arXiv subjects

Haiqing Xu

Publications and source records attributed to Haiqing Xu.

At least 19 recordsLinked to original sources

arXiv2026

Distribution of magnetic helicity and energy with height in solar atmosphere

Magnetic helicity and magnetic energy are key to understanding the solar dynamo and eruptions, and their three-dimensional distributions are of great significance. However, how these quantities vary with height remains poorly understood. Moreover, because the three-dimensional distribution depends on magnetic field extrapolation, determining the optimal extrapolation height from physical rather than empirical criteria remains an open problem. To address this issue, this work investigates the vertical distributions of magnetic helicity and magnetic energy in the solar corona within active regions. We analyze 150 active regions observed by the Solar Magnetic Field Telescope (SMFT) from 1988 to 2019, grouped by absolute magnetic flux, perform nonlinear force-free field (NLFFF) extrapolations, and compute the relative magnetic helicity with a finite volume method. It is found that an extrapolation height of at least 81 Mm retains 97% of the total magnetic helicity and energy while reducing computational costs by approximately 38% under the adopted configuration. This work provides important parameter constraints for the long-term statistical study of magnetic helicity in solar active regions.

astro-ph.SR↗

arXiv2026

New Statistical Topology Theory Predicts Turbulent Magnetic Emergence from the Sun's Interior

We propose and verify a new statistical topology framework to study the complex magnetic field evolution of Sun-like stars. The Sun, as the star we are most familiar with, exhibits chaotic behaviors such as solar flares and mass ejections that are crucial to the Earth. While these phenomena are mainly driven by the magnetic field, it has been challenging to understand the complex magnetic field. In this paper, we propose a new model to understand the helicity behavior of magnetic loops before their emergence from the interior by advancing the loop ensemble theory from statistical physics. We derive several new power-law scalings that are essential to the Sun's magnetic field, including magnetic flux, magnetic helicity, and linking number. We examine our prediction by a large data analysis through long-term continuous observation over 32 yr. These results not only provide evidence for the new statistical topology framework but also systematically explain the intrinsic unpredictability on the emergence of extreme solar activities. This new discovery on the critical structure of loop ensemble can also be applied to a wide range of turbulence systems.

astro-ph.SR↗

arXiv2026

JW-VL: A Vision-Language Model for Solar Physics

Vision-Language Models (VLMs) have achieved breakthrough progress in general knowledge domains, yet adaptation to specialized scientific fields remains challenging due to multimodal representation shifts and the limited integration of domain-specific knowledge. To address the limitations of general-purpose VLMs when applied to solar physics image recognition, analysis, and reasoning, we propose JinWu Vision-Language (JW-VL), a fine-tuned foundation model tailored for solar physics. The model integrates multi-wavelength observational data from both space-based and ground-based telescopes, encompassing representative spectral bands spanning the photosphere, chromosphere, and corona. Built upon a cross-modal alignment knowledge distillation framework, JW-VL learns a joint visual-semantic embedding that enables end-to-end modeling from raw solar observational data to downstream tasks, including solar image recognition, solar activity analysis via image-based question answering, and optical character recognition (OCR), while also supporting the construction of a multi-band, cross-instrument solar image benchmark dataset. Furthermore, as a demonstration of interdisciplinary applicability, we developed a "Daily Solar Activity Reports" agent comprising core modules for solar activity level assessment, significant active region characterization, magnetic field complexity analysis, potential space weather impact assessment, and identifying active regions for targeted observation. While JW-VL may not yet meet the rigorous, high-precision demands of operational solar physics, it bridges raw observations and diverse downstream tasks, establishing a valuable methodological framework for applying multimodal deep learning to the field.

astro-ph.SR↗

arXiv2026

Advances and Challenges in Solar Flare Prediction: A Review

Solar flares, as one of the most prominent manifestations of solar activity, have a profound impact on both the Earth's space environment and human activities. As a result, accurate solar flare prediction has emerged as a central topic in space weather research. In recent years, substantial progress has been made in the field of solar flare forecasting, driven by the rapid advancements in space observation technology and the continuous improvement of data processing capabilities. This paper presents a comprehensive review of the current state of research in this area, with a particular focus on tracing the evolution of data-driven approaches -- which have progressed from early statistical learning techniques to more sophisticated machine learning and deep learning paradigms, and most recently, to the emergence of Multimodal Large Models (MLMs). Furthermore, this study examines the realistic performance of existing flare forecasting platforms, elucidating their limitations in operational space weather applications and thereby offering a practical reference for future advancements in technological optimization and system design.

astro-ph.SR↗

arXiv2025

JW-Flare: Accurate Solar Flare Forecasting Method Based on Multimodal Large Language Models

Solar flares, the most powerful explosive phenomena in the solar system, may pose significant hazards to spaceborne satellites and ground-based infrastructure. Despite decades of intensive research, reliable flare prediction remains a challenging task. Large Language Models, as a milestone in artificial intelligence, exhibit exceptional general knowledge and next-token prediction capabilities. Here we introduce JW-Flare, the first Multimodal Large Language Models (MLLMs) explicitly trained for solar flare forecasting through fine-tuning on textual physic parameters of solar active regions and magnetic field images. This method demonstrates state-of-the-art (SOTA) performance for large flares prediction on the test dataset. It effectively identifies all 79 X-class flares from 18,949 test samples, yielding a True Skill Statistic (TSS) of 0.95 and a True Positive Rate (TPR) of 1.00, outperforming traditional predictive models. We further investigate the capability origins of JW-Flare through explainability experiments, revealing that solar physics knowledge acquired during pre-training contributes to flare forecasting performance. Additionally, we evaluate models of different parameter scales, confirming the Scaling_Law of Large Language Models in domain-specific applications, such as solar physics. This study marks a substantial advance in both the scale and accuracy of solar flare forecasting and opens a promising avenue for AI-driven methodologies in broader scientific domains.

astro-ph.SR↗

arXiv2025

On Quantile Treatment Effects, Rank Similarity,and Variation of Instrumental Variables

This paper develops a nonparametric framework to identify and estimate distributional treatment effects under nonseparable endogeneity. We begin by revisiting the widely adopted \emph{rank similarity} (RS) assumption and characterizing it by the relationship it imposes between observed and counterfactual potential outcome distributions. The characterization highlights the restrictiveness of RS, motivating a weaker identifying condition. Under this alternative, we construct identifying bounds on the distributional treatment effects of interest through a linear semi-infinite programming (SILP) formulation. Our identification strategy also clarifies how richer exogenous instrument variation, such as multi-valued or multiple instruments, can further tighten these bounds. Finally, exploiting the SILP's saddle-point structure and Karush-Kuhn-Tucker (KKT) conditions, we establish large-sample properties for the empirical SILP: consistency and asymptotic distribution results for the estimated bounds and associated solutions.

econ.EM↗

arXiv2025

Characterizing Sobolev Homeomorphic Extensions via Internal Distances

We give a full characterization of embeddings of the unit circle that admit a Sobolev homeomorphic extension to the unit disk. As a direct corollary, we establish that for quasiconvex target domains $\mathbb Y$, any homeomorphism $φ\colon \partial \mathbb{D} \to \partial \mathbb Y$ that admits a continuous $W^{1,p}$-extension to the unit disk $\mathbb{D}$ also admits a $W^{1,p}$-homeomorphic extension. These Sobolev variants of the classical Jordan-Schönflies theorem are essential for ensuring the well-posedness of variational problems arising in Nonlinear Elasticity and Geometric Function Theory.

math.CV↗

arXiv2025

Option Market Making via Reinforcement Learning

Market making of options with different maturities and strikes is a challenging problem due to its highly dimensional nature. In this paper, we propose a novel approach that combines a stochastic policy and reinforcement learning-inspired techniques to determine the optimal policy for posting bid-ask spreads for an options market maker who trades options with different maturities and strikes.

q-fin.TR↗

arXiv2024

Econometrics of Insurance with Multidimensional Types

In this paper, we address the identification and estimation of insurance models where insurees have private information about their risk and risk aversion. The model includes random damages and allows for several claims, while insurers choose from a finite number of coverages. We show that the joint distribution of risk and risk aversion is nonparametrically identified despite bunching due to multidimensional types and a finite number of coverages. Our identification strategy exploits the observed number of claims as well as an exclusion restriction, and a full support assumption. Furthermore, our results apply to any form of competition. We propose a novel estimation procedure combining nonparametric estimators and GMM estimation that we illustrate in a Monte Carlo study.

econ.GN↗

arXiv2024

Homeomorphic Sobolev extensions of parametrizations of Jordan curves

Each homeomorphic parametrization of a Jordan curve via the unit circle extends to a homeomorphism of the entire plane. It is a natural question to ask if such a homeomorphism can be chosen so as to have some Sobolev regularity. This prompts the simplified question: for a homeomorphic embedding of the unit circle into the plane, when can we find a homeomorphism from the unit disk that has the same boundary values and integrable first-order distributional derivatives? We give the optimal geometric criterion for the interior Jordan domain so that there exists a Sobolev homeomorphic extension for any homeomorphic parametrization of the Jordan curve. The problem is partially motivated by trying to understand which boundary values can correspond to deformations of finite energy.

math.CV↗

arXiv2024

Correction for the Weakening Magnetic Field within the Sunspot Umbra Observed by ASO-S/FMG

The magnetic field inside the sunspot umbra, as observed by the Full-disk MagnetoGraph (FMG) onboard the Advanced Space based Solar Observatory (ASO-S), was found to be experiencing a weakening. To address this issue, we employed a method developed by Xu et al. (2021) to correct the weakening in the data of 20 active regions observed by FMG during the period spanning December 29, 2022, to July 23, 2023. Research has revealed that the onset of magnetic field weakening occurs at a minimum magnetic field strength of 705 G, with the peak strength reaching up to 1931 G. We computed the change ratio (R1) of the unsigned magnetic flux within the sunspot umbra, considering measurements both before and after correction. The change ratio (R1) spans from 26% to 124%, indicating a significant increase in the unsigned magnetic flux within sunspot umbrae observed by FMG after correction. To illustrate this, we selected four active regions for comparison with data from the Helioseismic and Magnetic Imager (HMI). After correction, it is found that the unsigned magnetic flux in sunspot umbrae measured by FMG aligns more closely with that of HMI. This supports the effectiveness of the corrective method for FMG, despite imperfections, particularly at the umbra-penumbra boundary.

astro-ph.SR↗

arXiv2024

Observation of a large-scale filament eruption initiated by two small-scale erupting filaments pushing out from below

Filament eruptions often result in flares and coronal mass ejections (CMEs). Most studies attribute the filament eruptions to their instabilities or magnetic reconnection. In this study, we report a unique observation of a filament eruption whose initiation process has not been reported before. This large-scale filament, with a length of about 360 Mm crossing an active region, is forced to erupted by two small-scale erupting filaments pushing out from below. This process of multi-filament eruption results in an M6.4 flare in the active region NOAA 13229 on 25th February 2023. The whole process can be divided into three stages: the eruptions of two active-region filaments F1 and F2; the interactions between the erupting F1, F2, and the large-scale filament F3; and the eruption of F3. Though this multi-filament eruption occurs near the northwest limb of the solar disk, it produces a strong halo CME that causes a significant geomagnetic disturbance. Our observations present a new filament eruption mechanism, in which the initial kinetic energy of the eruption is obtained from and transported to by other erupting structures. This event provides us a unique insight into the dynamics of multi-filament eruptions and their corresponding effects on the interplanetary space.

astro-ph.SR↗

arXiv2023

On Quantile Treatment Effects, Rank Similarity, and Variation of Instrumental Variables

This paper investigates how certain relationship between observed and counterfactual distributions serves as an identifying condition for treatment effects when the treatment is endogenous, and shows that this condition holds in a range of nonparametric models for treatment effects. To this end, we first provide a novel characterization of the prevalent assumption restricting treatment heterogeneity in the literature, namely rank similarity. Our characterization demonstrates the stringency of this assumption and allows us to relax it in an economically meaningful way, resulting in our identifying condition. It also justifies the quest of richer exogenous variations in the data (e.g., multi-valued or multiple instrumental variables) in exchange for weaker identifying conditions. The primary goal of this investigation is to provide empirical researchers with tools that are robust and easy to implement but still yield tight policy evaluations.

econ.EM↗

arXiv2023

Observations of Magnetic Helicity Proxies in Solar Photosphere: Helicity with Solar Cycles

Observations of magnetic helicity transportation through the solar photosphere reflect the interaction of turbulent plasma movements and magnetic fields in the solar dynamo process. In this chapter, we have reviewed the research process of magnetic helicity inferred from the observed solar magnetic fields in the photosphere and also the solar morphological configurations with solar cycles. After introducing some achievements in the study of magnetic helicity, some key points would like to be summarized. The magnetic (current) helicity in the solar surface layer presents a statistical distribution similar to that of the sunspot butterfly diagram, but its maximum value is delayed from the extreme value of the sunspot butterfly diagram and corresponds in the phase with the statistical eruption of solar flares. During the spatial transport of magnetic (current) helicity from the interior of the sun into the interplanetary space at the time-space scale of the solar cycle, it shows the statistical distribution and the fluctuation with the hemispheric sign rule. These show that the current helicity and magnetic helicity transport calculation methods are complementary to each other. We also notice that the study of the inherent relationship between magnetic helicity and the solar cycle still depends on the observed accuracy of the solar magnetic field.

astro-ph.SR↗

arXiv2023

Over-the-Counter Market Making via Reinforcement Learning

The over-the-counter (OTC) market is characterized by a unique feature that allows market makers to adjust bid-ask spreads based on order size. However, this flexibility introduces complexity, transforming the market-making problem into a high-dimensional stochastic control problem that presents significant challenges. To address this, this paper proposes an innovative solution utilizing reinforcement learning techniques to tackle the OTC market-making problem. By assuming a linear inverse relationship between market order arrival intensity and bid-ask spreads, we demonstrate the optimal policy for bid-ask spreads follows a Gaussian distribution. We apply two reinforcement learning algorithms to conduct a numerical analysis, revealing the resulting return distribution and bid-ask spreads under different time and inventory levels.

q-fin.TR↗

arXiv2023

A Local Machine Learning Approach for Fingerprint-based Indoor Localization

Machine learning (ML) solutions to indoor localization problems have become popular in recent years due to high positioning accuracy and low cost of implementation. This paper proposes a novel local nonparametric approach for solving localizations from high-dimensional Received Signal Strength Indicator (RSSI) values. Our approach consists of a sequence of classification algorithms that sequentially narrows down the possible space for location solutions into smaller neighborhoods. The idea of this sequential classification method is similar to the decision tree algorithm, but a key difference is our splitting of the dataset at each node is not based on features of input (i.e. RSSI values), but some discrete-valued variables generated from the output variable (i.e. the 3D real-world coordinates). The strength of our localization solution can be tuned to problem specifics by the appropriate choice of how to sequentially partition the the space of location into smaller neighborhoods. Using the publicly available indoor localization dataset UJIIndoorLoc, we evaluate our proposed method vs. the global ML algorithms for the dataset. The primary contribution of this paper is to introduce a novel local ML solution for indoor localization problems.

eess.SP↗

arXiv2021

Improvements of the Longitudinal Magnetic Field Measurement from the Solar Magnetic Field Telescope at Huairou Solar Observing Station

The weak-field approximation implying linear relationship between Stokes $V/I$ and longitudinal magnetic field, $B_{\Vert}$, often suffers from saturation observed in strong magnetic field regions such as sunspot umbra. In this work, we intend to improve the magnetic field observations carried out by the \textit{Solar Magnetic Field Telescope} (SMFT) at Huairou Solar Observing Station, China. We propose using non-linear relationship between Stokes $V/I$ and $B_{\Vert}$ to derive the magnetic field. To determine the form of the relationship, we perform a cross-calibration of the observed SMFT data and magnetograms provided by the \textit{Helioseismic and Magnetic Imager} on board the \textit{Solar Dynamics Observatory}. The algorithm of the magnetic field derivation is described in details. We show that using non-linear relationship between Stokes $V/I$ and $B_{\Vert}$ allows us to eliminate magnetic field saturation inside sunspot umbra. The proposed technique enables one to enhance the reliability of the SMFT magnetic field data obtained even long before the space-based instrumentation era, since 1987.

astro-ph.SR↗

arXiv2021

$p$-harmonic mappings between metric spaces

In this paper, we solve the Dirichlet problem for Sobolev maps between singular metric spaces that extends the corresponding result of Guo and Wenger [Comm. Anal. Geom. 2020]. The main new ingredient in our proofs is a suitable extension of the theory of trace for metric valued Sobolev maps developed by Korevaar and Schoen [Comm. Anal. Geom. 1993]. We also develop a theory of trace in the borderline case, which investigates a sharp condition to characterize the existence of traces.

math.AP↗