arXiv subjects

Pei Wang

Publications and source records attributed to Pei Wang.

At least 55 records · Page 3Linked to original sources

arXiv2026

A Persistently Active Fast Radio Burst source Embedded in an Expanding Supernova Remnant

Fast radio bursts (FRBs) remain one of the most puzzling astrophysical phenomena. While most FRBs are detected only once or sporadically, we present the identification of FRB 20190520B as the first persistently active source over a continuous span of ~ four years. This rare long-term activity enabled a detailed investigation of its dispersion measure (DM) evolution. We also report that FRB 20190520B exhibits a substantial decrease in DM at a global rate of minus 12.4 plus or minus 0.3 pc cm^-3 yr^-1, exceeding previous FRB DM variation measurements by a factor of three and surpassing those observed in pulsars by orders of magnitude. The magnitude and consistency of the DM evolution, along with a high host DM contribution, strongly indicate that the source resides in a dense, expanding ionized medium, likely a young supernova remnant (SNR).

astro-ph.HE↗

arXiv2026

ShopSimulator: Evaluating and Exploring RL-Driven LLM Agent for Shopping Assistants

Large language model (LLM)-based agents are increasingly deployed in e-commerce shopping. To perform thorough, user-tailored product searches, agents should interpret personal preferences, engage in multi-turn dialogues, and ultimately retrieve and discriminate among highly similar products. However, existing research has yet to provide a unified simulation environment that consistently captures all of these aspects, and always focuses solely on evaluation benchmarks without training support. In this paper, we introduce ShopSimulator, a large-scale and challenging Chinese shopping environment. Leveraging ShopSimulator, we evaluate LLMs across diverse scenarios, finding that even the best-performing models achieve less than 40% full-success rate. Error analysis reveals that agents struggle with deep search and product selection in long trajectories, fail to balance the use of personalization cues, and to effectively engage with users. Further training exploration provides practical guidance for overcoming these weaknesses, with the combination of supervised fine-tuning (SFT) and reinforcement learning (RL) yielding significant performance improvements. Code and data will be released at https://github.com/ShopAgent-Team/ShopSimulator.

cs.AI↗

arXiv2026

Dual-Backend Multibeam Position Switching Targeted SETI Observations toward Nearby Active Planet-Hosting Systems with FAST

The Five-hundred-meter Aperture Spherical Telescope (FAST), the world's largest single-dish radio telescope, lists the search for extraterrestrial intelligence (SETI) as one of its key scientific objectives. In this work, we present a targeted SETI observation for 7 nearby active stars utilizing the FAST L-band multibeam receiver, employing a observational strategy that combines position switching with multibeam tracking to balance on-source integration time with the accuracy of the beam response. Using both pulsar and SETI backends, we perform a comprehensive search for narrowband drifting signals with Doppler drift rates within diversified drift rate ranges and channel-width periodic signal with periods between 0.12 and 100 s and duty cycles between 10% and 50%. No credible radio technosignatures were detected from any of the target systems. Based on this null result, we place constraints on the presence of transmitters at a 95% confidence level, ruling out narrowband transmitters with with EIRP above $3.98\times10^8 \,\mathrm{W}$ and periodic transmitter with EIRP above $1.80\times10^{10} \,\mathrm{W}$,respectively, within the observation band.

astro-ph.IM↗

arXiv2025

Investigating FRB 20240114A with FAST: Morphological Classification and Drifting Rate Measurements in a Burst-Cluster Framework

This study investigates the morphological classification and drifting rate measurement of the repeating fast radio burst (FRB) source FRB 20240114A using the Five-hundred-meter Aperture Spherical Telescope (FAST). Detected on January 14, 2024, FRB 20240114A exhibited an exceptionally high burst rate, revealing unique properties. Through observational campaigns over several months, we selected a dataset comprising 3,203 bursts (2,109 burst-clusters) during a continuous monitoring session (15,780 seconds) on March 12, 2024. Improving upon previous work, we clarify the definitions of sub-bursts, bursts and burst-clusters. Using an average dispersion measures (DM) of 529.2 pc cm$^{-3}$, we classified the burst-clusters into Downward Drifting, Upward Drifting, No Drifting, No Evidence for Drifting, Not-Clear, and Complex burst-clusters. Among the 978 burst-clusters that exhibit drifting behavior, 233 (23.82%) show upward drifting. Additionally, if 142 upward drifting single-component burst-clusters are excluded, upward drifting double- and multi-component burst-clusters still account for 10.89% of the 836 burst-clusters exhibiting drifting behavior, equating to 91 burst-clusters. Furthermore, if only upward drifting burst-clusters with consecutive time intervals (or upward drifting bursts) are considered, only 9 bursts remain. Drifting rate comparisons with other physical quantities reveal that the drifting rate increases with peak frequency for single-component burst-clusters with drifting behavior. Moreover, in single-component burst-clusters, those with upward drifting exhibit smaller effective widths, bandwidths, and fluxes than their downward drifting counterparts. A Kolmogorov-Smirnov test further indicates that upward drifting burst-clusters possess longer consecutive time intervals than downward drifting ones, suggesting distinct underlying physical mechanisms.

astro-ph.HE↗

arXiv2025

Identifying Quasi-Periodic Micropulses in Pulsars with FAST Using Convolutional Neural Networks

Quasi-periodic MicroPulses (QMP) are quasi-periodic microstructural features manifested in individual pulsar radio pulses, the study of which is crucial for understanding pulsar radiation mechanisms. Manual identification of QMP in large-scale pulsar single-pulse datasets remains highly inefficient. To address this, we propose a Dual-Stage Residual Network (DSR) that achieves automated QMP detection in FAST observational data through joint analysis of single-pulse profiles and their Amplitude Distribution Profiles (ADP), defined as the power spectra of the autocorrelation function derivatives of the microstructure residuals. The model was trained on PSR B1933+16 data from 2019 (10,486 single pulses) and evaluated on manually annotated PSR B1933+16 data from 2020 (9,657 single pulses). DSR achieved 96.10\% recall and 95.85\% precision on the test set. This approach provides an automated pipeline for large-scale, reproducible QMP identification and establishes the foundation for in-depth investigation of their physical mechanisms.

astro-ph.HE↗

arXiv2025

FID-Net: A Feature-Enhanced Deep Learning Network for Forest Infestation Detection

Forest pests threaten ecosystem stability, requiring efficient monitoring. To overcome the limitations of traditional methods in large-scale, fine-grained detection, this study focuses on accurately identifying infected trees and analyzing infestation patterns. We propose FID-Net, a deep learning model that detects pest-affected trees from UAV visible-light imagery and enables infestation analysis via three spatial metrics. Based on YOLOv8n, FID-Net introduces a lightweight Feature Enhancement Module (FEM) to extract disease-sensitive cues, an Adaptive Multi-scale Feature Fusion Module (AMFM) to align and fuse dual-branch features (RGB and FEM-enhanced), and an Efficient Channel Attention (ECA) mechanism to enhance discriminative information efficiently. From detection results, we construct a pest situation analysis framework using: (1) Kernel Density Estimation to locate infection hotspots; (2) neighborhood evaluation to assess healthy trees' infection risk; (3) DBSCAN clustering to identify high-density healthy clusters as priority protection zones. Experiments on UAV imagery from 32 forest plots in eastern Tianshan, China, show that FID-Net achieves 86.10% precision, 75.44% recall, 82.29% mAP@0.5, and 64.30% mAP@0.5:0.95, outperforming mainstream YOLO models. Analysis confirms infected trees exhibit clear clustering, supporting targeted forest protection. FID-Net enables accurate tree health discrimination and, combined with spatial metrics, provides reliable data for intelligent pest monitoring, early warning, and precise management.

cs.CV↗

arXiv2025

Statistical and Temporal Analysis of Multi-component Burst-clusters from the Repeating FRB 20190520B

Fast Radio Bursts (FRBs) are bright, millisecond-duration extragalactic radio transients that probe extreme astrophysical environments. Many FRBs exhibit multi-component structures, which encode information about their emission mechanisms or progenitor systems and thus provide important clues to their origins. In this work, we systematically analyze the burst morphology of FRB 20190520B and compare component distributions across four active FRBs observed with FAST: FRB 20121102A, FRB 20190520B, FRB 20201124A, and FRB 20240114A. We find that multi-component burst-clusters show spectral properties similar to single-peak bursts, and no periodicity is detected in their temporal behavior. The component-count distributions follow a power law, revealing scale-free behavior consistent with self-organized criticality (SOC) processes. Multi-component clusters account for 12-30% of all detected bursts, regardless of source activity, providing new insights into burst-to-burst variability and the physical processes driving FRB emission.

astro-ph.HE↗

arXiv2025

Search for Radio Pulsations from Neutron Star Candidates in Detached Binaries

Recent optical astrometric and spectroscopic surveys have identified numerous neutron star (NS) candidates in non-accreting detached binary systems, but their compact-object nature remains unconfirmed. In this work, we present targeted radio observations of 31 such candidates using the Five-hundred-meter Aperture Spherical radio Telescope (FAST), the Robert C. Byrd Green Bank Telescope, and the Shanghai TianMa Radio Telescope. Over a total of 46.65 hours of observing time, we detected neither periodic nor single-pulse radio emissions. These nondetections place stringent upper limits on the flux densities of any potential radio signals, reaching ~4 $μ$Jy for periodic emission and ~10 mJy for single pulses with FAST. Since our observations are highly sensitive and the flux density upper limits are well below the median fluxes of known Galactic pulsars, this suggests that geometric beaming is the most likely explanation for the non-detections if these objects are indeed pulsars. Alternatively, the NSs may be sufficiently old ($\gtrsim$ 10 Gyr) and have become intrinsically radio-quiet. In this case, our findings highlight the inherent difficulty of confirming NSs in such old detached binary systems through radio pulsation searches.

astro-ph.HE↗

arXiv2025

MMDCP: A Distribution-free Approach to Outlier Detection and Classification with Coverage Guarantees and SCW-FDR Control

We propose the Modified Mahalanobis Distance Conformal Prediction (MMDCP), a unified framework for multi-class classification and outlier detection under label shift, where the training and test distributions may differ. In such settings, many existing methods construct nonconformity scores based on empirical cumulative or density functions combined with data-splitting strategies. However, these approaches are often computationally expensive due to their heavy reliance on resampling procedures and tend to produce overly conservative prediction sets with unstable coverage, especially in small samples. To address these challenges, MMDCP combines class-specific distance measures with full conformal prediction to construct a score function, thereby producing adaptive prediction sets that effectively capture both inlier and outlier structures. Under mild regularity conditions, we establish convergence rates for the resulting sets and provide the first theoretical characterization of the gap between oracle and empirical conformal $p$-values, which ensures valid coverage and effective control of the class-wise false discovery rate (CW-FDR). We further introduce the Summarized Class-Wise FDR (SCW-FDR), a novel global error metric aggregating false discoveries across classes, and show that it can be effectively controlled within the MMDCP framework. Extensive simulations and two real-data applications support our theoretical findings and demonstrate the advantages of the proposed method.

stat.ME↗

arXiv2025

Relativistic model of spontaneous wave-function localization induced by nonHermitian colored noise

We develop a quantum field theory based on random nonHermitian actions, which upon quantization lead to stochastic nonlinear Schrödinger dynamics for the state vector. In this framework, Lorentz and spacetime translation symmetries are preserved only in a statistical sense: the probability distribution of the action remains invariant under these transformations. As a result, the theory describes ensembles of quantum-state trajectories whose probability distributions remain invariant under changes of reference frame. As a concrete example, we augment the Dirac action with a purely imaginary term coupling the fermion density operator to a universal colored noise. This noise is constructed by solving the d'Alembert equation with white noise as its source, using a generalized stochastic calculus in 1+3 dimensions. We demonstrate that the colored noise drives stochastic localization of wave packets and derive the localization length analytically. Remarkably, the localization length decreases as the size of the observable universe increases. Our model thus provides a potential framework for relativistic spontaneous wave-function collapse. While establishing consistency with Born's law remains an open challenge, the present work constitutes a step toward embedding collapse models into a Lorentz-invariant quantum field theory.

quant-ph↗

arXiv2025

Branching rules for cell modules on a tower of the partition algebras

Partition algebras with non-zero parameters are cellularly stratified and thus have the features of both cellular algebras and stratified algebras. Also, partition algebras form a tower of algebras. In this paper, we provide a diagrammatic approach to study the branching rules for cell modules on a tower of the partition algebras. This also allows us to calculate the structure constants of the Grothendieck ring of the tower.

math.RT↗

arXiv2025

PCRLLM: Proof-Carrying Reasoning with Large Language Models under Stepwise Logical Constraints

Large Language Models (LLMs) often exhibit limited logical coherence, mapping premises to conclusions without adherence to explicit inference rules. We propose Proof-Carrying Reasoning with LLMs (PCRLLM), a framework that constrains reasoning to single-step inferences while preserving natural language formulations. Each output explicitly specifies premises, rules, and conclusions, thereby enabling verification against a target logic. This mechanism mitigates trustworthiness concerns by supporting chain-level validation even in black-box settings. Moreover, PCRLLM facilitates systematic multi-LLM collaboration, allowing intermediate steps to be compared and integrated under formal rules. Finally, we introduce a benchmark schema for generating large-scale step-level reasoning data, combining natural language expressiveness with formal rigor.

cs.CL↗

arXiv2025

Half-Diagrams in Partition Algebras: A Geometric Perspective on Multiplicities

This paper studies the restriction multiplicities of half-diagram modules for the partition algebra and their geometric interpretations. By specializing the Bowman-De Visscher-Orellana formula [BVC, Theorem 4.3] for restriction multiplicities of standard modules in the partition algebra, we compute these multiplicities and provide interpretations in terms of planar triangles and conic sections. Additionally, through the decomposition of half-diagrams, we explain the intrinsic reasons underlying this connection between geometry and algebra.

math.RT↗

arXiv2025

A comprehensive search for Long and Short Periodic Features from an Extremely Active Cycle of FRB 20240114A

Possible periodic features in fast radio bursts (FRBs) may provide insights into their astrophysical origins. Using extensive observations from the Five-hundred-meter Aperture Spherical radio Telescope (FAST), we conduct a multi-timescale periodicity search for the exceptionally active repeater FRB~20240114A. Our analysis is based on different datasets for different timescales: for short-timescale periodicity in Time of Arrivals (TOAs), we use 57 observations from January to August 2024; for long-timescale periodicity, we employ an extended TOA dataset comprising 111 observations spanning from January 2024 to October 2025; and for burst time series analysis, we utilize individual burst data from the 57 FAST observations. We identify three candidate short-timescale periodic signals (0.673~s, 0.635~s, and 0.536~s) with significances of $3.2σ$--$6σ$, each detected in two independent observations. On longer timescales, we detect a significant $143.40\pm7.19$-day periodicity with $5.2σ$ significance, establishing FRB~20240114A as a periodic repeater. In burst time series, we find quasi-periodic oscillations in the few hundred Hz range ($3.4σ$ and $3.7σ$) and periodic burst trains with periods of several to tens of milliseconds ($3σ$--$3.9σ$), though these periodic features appear transient and short-lived. The detection of periodic signals at these different time scales indicates that FRB 20240114A exhibits intriguing periodic self-similar characteristics. Despite the comprehensive dataset, no definitive periodicity linked to the source's rotation is confirmed, placing stringent constraints on the intrinsic source properties and the modulation mechanisms. All data are available via the Science Data Bank.

astro-ph.HE↗

arXiv2025

ASTROFLOW: A Real-Time End-to-End Pipeline for Radio Single-Pulse Searches

Fast radio bursts (FRBs) are extremely bright, millisecond duration cosmic transients of unknown origin. The growing number of wide-field and high-time-resolution radio surveys, particularly with next-generation facilities such as the SKA and MeerKAT, will dramatically increase FRB discovery rates, but also produce data volumes that overwhelm conventional search pipelines. Real-time detection thus demands software that is both algorithmically robust and computationally efficient. We present Astroflow, an end-to-end, GPU-accelerated pipeline for single-pulse detection in radio time-frequency data. Built on a unified C++/CUDA core with a Python interface, Astroflow integrates RFI excision, incoherent dedispersion, dynamic-spectrum tiling, and a YOLO-based deep detector. Through vectorized memory access, shared-memory tiling, and OpenMP parallelism, it achieves 10x faster-than-real-time processing on consumer GPUs for a typical 150 s, 2048-channel observation, while preserving high sensitivity across a wide range of pulse widths and dispersion measures. These results establish the feasibility of a fully integrated, GPU-accelerated single-pulse search stack, capable of scaling to the data volumes expected from upcoming large-scale surveys. Astroflow offers a reusable and deployable solution for real-time transient discovery, and provides a framework that can be continuously refined with new data and models.

astro-ph.IM↗

arXiv2025

Multi-Faceted Emission Properties of PSR J2129+4119 Observed with FAST

We present a detailed single-pulse study of the long-period pulsar PSR J2129+4119 using high-sensitivity FAST observations. Despite locating well below the traditional death line, the pulsar exhibits sustained and multi-modal emission behavior, including nulls, weak pulses, regular emission, and occasional bright pulses. The nulling fraction is measured to be $8.13\% \pm 0.51\%$, with null durations typically under four pulse periods. Fluctuation spectral analysis reveals both phase-modulated subpulse drifting and intermittent beat-like modulation. At the same time, polarization profiles show high linear polarization and stable polarization position angle (PPA) swings consistent with a near-tangential sightline geometry. Quasi-periodic microstructures are detected in 11.54\% of regular pulses, with a mean periodicity and width of 4.57 ms and 4.30 ms, respectively. A well-defined scintillation arc in the secondary spectrum confirms the presence of a localized scattering screen. These results indicate that PSR J2129+4119 remains magnetospherically active and coherently emitting despite its low energy loss rate, offering key insights into pulsar emission physics near the death line.

astro-ph.HE↗

arXiv2025

Revealing the Temporally Stable Bimodal Energy Distribution of FRB 20121102A with a Tripled Burst Set from AI Detections

Active repeating Fast Radio Bursts (FRBs), with their large number of bursts, burst energy distribution, and their potential energy evolution, offer critical insights into the FRBs emission mechanisms. Traditional pipelines search for bursts through conducting dedispersion trials and looking for signals above certain fluence thresholds, both of which could result in missing weak and narrow-band bursts. In order to improve the completeness of the burst set, we develop an End-to-end DedispersE-agnostic Nonparametric AI model (EDEN), which directly detect bursts from dynamic spectrum and is the first detection pipeline that operates without attempting dedispersion. We apply EDEN to archival FAST L-band observations during the extreme active phase of the repeating source FRB 20121102A, resulting in the largest burst set for any FRB to date, which contains 5,927 individual bursts, tripling the original burst set. The much enhanced completeness enables a refined analysis of the temporal behavior of energy distribution, revealing that the bimodal energy distribution remains stable over time. It is rather an intrinsic feature of the emission mechanisms than a consequence of co-evolving with burst rate.

astro-ph.HE↗

arXiv2025

DanceText: A Training-Free Layered Framework for Controllable Multilingual Text Transformation in Images

We present DanceText, a training-free framework for multilingual text editing in images, designed to support complex geometric transformations and achieve seamless foreground-background integration. While diffusion-based generative models have shown promise in text-guided image synthesis, they often lack controllability and fail to preserve layout consistency under non-trivial manipulations such as rotation, translation, scaling, and warping. To address these limitations, DanceText introduces a layered editing strategy that separates text from the background, allowing geometric transformations to be performed in a modular and controllable manner. A depth-aware module is further proposed to align appearance and perspective between the transformed text and the reconstructed background, enhancing photorealism and spatial consistency. Importantly, DanceText adopts a fully training-free design by integrating pretrained modules, allowing flexible deployment without task-specific fine-tuning. Extensive experiments on the AnyWord-3M benchmark demonstrate that our method achieves superior performance in visual quality, especially under large-scale and complex transformation scenarios. Code is avaible at https://github.com/YuZhenyuLindy/DanceText.git.

cs.CV↗