arXiv subjects

Yian Yu

Publications and source records attributed to Yian Yu.

8 recordsLinked to original sources

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

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

Stable Multivariate Functional Time Series Prediction for Major Geomagnetic Indices

High\text{--}resolution scientific data, such as geomagnetic index streams, often exhibit complex temporal dependencies that can be modeled through functional data analysis. Conventional functional time series (FTS) methods typically partition continuous processes into non-overlapping segments, which artificially fragments temporal continuity and can limit estimation efficiency and stability. This is particularly evident in geomagnetic time series prediction due to their noisy, sudden, and large\text{--}scale changes. This study presents a robust multivariate FTS forecasting framework for multi\text{--}dimensional time series with inter\text{--}series correlations and the existence of exogenous predictors. We introduce an overlapping rolling\text{--}window scheme that preserves temporal coherence and mitigates boundary information loss, thereby enriching the effective sample size for a more efficient and stable estimation. We integrate functional principal component analysis for dimension reduction with a vector autoregressive model with exogenous inputs to capture latent dynamics across correlated series. We also construct computationally efficient conformal prediction intervals for uncertainty quantification. The framework is motivated by and applied to the simultaneous forecasting of five critical geomagnetic indices, Kp, Dst, SYM\text{--}H, SME, and SMR, using solar wind parameters as predictors. Empirical results show that this approach outperforms state\text{--}of\text{--}the\text{--}art machine learning baselines, extends forecast horizons to 6\text{--}24 hours, and provides calibrated uncertainty bounds.

stat.AP↗

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

Highly-Parallel Atom-Detection Accelerator for Tweezer-Based Neutral Atom Quantum Computers

Neutral atom quantum computers (NAQCs) are among the most promising computational platforms for quantum computing. Controlling and measuring individual atoms and their states, which often requires multiple imaging and image-analysis procedures, is typically the most time-consuming task during computation and contributes significantly to overall cycle times. To resolve this challenge, we propose a highly-parallel atom-detection accelerator for tweezer-based NAQCs. Our design builds on an existing state-reconstruction method and combines an algorithm-level optimization with a Field Programmable Gate Array (FPGA) implementation to maximize parallelism and reduce the run time of the image-analysis process. We identify and overcome several challenges for an FPGA implementation, such as introducing a prefetching mechanism to improve scalability and customizing bus transfers to support large bandwidths. Tested on a Xilinx UltraScale+ FPGA, our design can analyze a 256x256-pixel fluorescence image in just 115mus, achieving 34.9x and 6.3x speedups over the original and optimized CPU baseline, respectively. Moreover, our accelerator can maintain consistent resource utilization across various atom array sizes, contributing to the ongoing efforts toward scalable and fully integrated FPGA-based control systems for NAQCs.

quant-ph↗

arXiv2026

Efficient Image Reconstruction Architecture for Neutral Atom Quantum Computing

In recent years, neutral atom quantum computers (NAQCs) have attracted a lot of attention, primarily due to their long coherence times and good scalability. One of their main drawbacks is their comparatively time-consuming control overhead, with one of the main contributing procedures being the detection of individual atoms and measurement of their states, each occurring at least once per compute cycle and requiring fluorescence imaging and subsequent image analysis. To reduce the required time budget, we propose a highly-parallel atom-detection accelerator for tweezer-based NAQCs. Building on an existing solution, our design combines algorithm-level optimization with a field-programmable gate array (FPGA) implementation to maximize parallelism and reduce the run time of the image analysis process. Our design can analyze a 256$\times$256-pixel image representing a 10$\times$10 atom array in just 115 $\mu$s on a Xilinx UltraScale+ FPGA. Compared to the original CPU baseline and our optimized CPU version, we achieve about 34.9$\times$ and 6.3$\times$ speedup of the reconstruction time, respectively. Moreover, this work also contributes to the ongoing efforts toward fully integrated FPGA-based control systems for NAQCs.

quant-ph↗

arXiv2025

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↗

arXiv2025

Analyzing Functional Data with a Mixture of Covariance Structures Using a Curve-Based Sampling Scheme

Motivated by distinct walking patterns in real-world free-living gait data, this paper proposes an innovative curve-based sampling scheme for the analysis of functional data characterized by a mixture of covariance structures. Traditional approaches often fail to adequately capture inherent complexities arising from heterogeneous covariance patterns across distinct subsets of the data. We introduce a unified Bayesian framework that integrates a nonlinear regression function with a continuous-time hidden Markov model, enabling the identification and utilization of varying covariance structures. One of the key contributions is the development of a computationally efficient curve-based sampling scheme for hidden state estimation, addressing the sampling complexities associated with high-dimensional, conditionally dependent data. This paper details the Bayesian inference procedure, examines the asymptotic properties to ensure the structural consistency of the model, and demonstrates its effectiveness through simulated and real-world examples.

stat.ME↗