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Shocks driven by coronal mass ejections (CMEs) are primary drivers of gradual solar energetic particle (SEP) events, posing significant risks to space technology and astronauts. Concurrently, particles accelerated at these shocks may also propagate back to the Sun, potentially generating gamma-ray emissions through pion decay. We incorporated advanced modeling and multi-messenger observations to explore the role of CME-driven shocks in gamma-ray emissions and SEPs. Motivated by Fermi-LAT long-duration solar flares, we used the AWSoM MHD model to investigate the connection between the shocks and the properties of observed gamma-ray emissions. By coupling the AWSoM with iPATH model, we evaluate the impact of shock evolution complexity near the Sun on SEP intensity and spectra. Our result points to the importance of accurate background coronal and solar wind modeling, as well as detailed observations of CME source regions, in advancing our understanding of CME-driven shocks and the dynamics of associated energetic particles., Comment: 11 pages, 6 figures, to appear in the Proceedings of IAU Symposium No. 388 - Solar and Stellar Coronal Mass Ejections

We give a gentle introduction to solar imaging data, focusing on the challenges and opportunities of data-driven approaches for solar eruptions. The various solar phenomenon prediction problems that might benefit from statistical methods are presented. Available data products and software are described. State-of-the-art solar eruption forecasting models with data-driven approaches are summarized and discussed. Based on the characteristics of the datasets and state-of-the-art approaches, we point out several promising directions to explore from statistical modeling and computational perspectives.

The exponential growth of scientific literature requires effective management and extraction of valuable insights. While existing scientific search engines excel at delivering search results based on relational databases, they often neglect the analysis of collaborations between scientific entities and the evolution of ideas, as well as the in-depth analysis of content within scientific publications. The representation of heterogeneous graphs and the effective measurement, analysis, and mining of such graphs pose significant challenges. To address these challenges, we present AceMap, an academic system designed for knowledge discovery through academic graph. We present advanced database construction techniques to build the comprehensive AceMap database with large-scale academic entities that contain rich visual, textual, and numerical information. AceMap also employs innovative visualization, quantification, and analysis methods to explore associations and logical relationships among academic entities. AceMap introduces large-scale academic network visualization techniques centered on nebular graphs, providing a comprehensive view of academic networks from multiple perspectives. In addition, AceMap proposes a unified metric based on structural entropy to quantitatively measure the knowledge content of different academic entities. Moreover, AceMap provides advanced analysis capabilities, including tracing the evolution of academic ideas through citation relationships and concept co-occurrence, and generating concise summaries informed by this evolutionary process. In addition, AceMap uses machine reading methods to generate potential new ideas at the intersection of different fields. Exploring the integration of large language models and knowledge graphs is a promising direction for future research in idea evolution. Please visit \url{https://www.acemap.info} for further exploration., Comment: Technical Report for AceMap (https://www.acemap.info)

Ultra-cool dwarf stars are abundant, long-lived, and uniquely suited to enable the atmospheric study of transiting terrestrial companions with JWST. Amongst them, the most prominent is the M8.5V star TRAPPIST-1 and its seven planets. While JWST Cycle 1 observations have started to yield preliminary insights into the planets, they have also revealed that their atmospheric exploration requires a better understanding of their host star. Here, we propose a roadmap to characterize the TRAPPIST-1 system -- and others like it -- in an efficient and robust manner. We notably recommend that -- although more challenging to schedule -- multi-transit windows be prioritized to mitigate the effects of stellar activity and gather up to twice more transits per JWST hour spent. We conclude that, for such systems, planets cannot be studied in isolation by small programs, but rather need large-scale, jointly space- and ground-based initiatives to fully exploit the capabilities of JWST for the exploration of terrestrial planets.

Coronal mass ejections (CMEs) are large eruptions from the Sun that propagate through the heliosphere after launch. Observational studies of these transient phenomena are usually based on 2D images of the Sun, corona, and heliosphere (remote-sensing data), as well as magnetic field, plasma, and particle samples along a 1D spacecraft trajectory (in-situ data). Given the large scales involved and the 3D nature of CMEs, such measurements are generally insufficient to build a comprehensive picture, especially in terms of local variations and overall geometry of the whole structure. This White Paper aims to address this issue by identifying the data sets and observational priorities that are needed to effectively advance our current understanding of the structure and evolution of CMEs, in both the remote-sensing and in-situ regimes. It also provides an outlook of possible missions and instruments that may yield significant improvements into the subject., Comment: White Paper submitted to the Heliophysics 2024-2033 Decadal Survey, 9 pages, 4 figures

Multi-channel imaging data is a prevalent data format in scientific fields such as astronomy and biology. The structured information and the high dimensionality of these 3-D tensor data makes the analysis an intriguing but challenging topic for statisticians and practitioners. The low-rank scalar-on-tensor regression model, in particular, has received widespread attention and has been re-formulated as a tensor Gaussian Process (Tensor-GP) model with multi-linear kernel in Yu et al.(2018). In this paper, we extend the Tensor-GP model by introducing an integrative dimensionality reduction technique, called tensor contraction, with a Tensor-GP for a scalar-on-tensor regression task with multi-channel imaging data. This is motivated by the solar flare forecasting problem with high dimensional multi-channel imaging data. We first estimate a latent, reduced-size tensor for each data tensor and then apply a multi-linear Tensor-GP on the latent tensor data for prediction. We introduce an anisotropic total-variation regularization when conducting the tensor contraction to obtain a sparse and smooth latent tensor. We then propose an alternating proximal gradient descent algorithm for estimation. We validate our approach via extensive simulation studies and applying it to the solar flare forecasting problem., Comment: 23 pages, 12 figures, 3 tables

Acoustic signal acts as an essential input to many systems. However, the pure acoustic signal is very difficult to extract, especially in noisy environments. Existing beamforming systems are able to extract the signal transmitted from certain directions. However, since microphones are centrally deployed, these systems have limited coverage and low spatial resolution. We overcome the above limitations and present ChordMics, a distributed beamforming system. By leveraging the spatial diversity of the distributed microphones, ChordMics is able to extract the acoustic signal from arbitrary points. To realize such a system, we further address the fundamental challenge in distributed beamforming: aligning the signals captured by distributed and unsynchronized microphones. We implement ChordMics and evaluate its performance under both LOS and NLOS scenarios. The evaluation results tell that ChordMics can deliver higher SINR than the centralized microphone array. The average performance gain is up to 15dB.

Gaze tracking is a useful human-to-computer interface, which plays an increasingly important role in a range of mobile applications. Gaze calibration is an indispensable component of gaze tracking, which transforms the eye coordinates to the screen coordinates. The existing approaches of gaze tracking either have limited accuracy or require the user's cooperation in calibration and in turn hurt the quality of experience. We in this paper propose vGaze, continuous gaze tracking with implicit saliency-aware calibration on mobile devices. The design of vGaze stems from our insight on the temporal and spatial dependent relation between the visual saliency and the user's gaze. vGaze is implemented as a light-weight software that identifies video frames with "useful" saliency information, sensing the user's head movement, performs opportunistic calibration using only those "useful" frames, and leverages historical information for accelerating saliency detection. We implement vGaze on a commercial mobile device and evaluate its performance in various scenarios. The results show that vGaze can work at real time with video playback applications. The average error of gaze tracking is 1.51 cm (2.884 degree) which decreases to 0.99 cm (1.891 degree) with historical information and 0.57 cm (1.089 degree) with an indicator.

Measurements of the stellar coronal magnetic field are of great importance in understanding the stellar magnetic activity, yet the measurements have been extremely difficult. Recent studies proposed a new method of magnetic field measurements based on the magnetic-field-induced-transition (MIT) of the Fe~{\sc{x}} ion. Here we construct a series of stellar coronal magnetohydrodynamics (MHD) models and synthesize several Fe~{\sc{x}} emission lines at extreme-ultraviolet wavelengths, and then diagnose the magnetic field strength at the bases of the coronae using the MIT technique. Our results show that the technique can be applied to some stars with magnetic fields more than three times higher than that of the Sun at solar maximum. Furthermore, we investigate the uncertainty of the derived magnetic field strength caused by photon counting error and find that a signal-noise ratio of $\sim$50 for the Fe~{\sc{x}} 175 {\AA}~line is required to achieve effective measurements of the stellar coronal magnetic field., Comment: 12 pages, 7 figures

The Solar Dynamics Observatory (SDO), a NASA multi-spectral decade-long mission that has been daily producing terabytes of observational data from the Sun, has been recently used as a use-case to demonstrate the potential of machine learning methodologies and to pave the way for future deep-space mission planning. In particular, the idea of using image-to-image translation to virtually produce extreme ultra-violet channels has been proposed in several recent studies, as a way to both enhance missions with less available channels and to alleviate the challenges due to the low downlink rate in deep space. This paper investigates the potential and the limitations of such a deep learning approach by focusing on the permutation of four channels and an encoder--decoder based architecture, with particular attention to how morphological traits and brightness of the solar surface affect the neural network predictions. In this work we want to answer the question: can synthetic images of the solar corona produced via image-to-image translation be used for scientific studies of the Sun? The analysis highlights that the neural network produces high-quality images over three orders of magnitude in count rate (pixel intensity) and can generally reproduce the covariance across channels within a 1% error. However the model performance drastically diminishes in correspondence of extremely high energetic events like flares, and we argue that the reason is related to the rareness of such events posing a challenge to model training., Comment: 16 pages, 8 figures. To be published on ApJ (submitted on Feb 21st, accepted on July 28th)

Coronal mass ejections (CMEs) are a prominent contributor to solar system space weather and might have impacted the Sun's early angular momentum evolution. A signal diagnostic of CMEs on the Sun is coronal dimming: a drop in coronal emission, tied to the mass of the CME, that is the direct result of removing emitting plasma from the corona. We present the results of a coronal dimming analysis of Fe XII 1349 A and Fe XXI 1354 A emission from $\epsilon$ Eridani ($\epsilon$ Eri), a young K2 dwarf, with archival far-ultraviolet observations by the Hubble Space Telescope's Cosmic Origins Spectrograph. Following a flare in February 2015, $\epsilon$ Eri's Fe XXI emission declined by $81\pm5$%. Although enticing, a scant 3.8 min of preflare observations allows for the possibility that the Fe XXI decline was the decay of an earlier, unseen flare. Dimming nondetections following each of three prominent flares constrain the possible mass of ejected Fe XII-emitting (1 MK) plasma to less than a few $\times10^{15}$ g. This implies that CMEs ejecting this much or more 1 MK plasma occur less than a few times per day on $\epsilon$ Eri. On the Sun, $10^{15}$ g CMEs occur once every few days. For $\epsilon$ Eri, the mass loss rate due to CME-ejected 1 MK plasma could be $<0.6$ $\dot{M}_\odot$, well below the star's estimated 30 $\dot{M}_\odot$ mass loss rate (wind + CMEs). The order-of-magnitude formalism we developed for these mass estimates can be broadly applied to coronal dimming observations of any star., Comment: 27 pages, 22 figures, accepted to ApJ