arXiv subjects

Jianghua Wu

Publications and source records attributed to Jianghua Wu.

At least 19 recordsLinked to original sources

arXiv2026

From Variability to SED Modeling: A Multiwavelength Study of the Neutrino Blazar TXS 0506+056

The blazar TXS 0506+056 is the first source that was reported to be associated with high-energy extragalactic neutrino events and is one of the major targets for multi-messenger studies. We carried out multi-wavelength optical monitoring of this object on 24 nights in the period from 2018 to 2023. The overall light curves exhibit a dimming trend superposed by some small-amplitude fluctuations, and intraday variability was detected on four nights. Bluer-when-brighter behaviors were observed on both intraday and long timescales and were more pronounced on long timescales, while a weak redder-when-brighter trend was detected on one night. No significant time lags were found between variations at different optical wavelengths. We also retrieved the multi-broadband data from some monitoring programs. The data reveal complex, asynchronous flaring in different wavebands. A cross-correlation analysis shows that the high-energy emission (optical to gamma-ray) is co-spatial and leads the radio emission by a substantial time of about 800 to 900 days, suggesting that the radio emission originates from a downstream region of the jet. We performed time-dependent lepto-hadronic modeling of the spectral energy distributions for three representative epochs, the 2017 neutrino-associated flare, a post-flare phase, and a deep quiescent state, revealing an evolution in the radiative properties of the emission regions. The modeling results provide a phenomenological framework for interpreting the long-term multiwavelength behavior of TXS 0506+056 in a multi-messenger context.

astro-ph.HE↗

arXiv2026

Search for quasar pairs with Gaia astrometric data IV. Confirmation of 17 dual quasars and 143 projected quasars

Dual quasars separated at the kiloparsec scale are widely regarded as precursors to binary supermassive black holes and offer a key insight into the dynamical evolution of galaxy mergers. Our series of studies focuses on searching for dual quasars by using a selection strategy of zero proper motion and zero parallax to isolate candidate quasars near known ones and by follow-up spectroscopy of the candidates. This paper, the fourth in the series, reports the spectroscopic confirmations of our quasar pair candidates based on the spectroscopic data of the SDSS and DESI DR1. We newly identified 17 dual quasars and 143 projected quasars. The redshifts of the 17 dual quasars range from 0.573 to 2.758, with a median of 1.512. One notable system, J0023+0417, exhibits nearly identical spectral features in the two members and shows evidence of a potential foreground galaxy, making it a high-confidence strong gravitational lensing system. The redshifts of the 143 projected quasars are from 0.301 to 4.030, with a median of 1.596. Among them, four have projected distances below 30 kpc, offering valuable opportunities to probe the circumgalactic medium (CGM) of the foreground host galaxy through absorption lines.

astro-ph.GA↗

arXiv2026

Search for Quasar Pairs with ${\it Gaia}$ Astrometric Data. III. Discovery of 9 dual and projected quasars

We report the low-resolution long-slit spectroscopic observations and confirmations of 11 quasar pair candidates, which are selected from the MGQPC catalog presented in the first paper of our series work (hereafter, Paper-I) and the early version of this catalog. The spectroscopic follow-up was carried out with 5 spectrographs equipped on 3 telescopes, and the major discoveries include 6 dual quasars and 3 projected quasars. One of the dual quasars has a high redshift of $\sim$ 3.1. The LQ hypothesis of 3 dual quasars cannot be completely ruled out. We investigated the reason why previous spectroscopic surveys missed several new quasars. We discussed a projected quasar with a wide-separation lensing configuration, as well as two quasar-star projections that mimic the configuration of lensed quasars. The photometric redshifts for the 11 observed candidates were extracted from the second paper of our series work (hereafter, Paper-II) to illustrate their positive role in mitigating contamination from projected quasars and quasar-star projections. We also reviewed and discussed the confirmation strategies for dual and lensed quasar candidates, and outlined future confirmation strategies for them in the context of the era dominated by large-scale spectroscopic and imaging surveys.

astro-ph.GA↗

arXiv2026

Search for quasar pairs with Gaia astrometric data. II. Photometric redshift prediction with machine learning for the MGQPC catalogue

The identification of physically associated kiloparsec-scale quasar pairs is important for understanding galaxy evolution, the growth of supermassive black holes, and their co-evolution with host galaxies. However, their rarity and the high contamination from stellar superpositions and projected alignments require efficient pre-selection methods. We develop a machine-learning framework to produce photometric-redshift point estimates and redshift probability density functions for quasars, with the main goal of identifying high-probability quasar pair candidates in the MGQPC catalogue. We construct two large spectroscopically confirmed quasar samples with multi-wavelength photometry, based on SDSS and DESI Legacy Imaging Surveys data. CatBoost is used for point-estimate photometric-redshift regression, and FlexZBoost is used for full redshift-PDF estimation. The workflow achieves robust performance, with a normalised median absolute deviation of 0.036 and an outlier fraction of 5.6% on the test sample. Applying the trained model to the MGQPC catalogue, we identify 185 high-probability quasar pair candidates based on photometric-redshift consistency. Among them, 20 systems have been subsequently confirmed as genuine physical pairs by independent spectroscopic observations. The resulting MGQPC photometric-redshift catalogue provides a useful resource for future spectroscopic follow-up of quasar pairs and dual supermassive black holes.

astro-ph.GA↗

arXiv2026

Smoothing Binary Optimization: A Primal-Dual Perspective

Binary optimization is a powerful tool for modeling combinatorial problems, yet scalable and theoretically sound solution methods remain elusive. Conventional solvers often rely on heuristic strategies with weak guarantees or struggle with large-scale instances. In this work, we introduce a novel primal-dual framework that reformulates unconstrained binary optimization as a continuous minimax problem, satisfying a strong max-min property. This reformulation effectively smooths the discrete problem, enabling the application of efficient gradient-based methods. We propose a simultaneous gradient descent-ascent algorithm that is highly parallelizable on GPUs and provably converges to a near-optimal solution in linear time. Extensive experiments on large-scale problems--including Max-Cut, MaxSAT, and Maximum Independent Set with up to 50,000 variables--demonstrate that our method identifies high-quality solutions within seconds, significantly outperforming state-of-the-art alternatives.

math.OC↗

arXiv2026

Promoting Fair Online Resource Allocation with Indivisible Units

Allocating scarce, indivisible resources to diverse groups under uncertainty is a central challenge in operations research, where efficiency-focused methods often underserve marginalized populations. We study the Fair Online Resource Allocation with Indivisible Units (FORA-IU) problem, in which an unpredictable sequence of demands must be served from a strictly fixed inventory, and ask what fairness guarantees are achievable under different distributional and structural assumptions. We adopt a fairness criterion based on the expected filling ratio (FE-FR-beta), which balances each group's expected allocation against its expected demand and priority weight. We design online policies that calibrate acceptance probabilities to the remaining budget, analyze both arbitrary time-varying and stationary arrivals, introduce the Random Cyclic Blocks (RCB) algorithm tailored to the stationary case, and study the effect of restricting policies to all-or-nothing allocations. For arbitrary time-varying arrivals, our policy achieves the optimal universal fairness guarantee of 1/(1+R_beta), where R_beta denotes the priority-weighted system load. For time-invariant arrivals, RCB achieves the exact finite-horizon guarantee [1-(1-R_beta/T)^T]/R_beta, which is at least (1-e^{-R_beta})/R_beta and is also tight. We further show that all-or-nothing allocation policies cannot match these guarantees. These findings demonstrate that distributional stationarity strictly improves the fairness frontier, and that partial fulfillment is a necessary condition for attaining optimal fairness in online indivisible resource allocation.

math.OC↗

arXiv2026

Search for Quasar Pairs with ${\it Gaia}$ Astrometric Data. I. Method and Candidates

Quasar pairs, a special subclass of galaxy pairs, are valuable in the investigation of quasar interaction, clustering, co-evolution between the two quasars' host galaxies, the growth of supermassive black holes, as well as the formation and evolution of galaxies. However, quasar pairs on kpc scales are observationally rare. The scarcity of available samples hindered the deeper exploration and statistics of these objects. In this work, we apply an astrometric method to systematically search for quasar candidates within a transverse distance of 100 kpc to known quasars in the Million Quasar Catalog. These candidates are \textit{Gaia} sources with proper motions and parallaxes that are consistent with zero. Visual inspection of the sample was performed to remove the contamination of crowded stellar fields and nearby galaxies. A total of 4\,112 quasar pair candidates were isolated, with a median member separation of 8.81{\arcsec}, a median \textit{Gaia} $G$-band magnitude of 20.52, and a median redshift of 1.61. Our catalog was compared with three major candidate quasar pair catalogs and identified 3\,984 new quasar pair candidates previously uncataloged in the three catalogs. Several interesting quasar pair candidates are highlighted and discussed. We also briefly discussed our quasar selection and several techniques for improving the success rate of quasar pair selection. Extensive spectroscopic follow-up campaigns are being carried out to validate their astrophysical nature.

astro-ph.GA↗

arXiv2026

Multiple Components and Spectral Evolution of BL Lacertae as Revealed by Multiwavelength Variability and SED Modeling

BL Lac has entered an active state since 2020, with multiwavelength observations revealing intense flares. In this study, we conducted 12-night multicolor optical monitoring using an 85 cm telescope from 2020 September to 2024 June and collected long-term broad-band archived data from radio to $γ$-rays. Intraday variabilities were detected on four nights, and most of them exhibited a bluer-when-brighter behavior. Both clockwise and counterclockwise spectral hysteresis loops were found within a single night. However, no reliable intraband time lag was detected for the intranight variabilities. On long timescales, the cross-correlation analysis shows that the variations of the optical, X-ray, and $γ$-ray bands do not reveal an obvious time delay, while the variations in the radio bands lagged them by about 370 days. The measured time lags suggest two distinct emission regions respectively responsible for the optical to $γ$-ray radiation and for the radio radiation, with a spatial separation of approximately $4.50\times10^{19}\ \rm cm$. We modeled the broad-band spectral energy distributions during four flaring epochs and one quiescent epoch, and found evidence for the possible persistent existence of a very high energy emission region. We also confirmed a spectral evolution of the source from an intermediate synchrotron peaked BL Lac object to a low synchrotron peaked BL Lac object.

astro-ph.HE↗

arXiv2026

A Quasar Pair Sample Compiled from DESI DR1

Interacting quasar pairs (QPs, either dual or binary) provide crucial insights into galaxy mergers, black hole growth, and large-scale structure formation. The current literature reports fewer than 200 spectroscopically confirmed QPs, highlighting the need for larger samples to enable statistically meaningful investigations. In this paper, we present a sample of 1,220 quasar pairs or candidates compiled from the DESI DR1 quasar sample. Among them, 145 systems have been previously reported. We visually classified the full sample using DESI Legacy Images and SPARCL spectra into three categories: QP (quasar pairs, N = 1020), QPC (quasar pair candidates, N = 142), LQC (lensed quasar candidates, N = 58). Within the LQC subset, we find an intriguing wide-separation ($\sim$7.15$^{\prime\prime}$) quadruply lensed quasar candidate. The redshift distribution of pair sample peaks at $z \sim 1$--$2.5$, with an overall pair fraction of $6.2^{+0.2}_{-0.2}\times10^{-4}$ (Poisson error) and a generally weak redshift dependence. 63.8\% of QPs have $|ΔV_r| < 600$\,km/s, suggesting dynamical associations. This sample offers a statistically meaningful dataset for future studies of quasar pairs, lensing events, and potentially merger-triggered or merger-induced SMBH growth across the merger sequence.

astro-ph.GA↗

arXiv2026

Dual AGNs on 100 kpc Scales from the Million Quasar Catalog

Research on dual active galactic nuclei (AGNs) is crucial for understanding the coevolution of galaxies and supermassive black holes. However, the current number of dual AGNs remains scarce. In this work, we selected 173 new dual AGNs, 4 AGN triplets, and 1 AGN quadruplet from the Million Quasars Catalog, all with low redshift ($z < 0.5$), a projected distance ($r_p$) of no more than 100 kpc, and a line-of-sight velocity difference ($|Δv|$) of less than 600 km s$^{-1}$, thus supplementing existing low-redshift dual AGNs demographics. Visual inspection of the optical images from the Dark Energy Spectroscopic Instrument Legacy Survey was performed for each pair, revealing that $\sim$16\% of pairs exhibit tidal features. Statistical analyses show an increasing number of dual AGNs with decreasing redshift, with velocity difference primarily at $|Δv| < $ 300 km s$^{-1}$, which is likely an artifact of our selection strategy. The tidal sample peaks as having 13 pairs at 5-20$h^{-1}_{70}$ kpc, but drops to 1 pair $> 55\,h^{-1}_{70}$ kpc. Our study also explores thewide separation ($r_p>10$ kpc) dual AGNs, finding 165 such systems, with 25 displaying clear tidal features. Furthermore, some extra galaxies, AGNs, and/or their candidates were found in the same regions of the pairs or multiplets forming interacting systems with these pairs or multiplets.

astro-ph.GA↗

arXiv2025

FCOC: A Fractal-Chaotic Co-driven Framework for Financial Volatility Forecasting

This paper introduces the Fractal-Chaotic Oscillation Co-driven (FCOC) framework, a novel paradigm for financial volatility forecasting that systematically resolves the dual challenges of feature fidelity and model responsiveness. FCOC synergizes two core innovations: our novel Fractal Feature Corrector (FFC), engineered to extract high-fidelity fractal signals, and a bio-inspired Chaotic Oscillation Component (COC) that replaces static activations with a dynamic processing system. Empirically validated on the S\&P 500 and DJI, the FCOC framework demonstrates profound and generalizable impact. The framework fundamentally transforms the performance of previously underperforming architectures, such as the Transformer, while achieving substantial improvements in key risk-sensitive metrics for state-of-the-art models like Mamba. These results establish a powerful co-driven approach, where models are guided by superior theoretical features and powered by dynamic internal processors, setting a new benchmark for risk-aware forecasting.

q-fin.RM↗

arXiv2025

A Hybrid Autoencoder-Transformer Model for Robust Day-Ahead Electricity Price Forecasting under Extreme Conditions

Accurate day-ahead electricity price forecasting (DAEPF) is critical for the efficient operation of power systems, but extreme condition and market anomalies pose significant challenges to existing forecasting methods. To overcome these challenges, this paper proposes a novel hybrid deep learning framework that integrates a Distilled Attention Transformer (DAT) model and an Autoencoder Self-regression Model (ASM). The DAT leverages a self-attention mechanism to dynamically assign higher weights to critical segments of historical data, effectively capturing both long-term trends and short-term fluctuations. Concurrently, the ASM employs unsupervised learning to detect and isolate anomalous patterns induced by extreme conditions, such as heavy rain, heat waves, or human festivals. Experiments on datasets sampled from California and Shandong Province demonstrate that our framework significantly outperforms state-of-the-art methods in prediction accuracy, robustness, and computational efficiency. Our framework thus holds promise for enhancing grid resilience and optimizing market operations in future power systems.

cs.LG↗

arXiv2025

COTN: A Chaotic Oscillatory Transformer Network for Complex Volatile Systems under Extreme Conditions

Accurate prediction of financial and electricity markets, especially under extreme conditions, remains a significant challenge due to their intrinsic nonlinearity, rapid fluctuations, and chaotic patterns. To address these limitations, we propose the Chaotic Oscillatory Transformer Network (COTN). COTN innovatively combines a Transformer architecture with a novel Lee Oscillator activation function, processed through Max-over-Time pooling and a lambda-gating mechanism. This design is specifically tailored to effectively capture chaotic dynamics and improve responsiveness during periods of heightened volatility, where conventional activation functions (e.g., ReLU, GELU) tend to saturate. Furthermore, COTN incorporates an Autoencoder Self-Regressive (ASR) module to detect and isolate abnormal market patterns, such as sudden price spikes or crashes, thereby preventing corruption of the core prediction process and enhancing robustness. Extensive experiments across electricity spot markets and financial markets demonstrate the practical applicability and resilience of COTN. Our approach outperforms state-of-the-art deep learning models like Informer by up to 17% and traditional statistical methods like GARCH by as much as 40%. These results underscore COTN's effectiveness in navigating real-world market uncertainty and complexity, offering a powerful tool for forecasting highly volatile systems under duress.

cs.LG↗

arXiv2025

Successive Fixing for Large-Scale SCUC Using First-Order Methods

Security-Constrained Unit Commitment is a fundamental optimization problem in power systems operations. The primary computational bottleneck arises from the need to solve large-scale Linear Programming (LP) relaxations within branch-and-cut. Conventional simplex and barrier methods become computationally prohibitive at this scale due to their reliance on expensive matrix factorizations. While matrix-free first-order methods present a promising alternative, their tendency to converge to non-vertex solutions renders them incompatible with standard branch-and-cut procedures. To bridge this gap, we propose a successive fixing framework that leverages a customized GPU-accelerated first-order LP solver to guide a logic-driven variable-fixing strategy. Each iteration produces a reduced Mixed-Integer Linear Programming (MILP) problem, which is subsequently tightened via presolving. This iterative cycle of relaxation, fixing, and presolving progressively reduces problem complexity, producing a highly tractable final MILP model. When evaluated on public benchmarks exceeding 13,000 buses, our approach achieves a tenfold speedup over state-of-the-art methods without compromising solution quality.

math.OC↗

arXiv2025

Relax-and-Cut for Temporal SCUC Decomposition

The Security-Constrained Unit Commitment (SCUC) problem presents formidable computational challenges due to its combinatorial complexity, large-scale network dimensions, and numerous security constraints. While conventional temporal decomposition methods achieve computational tractability through fixed short-term time windows, this limited look-ahead capability often results in suboptimal, myopic solutions. We propose an innovative relax-and-cut framework that alleviates these limitations through two key innovations. First, our enhanced temporal decomposition strategy maintains integer variables for immediate unit commitment decisions while relaxing integrality constraints for future time periods, thereby extending the optimization horizon without compromising tractability. Second, we develop a dynamic cutting-plane mechanism that selectively incorporates N-1 contingency constraints during the branch-and-cut process, avoiding the computational burden of complete upfront enumeration. The framework optionally employs a Relaxation-Induced Neighborhood Search procedure for additional solution refinement when computational resources permit. Comprehensive numerical experiments demonstrate the effectiveness of our approach on large-scale systems up to 13,000 buses. The proposed method can achieve optimality gaps below 1% while requiring only 20% of the computation time of monolithic Gurobi solutions. Compared to existing decomposition approaches, our framework provides superior performance, simultaneously reducing primal gaps by 60% and doubling solution speed. These significant improvements make our method particularly well-suited for practical SCUC implementations where both solution quality and computational efficiency are crucial.

math.OC↗

arXiv2025

When GNNs meet symmetry in ILPs: an orbit-based feature augmentation approach

A common characteristic in integer linear programs (ILPs) is symmetry, allowing variables to be permuted without altering the underlying problem structure. Recently, GNNs have emerged as a promising approach for solving ILPs. However, a significant challenge arises when applying GNNs to ILPs with symmetry: classic GNN architectures struggle to differentiate between symmetric variables, which limits their predictive accuracy. In this work, we investigate the properties of permutation equivariance and invariance in GNNs, particularly in relation to the inherent symmetry of ILP formulations. We reveal that the interaction between these two factors contributes to the difficulty of distinguishing between symmetric variables. To address this challenge, we explore the potential of feature augmentation and propose several guiding principles for constructing augmented features. Building on these principles, we develop an orbit-based augmentation scheme that first groups symmetric variables and then samples augmented features for each group from a discrete uniform distribution. Empirical results demonstrate that our proposed approach significantly enhances both training efficiency and predictive performance.

cs.LG↗

arXiv2024

An Efficient Unsupervised Framework for Convex Quadratic Programs via Deep Unrolling

Quadratic programs (QPs) arise in various domains such as machine learning, finance, and control. Recently, learning-enhanced primal-dual hybrid gradient (PDHG) methods have shown great potential in addressing large-scale linear programs; however, this approach has not been extended to QPs. In this work, we focus on unrolling "PDQP", a PDHG algorithm specialized for convex QPs. Specifically, we propose a neural network model called "PDQP-net" to learn optimal QP solutions. Theoretically, we demonstrate that a PDQP-net of polynomial size can align with the PDQP algorithm, returning optimal primal-dual solution pairs. We propose an unsupervised method that incorporates KKT conditions into the loss function. Unlike the standard learning-to-optimize framework that requires optimization solutions generated by solvers, our unsupervised method adjusts the network weights directly from the evaluation of the primal-dual gap. This method has two benefits over supervised learning: first, it helps generate better primal-dual gap since the primal-dual gap is in the objective function; second, it does not require solvers. We show that PDQP-net trained in this unsupervised manner can effectively approximate optimal QP solutions. Extensive numerical experiments confirm our findings, indicating that using PDQP-net predictions to warm-start PDQP can achieve up to 45% acceleration on QP instances. Moreover, it achieves 14% to 31% acceleration on out-of-distribution instances.

math.OC↗

arXiv2024

Invar-RAG: Invariant LLM-aligned Retrieval for Better Generation

Retrieval-augmented generation (RAG) has shown impressive capability in providing reliable answer predictions and addressing hallucination problems. A typical RAG implementation uses powerful retrieval models to extract external information and large language models (LLMs) to generate answers. In contrast, recent LLM-based retrieval has gained attention for its substantial improvements in information retrieval (IR) due to the LLMs' semantic understanding capability. However, directly applying LLM to RAG systems presents challenges. This may cause feature locality problems as massive parametric knowledge can hinder effective usage of global information across the corpus; for example, an LLM-based retriever often inputs document summaries instead of full documents. Moreover, various pre-trained tasks in LLMs introduce variance, further weakening performance as a retriever. To address these issues, we propose a novel two-stage fine-tuning architecture called Invar-RAG. In the retrieval stage, an LLM-based retriever is constructed by integrating LoRA-based representation learning to tackle feature locality issues. To enhance retrieval performance, we develop two patterns (invariant and variant patterns) and an invariance loss to reduce LLM variance. In the generation stage, a refined fine-tuning method is employed to improve LLM accuracy in generating answers based on retrieved information. Experimental results show that Invar-RAG significantly outperforms existing baselines across three open-domain question answering (ODQA) datasets. Code is available in the Supplementary Material for reproducibility.

cs.IR↗