existing (deep) neural networks, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), broadening future applications of the ENN.

stat.ML↗

arXiv2019

EUV influences on exoplanet atmospheric stability and evolution

The planetary effective surface temperature alone is insufficient to characterize exoplanet atmospheres and their stability or evolution. Considering the star-planet system as a whole is necessary, and a critical component of the system is the photoionizing stellar extreme ultraviolet emission (EUV; 100-912 Å). EUV photons drive atmospheric mass loss through thermal and nonthermal processes, and an accurate accounting of the EUV energy deposition in a planet's energy budget is essential, especially for terrestrial habitable zone planets and close-in gaseous planets. Direct EUV observations of exoplanet host stars would require a new, dedicated observatory. Archival observations from the $\textit{EUVE}$ satellite, models, and theory alone are insufficient to accurately characterize EUV spectra of the majority of exoplanet host stars, especially for low-mass stars.

astro-ph.EP↗

arXiv2019

EUV observations of cool dwarf stars

The EUV (100-912 Å) is a spectral region notoriously difficult to observe due to attenuation by neutral hydrogen gas in the interstellar medium. Despite this, hundreds to thousands of nearby stars of different spectral types and magnetic activity levels are accessible in the EUV range. The EUV probes interesting and complicated regions in the stellar atmosphere like the lower corona and transition region that are inaccessible from other spectral regions. In this white paper we describe how direct EUV observations, which require a dedicated grazing-incidence observatory, cannot yet be accurately substituted with models and theory. Exploring EUV emission from cool dwarf stars in the time domain can make a major contribution to understanding stellar outer atmospheres and magnetism, and offers the clearest path toward detecting coronal mass ejections on stars other than the Sun.

astro-ph.SR↗

arXiv2019

A Machine Learning Dataset Prepared From the NASA Solar Dynamics Observatory Mission

In this paper we present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We illustrate the use of this dataset with two example applications: forecasting future EVE irradiance from present EVE irradiance and translating HMI observations into AIA observations. For each application we provide metrics and baselines for future model comparison. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a direct result of the 2018 NASA Frontier Development Laboratory Program. Please see the appendix for access to the dataset.

astro-ph.SR↗

arXiv2018

Probing the Puzzle of Behind-the-Limb $γ$-ray Flares: Data-driven Simulations of Magnetic Connectivity and CME-driven Shock Evolution

Recent detections of high-energy $γ$-rays from behind-the-limb (BTL) solar flares by the \emph{Fermi $γ$-ray Space Telescope} pose a puzzle and challenge on the particle acceleration and transport mechanisms. In such events, the $γ$-ray emission region is located away from the BTL flare site by up to tens of degrees in heliogrpahic longitude. It is thus hypothesized that particles are accelerated at the shock driven by the coronal mass ejection (CME) and then travel from the shock downstream back to the front side of the Sun to produce the observed $γ$-rays. To test this scenario, we performed data-driven, global magnetohydrodynamics simulations of the CME associated with a well-observed BTL flare on 2014 September 1. We found that part of the CME-driven shock develops magnetic connectivity with the $γ$-ray emission region, facilitating transport of particles back to the Sun. Moreover, the observed increase in $γ$-ray flux is temporally correlated with (1) the increase of the shock compression ratio and (2) the presence of a quasi-perpendicular shock over the area that is magnetically connected to the $γ$-ray emitting region, both conditions favoring the diffusive shock acceleration (DSA) of particles. These results support the above hypothesis and can help resolve another puzzle, i.e., long-duration (up to 20 hours) $γ$-rays flares. We suggest that, in addition to DSA, stochastic acceleration by plasma turbulence may also play a role, especially in the shock downstream region and during the early stage when the shock Alfvén Mach number is small.

astro-ph.SR↗

arXiv2018

A Truly Global Extreme Ultraviolet Wave from the SOL2017-09-10 X8.2+ Solar Flare-Coronal Mass Ejection

We report SDO/AIA observations of an extraordinary global extreme ultraviolet (EUV) wave triggered by the X8.2+ flare-CME eruption on 2017 September 10. This was one of the best EUV waves ever observed with modern instruments, yet it was likely the last one of such magnitudes of Solar Cycle 24 as the Sun heads toward the minimum. Its remarkable characteristics include the following. (1) The wave was observed, for the first time, to traverse the full-Sun corona over the entire visible solar disk and off-limb circumference, manifesting a truly global nature, owing to its exceptionally large amplitude, e.g., with EUV enhancements by up to 300% at 1.1 Rsun from the eruption. (2) This leads to strong transmissions (in addition to commonly observed reflections) in and out of both polar coronal holes, which are usually devoid of EUV waves. It has elevated wave speeds >2000 km/s within them, consistent with the expected higher fast-mode magnetosonic wave speeds. The coronal holes essentially serve as new "radiation centers" for the waves being refracted out of them, which then travel toward the equator and collide head-on, causing additional EUV enhancements. (3) The wave produces significant compressional heating to local plasma upon its impact, indicated by long-lasting EUV intensity changes and differential emission measure increases at higher temperatures (e.g., log T=6.2) accompanied by decreases at lower temperatures (e.g., log T=6.0). These characteristics signify the potential of such EUV waves for novel magnetic and thermal diagnostics of the solar corona {\it on global scales}.

astro-ph.SR↗

arXiv2017

Atmospheric escape from the TRAPPIST-1 planets and implications for habitability

The presence of an atmosphere over sufficiently long timescales is widely perceived as one of the most prominent criteria associated with planetary surface habitability. We address the crucial question as to whether the seven Earth-sized planets transiting the recently discovered ultracool dwarf star TRAPPIST-1 are capable of retaining their atmospheres. To this effect, we carry out numerical simulations to characterize the stellar wind of TRAPPIST-1 and the atmospheric ion escape rates for all the seven planets. We also estimate the escape rates analytically and demonstrate that they are in good agreement with the numerical results. We conclude that the outer planets of the TRAPPIST-1 system are capable of retaining their atmospheres over billion-year timescales. The consequences arising from our results are also explored in the context of abiogenesis, biodiversity, and searches for future exoplanets. In light of the many unknowns and assumptions involved, we recommend that these conclusions must be interpreted with due caution.

astro-ph.EP↗

arXiv2017

Eruptive Event Generator Based on the Gibson-Low Magnetic Configuration

Coronal Mass Ejections (CMEs), a kind of energetic solar eruptions, are an integral subject of space weather research. Numerical magnetohydrodynamic (MHD) modeling, which requires powerful computational resources, is one of the primary means of studying the phenomenon. With increasing accessibility of such resources, grows the demand for user-friendly tools that would facilitate the process of simulating CMEs for scientific and operational purposes. The Eruptive Event Generator based on Gibson-Low flux rope (EEGGL), a new publicly available computational model presented in this paper, is an effort to meet this demand. EEGGL allows one to compute the parameters of a model flux rope driving a CME via an intuitive graphical user interface (GUI). We provide a brief overview of the physical principles behind EEGGL and its functionality. Ways towards future improvements of the tool are outlined.

physics.space-ph↗