arXiv subjects

Lulu Zhao

Publications and source records attributed to Lulu Zhao.

At least 19 recordsLinked to original sources

arXiv2026

Counterintuitive Magnetic Connectivity and Energetic Particle Flux Differences among Nearby Spacecraft During the 2023 February 24 Solar Energetic Particle Event

For solar energetic particles (SEPs), it is generally expected that observers magnetically closer to the eruption source region exhibit higher particle intensities than those poorly connected to the eruption site. However, the 2023 February 24 SEP event departs from this simple picture: Earth and STA, near 1 au, are nominally better connected to the source region, whereas Solar Orbiter (SolO), at 0.77 au but less favorably connected, observed SEP fluxes more than an order of magnitude higher. This difference cannot be simply explained by nominal magnetic connectivity or radial scaling of SEP fluxes alone. To investigate this behavior, we perform a global magnetohydrodynamic simulation of the associated coronal mass ejection (CME) using the Alfvén Wave Solar-atmosphere Model-Realtime (AWSoM-R). The simulation reveals that the CME flux rope originates close to a coronal streamer and as it propagates and expands, the CME-driven shock is effectively distorted, developing into two distinct flanks with different strengths. Although the three spacecraft are separated by only $\lesssim$30$^{\circ}$ in heliolongitude, their magnetic footpoints differ by $\gtrsim$50$^{\circ}$ in longitude because of a nearby stream interaction region. Specifically, Earth and STA connect to a weaker shock region, while SolO connects to the shock nose with a higher compression ratio and more efficient particle acceleration. We further simulate SEPs using the Multiple-Field-Line Advection Model for Particle Acceleration (M-FLAMPA) coupled with AWSoM-R, obtaining results that reproduce the observed flux differences among the three spacecraft, demonstrating that this counterintuitive behavior results from their connections to different regions of the inhomogeneous CME-driven shock.

physics.space-ph↗

arXiv2026

Adaptive Hybrid Modeling of Collisionless Plasma Shocks and Ion Acceleration

We present a novel efficient technique for hybrid (kinetic ions, quasi-neutral fluid electrons) simulations of non-relativistic magnetized collisionless plasma shocks, frequently observed near the Sun, in the solar system, and beyond. This Adaptive Frame-Of-Reference Algorithm (AFORA) enables multi-dimensional simulations of plasma shocks along with concomitant ion acceleration in the shock frame, where shock evolution remains quasi-steady. Compared to moving shocks, this technique allows us to reduce the simulation time and domain size to a minimum while achieving converged shock dynamics and spectra of energetic ions. Using an event-driven (asynchronous) hybrid code, HYPERS, we demonstrate this approach in two spatial dimensions for different orientations of the background magnetic field with respect to the shock normal. Our results show excellent agreement of simulation shocks with observations of interplanetary (IP) shocks. We verify that different shock configurations (quasi-parallel, oblique, and quasi-perpendicular) convert bulk plasma flow energy into ion acceleration with varying degrees of efficiency. These findings underscore the importance of efficient and robust numerical algorithms for future high-resolution modeling of plasma shocks and ion acceleration in three dimensions. In addition to enabling efficient computational studies of collisionless shocks in general, this work paves the way for accurate prediction of seed populations of Solar Energetic Particles (SEPs), generated by coronal mass ejection (CME) shocks. The characteristics of seed ions can be used as inputs to Fokker-Planck models that simulate long-term transport and acceleration of ions along magnetic field lines through their interactions with background solar wind turbulence.

physics.space-ph↗

arXiv2026

The Effects of Energy Conservation in Simulating Solar Eruptions

Strict energy conservation is, perhaps, the most basic principle in all physics, but has proven to be difficult to satisfy in numerical simulations of solar eruptions. The Alfvén Wave Solar atmosphere Model (AWSoM) is used to perform a rigorous comparison of CME simulations whose only difference is the use of a conservative vs. non-conservative scheme for the energy equation. A simple, symmetric active region is assumed for the initial magnetic field. As expected, the different numerical schemes result in very different plasma thermal energy, but surprisingly, we also find a factor $>2$ difference in the final kinetic energy, with the energy substantially larger in the energy-conservative scheme. The increase in thermal energy is comparable to the increase in kinetic energy in the conservative simulation. Our analysis reveals that the flare reconnection and increase of kinetic energy terminate earlier with the non-conservative scheme. We conclude that the plasma thermodynamics plays a critical role in the flare reconnection, with the thermal pressure gradient in the current sheet slowing down the reconnection. Our results imply that using strict energy-conservative numerics is critical for space weather modeling of CMEs and for understanding the CME energy budget partitioning.

astro-ph.SR↗

arXiv2026

Simulating the Solar Corona with Multiple Solar Photospheric Magnetic Maps during the 8 April 2024 Total Solar Eclipse

The 8 April 2024 total solar eclipse (TSE) provides a unique opportunity to study the solar corona. This work presents our simulations of the solar corona at the time of the eclipse based on magnetohydrodynamic (MHD) modeling performed with the Alfvén Wave Solar atmosphere Model (AWSoM) in the Space Weather Modeling Framework, developed at the University of Michigan. We performed multiple simulations based on photospheric magnetic maps from four sources, i.e., ADAPT-GONG, Lockheed Martin ESFAM-HMI, HipFT-HMI, and NSO-NRT-HMI maps. Our study focuses on how differences in the magnetic field maps affect the coronal magnetic field structure and coronal heating properties in the simulation. The synthesized observables show remarkable differences due to the distinct magnetic coronal topologies, which stem from the different local magnetic flux distributions. We analyze the properties of the open magnetic flux regions of the models. We also study the coronal heating rate in the models. The total volume integrated heating rate yields a difference of $20\%$ across the models. The results also show that the differential emission measure in the high-temperature regions is sensitive to the magnetic field maps. Our findings underscore the importance of comprehensive photospheric magnetic field data in improving future solar coronal models.

astro-ph.SR↗

arXiv2026

Realtime forecasting of solar energetic particle event and proton flux using multi-source solar observations and multi-task deep learning

Solar energetic particle (SEP) events, defined by proton flux exceeding 10 pfu in the > 10 MeV channel, pose major risks to spacecraft operations, astronaut safety, and high-latitude aviation. Due to the complexity and rarity of SEP events, reliable operational SEP forecasting remains an important challenge in space weather. Here we present a novel 24-hour-ahead realtime forecasting framework, SEPNET-PRISM, based on a multi-task learning structure and a thoroughly constructed list of features from multiple sources spanning multiple solar cycles, that jointly predicts SEP event occurrence and future proton and soft X-ray fluxes. SEPNET-PRISM extends the earlier-introduced SEPNET-based models by integrating a broader range of solar observations, including active-region magnetic parameters from SHARP and SMARP, solar-flare information, coronal mass ejections, soft X-ray flux, and historical > 10 MeV proton flux. As compared with SEPNET, the inclusion of SMARP data expands the temporal coverage of magnetic-field predictors to earlier solar cycles, while flux-based inputs provide additional precursor information. Evaluation on the CLEAR SEP benchmark dataset shows improved classification performance over the earlier SEPNET-O (operational version of SEPNET) on the newly aligned dataset. The best operational model is obtained when magnetic, radiative, and proton-flux predictors are combined, highlighting the value of expanded historical coverage and complementary precursor information for improving realtime SEP forecasting.

astro-ph.SR↗

arXiv2026

SEP-PRISM Data: A multi-source dataset for solar energetic particle forecasting

Solar energetic particle (SEP) event forecasting often involves integrating heterogeneous observations that differ in cadence, temporal coverage, format, and historical availability, posing challenges for reproducible analysis of data-driven approaches. This paper presents SEP-PRISM Data, a curated multi-source dataset designed for 24-hour ahead forecasting of operational SEP events, defined by proton flux exceeding 10 pfu in the GOES > 10 MeV channel. SEP-PRISM Data integrates flare records, active-region magnetic field parameters, coronal mass ejection (CME) catalogue data, GOES soft X-ray flux, and historical proton flux into a common window-based representation spanning 3 February 1986 to 10 September 2025. To improve temporal coverage and cross-source consistency, SHARP and SMARP magnetic products were aligned into a unified SMHARP archive, and CME records from DONKI and CDAW were aligned into a unified CDAWDONKI event set. Predictor variables were summarized over fixed non-overlapping 24-hour historical windows using minimum, mean, and maximum statistics and paired with targets defined over the subsequent 24-hour window, forming a supervised learning dataset. The resulting SEP-PRISM Data contains 14,464 labeled samples, including 650 positive operational SEP cases, and is intended to support reproducible benchmarking, model development, feature analysis, and future studies of space weather forecasting.

astro-ph.SR↗

arXiv2026

Defects and Inconsistencies in Solar Flare Data Sources: Implications for Machine Learning Forecasting

Machine learning models for forecasting solar flares have been trained and evaluated using a variety of data sources, including Space Weather Prediction Center (SWPC) operational and science-quality data. Typically, data from these sources is minimally processed before being used to train and validate a forecasting model. However, predictive performance can be affected if defects and inconsistencies between these data sources are ignored. For a set of commonly used data sources, along with the software that queries and outputs processed data, we identify their defects and inconsistencies, quantify their extent, and show how they can affect predictions from data-driven machine-learning forecasting models. We also outline procedures for fixing these issues or at least mitigating their impacts. Finally, based on thorough comparisons of the effects of data sources on the trained forecasting model's predictive skill scores, we offer recommendations for using different data products in operational forecasting.

astro-ph.SR↗

arXiv2026

Review of Machine Learning Models for Solar Energetic Particle Prediction

Solar energetic particle (SEP) events have attracted increasing attention due to their significant radiation hazards for aviation, spacecraft electronics, and human missions beyond Earth's magnetosphere. From a scientific perspective, SEP events are intriguing because they arise from a set of physical processes extending from the solar surface and corona through the heliosphere, offering insight into particle acceleration and transport mechanisms that are widely applicable across astrophysics. Therefore, advancing our ability to understand and predict SEP events is essential both for deepening our knowledge of such mechanisms and for safeguarding space technologies and exploration. Traditionally, researchers have modeled SEPs using physics-based simulations and empirical methods. More recently, machine learning (ML) has emerged as a new tool for understanding and predicting SEP events. The purpose of this manuscript is to review the currently available ML models for SEP prediction, identify the datasets used for training, compare their architectures, inputs, and outputs, and, based on these insights, outline good practices and recommendations for future research.

astro-ph.SR↗

arXiv2026

Effects of the Background Magnetic Field on Flux Rope Eruptions

Solar eruptive events are generally believed to involve magnetic flux ropes (MFR), formed either in the pre-eruptive phase of the event or during the eruption itself. These MFR eruptions exhibit significant complexity and variations due to the interplay of the physical mechanisms involved, in particular magnetic reconnection and ideal instabilities. This work considers the effect of the background magnetic field on the nature of eruptions with pre-existing MFRs. We used a new MHD model to simulate the whole MFR eruption process, including the pre-eruptive stage and the initiation. Three simulations were performed, all of which used an identical bipolar active region, but with different background magnetic fields in the three cases. The simulations resulted in two successful eruptions (CMEs) and one failed eruption (a confined flare). We analyzed the energetics and the acceleration of the MFR in detail, and found a transition to a rapid exponential rise phase in two of the simulations. We also calculated the criterion for the torus instability and the timing of the breakout and flare reconnections. Our results show that the rapid exponential rise phase is likely due to breakout reconnection. We conclude that a background field antiparallel to the active-region field lowers the magnetic free-energy threshold for eruption; but, does not guarantee a successful eruption. We also found that an antiparallel background field leads to faster flare reconnection, but of shorter duration. Our findings underscore the importance of the background magnetic field in understanding CMEs.

astro-ph.SR↗

arXiv2026

Solar Energetic Particle Forecasting with Multi-Task Deep Learning: SEPNET

Solar energetic particle (SEP) events pose severe threats to spacecraft, astronaut safety, and aviation operations. Accurate SEP forecasting remains a critical challenge in space weather research due to their complex origins and highly variable propagation. In this work, we built SEPNET, an innovative multi-task neural network that jointly predicts future solar eruptive events, including solar flares and coronal mass ejections (CMEs) and SEPs, incorporating long short-term memory and transformer architectures that capture contextual dependencies. SEPNet is a machine learning framework for SEP prediction that utilizes an extensive set of predictors, including solar flares, CMEs, and space-weather HMI active region patches (SHARP) magnetic field parameters. SEPNET is rigorously evaluated on the SEPVAL SEP dataset (Whitman, 2025b), which is used to evaluate the performance of the current SEP prediction models. The performance of SEPNet is compared with classical machine learning methods and current state-of-the-art pre-eruptive SEP prediction models. The results show that SEPNET, particularly with SHARP parameters, achieves higher detection rates and skill scores while maintaining suitable for real-time space weather alert operations. Although class imbalance in the data leads to relatively high false alarm rates, SEPNET consistently outperforms reference methods and provides timely SEP forecasts, highlighting the capability of deep multi-task learning for next-generation space weather prediction. All data and code are available on GitHub at https://github.com/yuyian/SEP-Prediction.git.

physics.space-ph↗

arXiv2026

Simulated Operational Testing of the Prototype Implementation of the SOFIE Model: The 2025 Space Weather Prediction Testbed Exercise

The CLEAR Space Weather Center of Excellence's solar energetic particle (SEP) model, SOlar wind with FIeld lines and Energetic particles (SOFIE), was run and evaluated on-site during the Space Weather Prediction Testbed (SWPT) exercise at the National Oceanic and Atmospheric Administration's Space Weather Prediction Center (NOAA/SWPC) in May 2025. As a physics-based SEP model, SOFIE simulates the acceleration and transport of energetic particles by the coronal mass ejection (CME)-driven shock in the solar corona and inner heliosphere, and has been validated against historical events. However, questions remain regarding whether a physics-based model, traditionally considered computationally expensive, could meet operational needs. The SWPT exercise offered a valuable opportunity to evaluate SOFIE under simulated operational conditions. On-site interactive feedback from SWPC forecasters, Space Radiation Analysis Group (SRAG) console operators, Community Coordinated Modeling Center (CCMC) personnel, and Moon-to-Mars Space Weather Analysis Office (M2M SWAO) analysts led to significant strategic improvements in the model configuration. The simulation grid was optimized by combining a coarser background grid with higher-resolution regions along the CME path and toward Earth, reducing computational cost without compromising accuracy. In this work, we present the simulated operational performance of SOFIE and its capability to predict SEP fluxes significantly faster than real time. During the SWPT exercise, SOFIE completed a 4-day SEP simulation within 5 hours using 1,000 central processing unit cores, although the earliest SEP forecast was obtained a few hours after CME onset. This marks a milestone in demonstrating SOFIE's operational usefulness and robustness to support future human space exploration.

astro-ph.SR↗

arXiv2025

Evidence of Time-Dependent Diffusive Shock Acceleration in the 2022 September 5 Solar Energetic Particle Event

On 2022 September 5, a large solar energetic particle (SEP) event was detected by Parker Solar Probe (PSP) and Solar Orbiter (SolO), at heliocentric distances of 0.07 and 0.71 au, respectively. PSP observed an unusual velocity-dispersion signature: particles below $\sim$1 MeV exhibited a normal velocity dispersion, while higher-energy particles displayed an inverse velocity arrival feature, with the most energetic particles arriving later than those at lower energies. The maximum energy increased from about 20-30 MeV upstream to over 60 MeV downstream of the shock. The arrival of SEPs at PSP was significantly delayed relative to the expected onset of the eruption. In contrast, SolO detected a typical large SEP event characterized by a regular velocity dispersion at all energies up to 100 MeV. To understand these features, we simulate particle acceleration and transport from the shock to the observers with our newly developed SEP model - Particle ARizona and MIchigan Solver on Advected Nodes (PARMISAN). Our results reveal that the inverse velocity arrival and delayed particle onset detected by PSP originate from the time-dependent diffusive shock acceleration processes. After shock passage, PSP's magnetic connectivity gradually shifted due to its high velocity near perihelion, detecting high-energy SEPs streaming sunward. Conversely, SolO maintained a stable magnetic connection to the strong shock region where efficient acceleration was achieved. These results underscore the importance of spatial and temporal dependence in SEP acceleration at interplanetary shocks, and provide new insights to understand SEP variations in the inner heliosphere.

astro-ph.SR↗

arXiv2025

AutoRed: A Free-form Adversarial Prompt Generation Framework for Automated Red Teaming

The safety of Large Language Models (LLMs) is crucial for the development of trustworthy AI applications. Existing red teaming methods often rely on seed instructions, which limits the semantic diversity of the synthesized adversarial prompts. We propose AutoRed, a free-form adversarial prompt generation framework that removes the need for seed instructions. AutoRed operates in two stages: (1) persona-guided adversarial instruction generation, and (2) a reflection loop to iteratively refine low-quality prompts. To improve efficiency, we introduce a verifier to assess prompt harmfulness without querying the target models. Using AutoRed, we build two red teaming datasets -- AutoRed-Medium and AutoRed-Hard -- and evaluate eight state-of-the-art LLMs. AutoRed achieves higher attack success rates and better generalization than existing baselines. Our results highlight the limitations of seed-based approaches and demonstrate the potential of free-form red teaming for LLM safety evaluation. We will open source our datasets in the near future.

cs.CL↗

arXiv2025

RecGPT Technical Report

Recommender systems are among the most impactful applications of artificial intelligence, serving as critical infrastructure connecting users, merchants, and platforms. However, most current industrial systems remain heavily reliant on historical co-occurrence patterns and log-fitting objectives, i.e., optimizing for past user interactions without explicitly modeling user intent. This log-fitting approach often leads to overfitting to narrow historical preferences, failing to capture users' evolving and latent interests. As a result, it reinforces filter bubbles and long-tail phenomena, ultimately harming user experience and threatening the sustainability of the whole recommendation ecosystem. To address these challenges, we rethink the overall design paradigm of recommender systems and propose RecGPT, a next-generation framework that places user intent at the center of the recommendation pipeline. By integrating large language models (LLMs) into key stages of user interest mining, item retrieval, and explanation generation, RecGPT transforms log-fitting recommendation into an intent-centric process. To effectively align general-purpose LLMs to the above domain-specific recommendation tasks at scale, RecGPT incorporates a multi-stage training paradigm, which integrates reasoning-enhanced pre-alignment and self-training evolution, guided by a Human-LLM cooperative judge system. Currently, RecGPT has been fully deployed on the Taobao App. Online experiments demonstrate that RecGPT achieves consistent performance gains across stakeholders: users benefit from increased content diversity and satisfaction, merchants and the platform gain greater exposure and conversions. These comprehensive improvement results across all stakeholders validates that LLM-driven, intent-centric design can foster a more sustainable and mutually beneficial recommendation ecosystem.

cs.IR↗

arXiv2025

Manipulating Elasto-Plastic Objects With 3D Occupancy and Learning-Based Predictive Control

Manipulating elasto-plastic objects remains a significant challenge due to severe self-occlusion, difficulties of representation, and complicated dynamics. This work proposes a novel framework for elasto-plastic object manipulation with a quasi-static assumption for motions, leveraging 3D occupancy to represent such objects, a learned dynamics model trained with 3D occupancy, and a learning-based predictive control algorithm to address these challenges effectively. We build a novel data collection platform to collect full spatial information and propose a pipeline for generating a 3D occupancy dataset. To infer the 3D occupancy during manipulation, an occupancy prediction network is trained with multiple RGB images supervised by the generated dataset. We design a deep neural network empowered by a 3D convolution neural network (CNN) and a graph neural network (GNN) to predict the complex deformation with the inferred 3D occupancy results. A learning-based predictive control algorithm is introduced to plan the robot actions, incorporating a novel shape-based action initialization module specifically designed to improve the planner efficiency. The proposed framework in this paper can successfully shape the elasto-plastic objects into a given goal shape and has been verified in various experiments both in simulation and the real world.

cs.RO↗

arXiv2025

B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners

In the absence of extensive human-annotated data for complex reasoning tasks, self-improvement -- where models are trained on their own outputs -- has emerged as a primary method for enhancing performance. However, the critical factors underlying the mechanism of these iterative self-improving methods remain poorly understood, such as under what conditions self-improvement is effective, and what are the bottlenecks in the current iterations. In this work, we identify and propose methods to monitor two pivotal factors in this iterative process: (1) the model's ability to generate sufficiently diverse responses (exploration); and (2) the effectiveness of external rewards in distinguishing high-quality candidates from lower-quality ones (exploitation). Using mathematical reasoning as a case study, we begin with a quantitative analysis to track the dynamics of exploration and exploitation, discovering that a model's exploratory capabilities rapidly deteriorate over iterations, and the effectiveness of exploiting external rewards diminishes as well. Motivated by these findings, we introduce B-STaR, a Self-Taught Reasoning framework that autonomously adjusts configurations across iterations to Balance exploration and exploitation, thereby optimizing the self-improving effectiveness based on the current policy model and available rewards. Our experiments on mathematical reasoning, coding, and commonsense reasoning demonstrate that B-STaR not only enhances the model's exploratory capabilities throughout training but also achieves a more effective balance between exploration and exploitation, leading to superior performance.

cs.AI↗

arXiv2025

Physics-Based Simulation of the 2013 April 11 Solar Energetic Particle Event

Solar energetic particles (SEPs) can pose hazardous radiation risks to both humans and spacecraft electronics in space. Numerical modeling based on first principles offers valuable insights into the underlying physics of SEPs and provides synthetic observables for SEPs at any time and location in the inner heliosphere. In this work, we present a numerical scheme, which conserves the number of particles based on integral relations for Poisson brackets \citep{sokolov2023high}, to solve the kinetic equation for particle acceleration and transport processes. We implement this scheme within the Space Weather Modeling Framework, developed at the University of Michigan. In addition, we develop a new shock-capturing tool to study the coronal mass ejection-driven shock originating from the low solar corona. These methodological advancements are applied to conduct a comprehensive study of a historical SEP event on April 11, 2013. Multi-spacecraft observations, including SOHO, SDO, GOES and ACE near Earth, and STEREO-A/B, are used for model--data comparison and validation. We show synthetic observables, including extreme ultraviolet and white-light images, proton time--intensity profiles, and energy spectra, and discuss their differences and probable explanations compared to observations. Our simulation results demonstrate the application of the Poisson bracket scheme with a particle solver to simulating a historical SEP event. We also show the capability of extracting the complex shock surface using our shock-capturing tool and understand how the complex shock surface affects the particle acceleration process.

astro-ph.SR↗

arXiv2024

CareBot: A Pioneering Full-Process Open-Source Medical Language Model

Recently, both closed-source LLMs and open-source communities have made significant strides, outperforming humans in various general domains. However, their performance in specific professional domains such as medicine, especially within the open-source community, remains suboptimal due to the complexity of medical knowledge. In this paper, we propose CareBot, a bilingual medical LLM, which leverages a comprehensive approach integrating continuous pre-training (CPT), supervised fine-tuning (SFT), and reinforcement learning with human feedback (RLHF). Our novel two-stage CPT method, comprising Stable CPT and Boost CPT, effectively bridges the gap between general and domain-specific data, facilitating a smooth transition from pre-training to fine-tuning and enhancing domain knowledge progressively. We also introduce DataRater, a model designed to assess data quality during CPT, ensuring that the training data is both accurate and relevant. For SFT, we develope a large and diverse bilingual dataset, along with ConFilter, a metric to enhance multi-turn dialogue quality, which is crucial to improving the model's ability to handle more complex dialogues. The combination of high-quality data sources and innovative techniques significantly improves CareBot's performance across a range of medical applications. Our rigorous evaluations on Chinese and English benchmarks confirm CareBot's effectiveness in medical consultation and education. These advancements not only address current limitations in medical LLMs but also set a new standard for developing effective and reliable open-source models in the medical domain. We will open-source the datasets and models later, contributing valuable resources to the research community.

cs.CL↗