arXiv subjects

Xinsheng Zhang

Publications and source records attributed to Xinsheng Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Observations of a Solar Jet Triggered by Reconnection between Super-penumbral Fibrils and a Mini-filament

Coronal jets are highly dynamic phenomena in the solar atmosphere, yet their driving mechanisms remain an active topic of investigation. In this paper, we report a coronal jet triggered by the interaction between super-penumbral fibrils and a mini-filament, based on coordinated observations from the New Vacuum Solar Telescope (NVST), the Chinese H$α$ Solar Explorer (CHASE), and the Solar Dynamics Observatory (SDO). The fibrils were anchored between the negative-polarity region of a sunspot and an emerging positive-polarity region associated with a moving magnetic feature (MMF). As the positive polarity migrated outward, the fibrils elongated and interacted with the mini-filament, one of whose footpoints was rooted in pre-existing negative-polarity fields. Intense brightenings at the interaction site, together with changes in the connectivity of the mini-filament footpoint from the pre-existing negative polarity to the sunspot, indicate the occurrence of magnetic reconnection. The event produced a narrow hot jet accompanied by a broader cool component. The cool plasma exhibited a clockwise rotation, providing evidence for the transfer of magnetic twist during reconnection. Persistent magnetic flux cancellation was observed before and during the jet eruption. These observations demonstrate that small-scale magnetic structures, such as MMFs, can significantly influence mini-filament eruptions and highlight the important role of flux cancellation in triggering coronal jet activity.

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arXiv2026

Observational Evidence of Solar Spicules Associated with Microfilament Eruptions Using DKIST

The formation mechanism of spicules is fundamentally important for understanding mass and energy transport from the chromosphere into the corona. Recent studies suggested that spicules may be powered by microfilament eruptions. However, direct observational evidence remains limited due to insufficient spatial resolution. Using high-resolution H$α$ broadband observations from the Visible Broadband Imager (VBI) onboard the Daniel K. Inouye Solar Telescope (DKIST), we identify 30 spicule events triggered by microfilament eruptions in a quiet Sun region near the solar disk center on 2023 August 29. The detected microfilaments have an average length of $0.93\pm0.46$ Mm and a minimum length of 0.17 Mm, substantially smaller than previously reported minifilaments. We identify two distinct morphological classes of ejecta: individual spicules associated with smaller microfilaments, and enhanced spicular activities associated with larger microfilaments. Moreover, some events exhibit apparent twisting motions. All these high-resolution observations provide compelling evidence that spicules can be triggered by microfilament eruptions.

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arXiv2026

Quasiperiodic Slipping Motion of Flare Ribbon Fine Structures Anchored in a Sunspot Light Bridge

We used high-resolution observations from the New Vacuum Solar Telescope and the Solar Dynamics Observatory to carry out a detailed multiwavelength analysis of the fine structures in the flare ribbon of a C3.9-class flare on 22 April 2021. A segment of the flare ribbon was rooted in a sunspot light bridge and exhibited discrete substructures, which we term "burrs", with equivalent diameters of 233-895 km and inter-core separations of 1129-1739 km. These structures are characterized by discrete redshifted cores accompanied by "tails" with lengths of 700-1370 km and widths of 310-600 km that show faint blueshifts. The burrs display systematic slipping motions along the ribbon, with apparent velocities decreasing from about 40 to 21 km/s, and show a distinct quasi-periodicity of about 6 minutes in H-alpha and EUV passbands. Differential emission measure analysis indicates that the emitting plasma is multi-thermal and dominated by temperatures of 1-2 MK. The observed morphology and kinematics are consistent with impulsive energy deposition by precipitating plasmoids, or oblique flux ropes, produced by tearing-mode fragmentation in the coronal current sheet. The close spatiotemporal association between the tails and blueshifts supports the interpretation that these features are related to untwisting magnetic flux ropes. The approximately 6-minute periodicity further suggests that the reconnection process may be modulated by photospheric p-mode oscillations coupled with tearing-mode instability. These results provide observational evidence that light-bridge-anchored fine structures can act as elementary units of flare energy release.

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arXiv2025

Observational study of chromospheric jets in and around a sunspot observed by NVST and SDO

To better understand the characteristics, driving mechanisms, and potential heating contributions of chromospheric jets, we analyze two contrasting types: one originating from within the sunspot penumbra (inside jets), and the other originating from outside the penumbra (outside jets). Statistical analysis of 100 jets (50 inside jets and 50 outside jets) reveals that inside jets have a projected velocity range of 4--14~km\,s$^{-1}$, a length range of 1--4~Mm, a width range of 0.2--0.6~Mm, and a lifetime range of 135--450~s, with mean values of 7.90~km\,s$^{-1}$, 2.61~Mm, 0.41~Mm, and 260~s, respectively. About 52\% of inside jets are associated with brightenings in H$α$ blue wing images, and some show high-temperature signatures, suggesting a connection with localized energy release. In contrast, outside jets have higher velocities (8--50~km\,s$^{-1}$, average 19.04~km\,s$^{-1}$), greater lengths (average 6.26~Mm, up to 27.27~Mm), slightly larger widths (average 0.46~Mm), and longer lifetimes (135--630~s, average 327~s). They typically originate from regions of opposite magnetic polarities and are associated with magnetic flux emergence and EUV brightenings. Some outside jets correspond to coronal jets with inverted Y-shaped structures and temperatures exceeding one million Kelvin. Our results suggest that both jet types are driven by magnetic reconnection occurring in distinct magnetic field configurations and contribute to chromospheric and coronal heating.

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arXiv2025

Properties of Chromospheric Fibrils Around a Quiescent Filament

Fibrils are dynamic plasma structures in the solar chromosphere. Studying these structures is critical for understanding solar atmospheric heating and mass transportation. The purpose of this study is to obtain the characteristics of fibrils surrounding the filament. By employing high-resolution H-alpha data obtained from the New Vacuum Solar Telescope (NVST), we undertake a detailed analysis of the properties of 63 fibrils situated in the vicinity of the filament. Comparing the fibrils on both sides of the filament demonstrates that these fibrils have similar physical properties except for their orientation. The properties of fibrils are statistically measured, including lifetimes of 150-650 s, widths of 320-850 km, maximum lengths of 3-8.5 Mm, projection velocities of 7-29 km/s, and decelerations of 45-474 m/s2. The dominant oscillation period of fibrils is predominantly concentrated in the range of 4.8-6.6 minutes (2.5-3.5 mHz). Transverse oscillations are identified in a subset of fibrils, with periodicities of 269-289 s and phase speeds of 13.7-25.8 km/s, indicating the presence of kink-mode magnetohydrodynamic (MHD) waves.

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arXiv2025

Few-Round Distributed Principal Component Analysis: Closing the Statistical Efficiency Gap by Consensus

Distributed algorithms and theories are called for in this era of big data. Under weaker local signal-to-noise ratios, we improve upon the celebrated one-round distributed principal component analysis (PCA) algorithm designed in the spirit of divide-and-conquer, by introducing a few additional communication rounds of consensus. The proposed shifted subspace iteration algorithm is able to close the local phase transition gap, reduce the asymptotic variance, and also alleviate the potential bias. Our estimation procedure is easy to implement and tuning-free. The resulting estimator is shown to be statistically efficient after an acceptable number of iterations. We also discuss extensions to distributed elliptical PCA for heavy-tailed data. Empirical experiments on synthetic and benchmark datasets demonstrate our method's statistical advantage over the divide-and-conquer approach.

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arXiv2025

The Observations of Magnetic Reconnection During the Interaction Process of Two Active Region Filaments

We investigate the interaction between two filaments (F1 and F2) and their subsequent magnetic reconnection in active region (AR) NOAA 13296 and AR NOAA 13293 on May 9, 2023, utilizing high spatial and temporal resolution and multi-wavelength observational data from the Solar Dynamics Observatory, the New Vacuum Solar Telescope, and the Chinese Hα Solar Explorer. The movement of F1 from the southeast toward the northwest, driven by the motion of the positive magnetic polarity (P1), leads to a collision and reconnection with F2. This reconnection exchanges their footpoints, resulting in the formation of two new filaments (F3 and F4) consistent with "slingshot" type filament interaction. During the interaction, the current sheet moving due to the motion of F1 and the reconnection outflows moving along F3 and F4 were both observed. The current sheet is rarely observed in the slingshot type filament interaction, measuring approximately 2.17 Mm in length and 0.84 Mm in width. After the interaction, the F1 disappears whereas a portion of F2 remains, indicating that the interaction involves partial slingshot reconnection, due to the unequal magnetic flux between the filaments. The residual part of F2 will undergo another magnetic reconnection in the same interaction region with the magnetic loops connecting polarities N1 and P1. The material generated by the reconnection is continuously injected into F4, leading to its final morphology. The findings enhance our understanding of slingshot-type filament interactions, indicating that partial slingshot reconnections between filaments may be more common than full slingshot events.

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arXiv2025

Recurrent Jetlets Associated with the Disappearance of a Satellite Spot

Recurrent small-scale eruptions are fascinating phenomena in the solar atmosphere. However, their underlying physical mechanisms remain unclear. On 2021 May 23, five recurrent jetlets (J1-J5) were observed continuously ejecting from a satellite spot located at the north edge of AR 12824. Using high-resolution, multi-wavelength data from NVST, SDO, and IRIS, we investigate the physical characteristics of these jetlets and their relationship with the satellite spot. The widths of these jetlets range from 1300 to 2900 km, their lifetimes range span 3 to 10 minutes, and their projection speeds vary from 152.8 to 406.0 km s$^{-1}$. During the eruptions, the satellite spot moved northwest at a low speed of 376 $\pm$ 12 m s$^{-1}$. Its area gradually decreased due to magnetic cancellation with surrounding positive magnetic field, resulting in an average cancellation rate of 1.3$\times$10$^{18}$ Mx hr$^{-1}$. Dark lanes that separated from the satellite spot and small pores were observed to move toward nearby these features or dark lanes with opposite polarities, eventually disappearing during the magnetic cancellation process. J4 was driven by an eruption of a micro-filament. Spectral observations revealed a redshift on the right side of J4 and a blueshift on the left side of its base, suggesting a counterclockwise rotation. The horizontal magnetic field of the satellite spot consistently exhibited a vortex structure throughout its evolution until it vanished. The nonlinear force-free field extrapolation confirms that the satellite spot serves as one footpoint of a mini-flux rope. These observations reveal that these jetlets might result from three-dimensional null-point magnetic reconnection, initiated by the continuous eruption of a mini-flux-rope or multiple mini-flux-ropes, driven by sustained magnetic cancellation.

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arXiv2024

Simultaneous Estimation and Dataset Selection for Transfer Learning in High Dimensions by a Non-convex Penalty

In this paper, we propose to estimate model parameters and identify informative source datasets simultaneously for high-dimensional transfer learning problems with the aid of a non-convex penalty, in contrast to the separate useful dataset selection and transfer learning procedures in the existing literature. To numerically solve the non-convex problem with respect to two specific statistical models, namely the sparse linear regression and the generalized low-rank trace regression models, we adopt the difference of convex (DC) programming with the alternating direction method of multipliers (ADMM) procedures. We theoretically justify the proposed algorithm from both statistical and computational perspectives. Extensive numerical results are reported alongside to validate the theoretical assertions. An \texttt{R} package \texttt{MHDTL} is developed to implement the proposed methods.

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arXiv2024

Origin of the Chromospheric Umbral Waves in Sunspots

Oscillations are ubiquitous in sunspots and the associated higher atmospheres. However, it is still unclear whether these oscillations are driven by the external acoustic waves (p-modes) or generated by the internal magnetoconvection. To obtain clues about the driving source of umbral waves in sunspots, we analyzed the spiral wave patterns (SWPs) in two sunspots registered by IRIS MgII 2796 Å slit-jaw images. By tracking the motion of the SWPs, we find for the first time that two one-armed SWPs coexist in the umbra, and they can rotate either in the same or opposite directions. Furthermore, by analyzing the spatial distribution of the oscillation centers of the one-armed SWPs within the umbra (the oscillation center is defined as the location where the SWP first appears), we find that the chromospheric umbral waves repeatedly originate from the regions with high oscillation power and most of the umbral waves occur in the dark nuclei and strong magnetic field regions of the umbra. Our study results indicate that the chromospheric umbral waves are likely excited by the p-mode oscillations.

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arXiv2024

Change Point Detection for High-dimensional Linear Models: A General Tail-adaptive Approach

We propose a novel approach for detecting change points in high-dimensional linear regression models. Unlike previous research that relied on strict Gaussian/sub-Gaussian error assumptions and had prior knowledge of change points, we propose a tail-adaptive method for change point detection and estimation. We use a weighted combination of composite quantile and least squared losses to build a new loss function, allowing us to leverage information from both conditional means and quantiles. For change point testing, we develop a family of individual testing statistics with different weights to account for unknown tail structures. These individual tests are further aggregated to construct a powerful tail-adaptive test for sparse regression coefficient changes. For change point estimation, we propose a family of argmax-based individual estimators. We provide theoretical justifications for the validity of these tests and change point estimators. Additionally, we introduce a new algorithm for detecting multiple change points in a tail-adaptive manner using the wild binary segmentation. Extensive numerical results show the effectiveness of our method. Lastly, an R package called ``TailAdaptiveCpt" is developed to implement our algorithms.

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arXiv2024

Knowledge Transfer across Multiple Principal Component Analysis Studies

Transfer learning has aroused great interest in the statistical community. In this article, we focus on knowledge transfer for unsupervised learning tasks in contrast to the supervised learning tasks in the literature. Given the transferable source populations, we propose a two-step transfer learning algorithm to extract useful information from multiple source principal component analysis (PCA) studies, thereby enhancing estimation accuracy for the target PCA task. In the first step, we integrate the shared subspace information across multiple studies by a proposed method named as Grassmannian barycenter, instead of directly performing PCA on the pooled dataset. The proposed Grassmannian barycenter method enjoys robustness and computational advantages in more general cases. Then the resulting estimator for the shared subspace from the first step is further utilized to estimate the target private subspace in the second step. Our theoretical analysis credits the gain of knowledge transfer between PCA studies to the enlarged eigenvalue gap, which is different from the existing supervised transfer learning tasks where sparsity plays the central role. In addition, we prove that the bilinear forms of the empirical spectral projectors have asymptotic normality under weaker eigenvalue gap conditions after knowledge transfer. When the set of informativesources is unknown, we endow our algorithm with the capability of useful dataset selection by solving a rectified optimization problem on the Grassmann manifold, which in turn leads to a computationally friendly rectified Grassmannian K-means procedure. In the end, extensive numerical simulation results and a real data case concerning activity recognition are reported to support our theoretical claims and to illustrate the empirical usefulness of the proposed transfer learning methods.

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arXiv2024

Simultaneous Change Point Detection and Identification for High Dimensional Linear Models

In this article, we consider change point inference for high dimensional linear models. For change point detection, given any subgroup of variables, we propose a new method for testing the homogeneity of corresponding regression coefficients across the observations. Under some regularity conditions, the proposed new testing procedure controls the type I error asymptotically and is powerful against sparse alternatives and enjoys certain optimality. For change point identification, an argmax based change point estimator is proposed which is shown to be consistent for the true change point location. Moreover, combining with the binary segmentation technique, we further extend our new method for detecting and identifying multiple change points. Extensive numerical studies justify the validity of our new method and an application to the Alzheimer's disease data analysis further demonstrate its competitive performance.

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arXiv2023

Onset mechanism of an inverted U-shaped solar filament eruption revealed by NVST, SDO, and STEREO-A observations

Utilizing observations from the New Vacuum Solar Telescope (NVST), Solar Dynamics Observatory (SDO), and Solar Terrestrial Relations Observatory-Ahead (STEREO-A), we investigate the event from two distinct observational perspectives: on the solar disk using NVST and SDO, and on the solar limb using STEREO-A. We employ both a non-linear force-free field model and a potential field model to reconstruct the coronal magnetic field, aiming to understand its magnetic properties. Two precursor jet-like activities were observed before the eruption, displaying an untwisted rotation. The second activity released an estimated twist of over two turns. During these two jet-like activities, Y-shaped brightenings, newly emerging magnetic flux accompanied by magnetic cancellation, and the formation of newly moving fibrils were identified. Combining these observational features, it can be inferred that these two precursor jet-like activities released the magnetic field constraining the filament and were triggered by newly emerging magnetic flux. Before the filament eruption, it was observed that some moving flows had been ejected from the site as the onset of two jet-like activities, indicating the same physical process as two jet-like activities. Extrapolations revealed that the filament laid under the height of the decay index of 1.0 and had strong magnetic field (540 Gauss) and a high twisted number (2.4 turns) before the eruption. An apparent rotational motion was observed during the filament eruption. We deduce that the solar filament, exhibiting an inverted U-shape, is a significantly twisted flux rope. The eruption of the filament was initiated by the release of constraining magnetic fields through continuous magnetic reconnection. This reconnection process was triggered by the emergence of newly magnetic flux.

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arXiv2022

Matrix Factor Analysis: From Least Squares to Iterative Projection

In this article, we study large-dimensional matrix factor models and estimate the factor loading matrices and factor score matrix by minimizing square loss function. Interestingly, the resultant estimators coincide with the Projected Estimators (PE) in Yu et al.(2022), which was proposed from the perspective of simultaneous reduction of the dimensionality and the magnitudes of the idiosyncratic error matrix. In other word, we provide a least-square interpretation of the PE for matrix factor model, which parallels to the least-square interpretation of the PCA for the vector factor model. We derive the convergence rates of the theoretical minimizers under sub-Gaussian tails. Considering the robustness to the heavy tails of the idiosyncratic errors, we extend the least squares to minimizing the Huber loss function, which leads to a weighted iterative projection approach to compute and learn the parameters. We also derive the convergence rates of the theoretical minimizers of the Huber loss function under bounded $(2+ε)$th moment of the idiosyncratic errors. We conduct extensive numerical studies to investigate the empirical performance of the proposed Huber estimators relative to the state-of-the-art ones. The Huber estimators perform robustly and much better than existing ones when the data are heavy-tailed, and as a result can be used as a safe replacement in practice. An application to a Fama-French financial portfolio dataset demonstrates the empirical advantage of the Huber estimator.

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arXiv2022

Manifold Principle Component Analysis for Large-Dimensional Matrix Elliptical Factor Model

Matrix factor model has been growing popular in scientific fields such as econometrics, which serves as a two-way dimension reduction tool for matrix sequences. In this article, we for the first time propose the matrix elliptical factor model, which can better depict the possible heavy-tailed property of matrix-valued data especially in finance. Manifold Principle Component Analysis (MPCA) is for the first time introduced to estimate the row/column loading spaces. MPCA first performs Singular Value Decomposition (SVD)for each "local" matrix observation and then averages the local estimated spaces across all observations, while the existing ones such as 2-dimensional PCA first integrates data across observations and then does eigenvalue decomposition of the sample covariance matrices. We propose two versions of MPCA algorithms to estimate the factor loading matrices robustly, without any moment constraints on the factors and the idiosyncratic errors. Theoretical convergence rates of the corresponding estimators of the factor loading matrices, factor score matrices and common components matrices are derived under mild conditions. We also propose robust estimators of the row/column factor numbers based on the eigenvalue-ratio idea, which are proven to be consistent. Numerical studies and real example on financial returns data check the flexibility of our model and the validity of our MPCA methods.

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arXiv2021

Simultaneous Cluster Structure Learning and Estimation of Heterogeneous Graphs for Matrix-variate fMRI Data

Graphical models play an important role in neuroscience studies, particularly in brain connectivity analysis. Typically, observations/samples are from several heterogenous groups and the group membership of each observation/sample is unavailable, which poses a great challenge for graph structure learning. In this article, we propose a method which can achieve Simultaneous Clustering and Estimation of Heterogeneous Graphs (briefly denoted as SCEHG) for matrix-variate function Magnetic Resonance Imaging (fMRI) data. Unlike the conventional clustering methods which rely on the mean differences of various groups, the proposed SCEHG method fully exploits the group differences of conditional dependence relationships among brain regions for learning cluster structure. In essence, by constructing individual-level between-region network measures, we formulate clustering as penalized regression with grouping and sparsity pursuit, which transforms the unsupervised learning into supervised learning. An ADMM algorithm is proposed to solve the corresponding optimization problem. We also propose a generalized criterion to specify the number of clusters. Extensive simulation studies illustrate the superiority of the SCEHG method over some state-of-the-art methods in terms of both clustering and graph recovery accuracy. We also apply the SCEHG procedure to analyze fMRI data associated with ADHD (abbreviated for Attention Deficit Hyperactivity Disorder), which illustrate its empirical usefulness. An R package ``SCEHG" to implement the method is available at https://github.com/heyongstat/SCEHG.

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arXiv2020

Projected Estimation for Large-dimensional Matrix Factor Models

In this study, we propose a projection estimation method for large-dimensional matrix factor models with cross-sectionally spiked eigenvalues. By projecting the observation matrix onto the row or column factor space, we simplify factor analysis for matrix series to that for a lower-dimensional tensor. This method also reduces the magnitudes of the idiosyncratic error components, thereby increasing the signal-to-noise ratio, because the projection matrix linearly filters the idiosyncratic error matrix. We theoretically prove that the projected estimators of the factor loading matrices achieve faster convergence rates than existing estimators under similar conditions. Asymptotic distributions of the projected estimators are also presented. A novel iterative procedure is given to specify the pair of row and column factor numbers. Extensive numerical studies verify the empirical performance of the projection method. Two real examples in finance and macroeconomics reveal factor patterns across rows and columns, which coincides with financial, economic, or geographical interpretations.

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