arXiv subjects

Zhen Wan

Publications and source records attributed to Zhen Wan.

At least 19 recordsLinked to original sources

arXiv2026

WFST Follow-up of S251112cm: Searching for an Optical Counterpart to a Subsolar-mass Compact-binary Merger Candidate

Electromagnetic counterparts to gravitational-wave (GW) sources probe the properties, environments, and evolution of compact-object mergers. S251112cm belongs to an emerging class of GW candidates with possible subsolar-mass components. We present an optical counterpart search for S251112cm with the Wide Field Survey Telescope (WFST). The observations began 20.2 hr after the GW trigger and continued for three nights, covering approximately $780~\mathrm{deg}^{2}$ and $51\%$ of the localization probability in the updated skymap. We searched for newly emerging, rapidly evolving, off-nuclear optical transients and identified four candidates whose host-galaxy distances are broadly consistent with the GW distance estimate. However, all four evolve substantially more slowly than AT 2017gfo, ruling out an AT 2017gfo-like origin and disfavoring their association with S251112cm as rapidly evolving kilonova counterparts. Combining WFST and DECam observations increases the covered localization probability to approximately 67%. Conditional on the counterpart lying within this footprint, we constrain a grid of binary-neutron-star kilonova models. For viewing angles $θ_{\rm obs}<60^{\circ}$, with the other parameters fixed to the best-fitting values for AT 2017gfo, models with dynamical ejecta mass $M_{\rm dyn}\gtrsim0.01\,M_{\odot}$ or wind ejecta mass $M_{\rm wind}\gtrsim0.05\,M_{\odot}$ are disfavored over most of the covered GW probability. Beyond kilonova models, our phenomenological analysis constrains rapidly evolving optical transients with $T_{\rm rise,1/2}\leq2$ days to peak absolute magnitudes fainter than approximately -13 mag. To our knowledge, the coordinated WFST and DECam observations provide the strongest optical constraints to date on possible rapidly evolving counterparts to this new class of GW candidates involving subsolar-mass compact objects.

astro-ph.HE↗

arXiv2026

Search for L4 Earth Trojan asteroids with the 2.5-meter Wide Field Survey Telescope

Earth Trojan asteroids (ETAs) are a mysterious population, and dynamically stable ETAs, if primordial, could be "living fossils" of the early solar system. To date, there are only two known ETAs, but both are temporary ETAs. The aim of our survey is to discover new temporary or stable ETAs; in the absence of detections, we derive upper limits on the population of stable ETAs. We conducted the largest wide-area survey of the Earth's L4 Lagrange point region so far using the Wide Field Survey Telescope, covering about 236.74 deg^2, corresponding to 33.24% of the probability coverage for sky regions where dynamically stable L4 ETAs are likely to reside. No new ETAs were detected in our survey. We place a cumulative upper limit of N(H < 19.1) < 19 on the stable population of objects larger than ~520 m (for an assumed albedo of 0.15). This represents the most stringent constraint on the ETA population to date.

astro-ph.EP↗

arXiv2026

DeSRPA: Decoupled Speech Role-Playing Agent via Inference-Time Intervention

While Large Language Models (LLMs) have revolutionized text-based role-playing, creating immersive Speech Role-Playing Agents (SRPAs) requires a seamless bridge between cognitive reasoning and paralinguistic nuances. Current SRPAs primarily rely on end-to-end (E2E) fine-tuning. However, this paradigm suffers from poor generalization to unseen characters due to its reliance on role-specific data, while imposing a "modality alignment tax" that degrades intrinsic LLM reasoning capabilities. We propose DeSRPA, an agentic framework for character role play via inference-time intervention on frozen backbones. DeSRPA employs a dual-level control vector mechanism, Internal Cognitive Steering and External Expressive Rendering, to synchronize "mind" and "voice". Experiments on SpeechRole and OmniCharacter benchmarks demonstrate that DeSRPA significantly outperforms E2E baselines in personality and emotional consistency. It achieves high speech naturalness, narrowing the gap with proprietary models like GPT-4o Audio, while remaining a scalable and training-free paradigm.

cs.SD↗

arXiv2026

Speech-Hands: A Self-Reflection Voice Agentic Approach to Speech Recognition and Audio Reasoning with Omni Perception

We introduce a voice-agentic framework that learns one critical omni-understanding skill: knowing when to trust itself versus when to consult external audio perception. Our work is motivated by a crucial yet counterintuitive finding: naively fine-tuning an omni-model on both speech recognition and external sound understanding tasks often degrades performance, as the model can be easily misled by noisy hypotheses. To address this, our framework, Speech-Hands, recasts the problem as an explicit self-reflection decision. This learnable reflection primitive proves effective in preventing the model from being derailed by flawed external candidates. We show that this agentic action mechanism generalizes naturally from speech recognition to complex, multiple-choice audio reasoning. Across the OpenASR leaderboard, Speech-Hands consistently outperforms strong baselines by 12.1% WER on seven benchmarks. The model also achieves 77.37% accuracy and high F1 on audio QA decisions, showing robust generalization and reliability across diverse audio question answering datasets. By unifying perception and decision-making, our work offers a practical path toward more reliable and resilient audio intelligence.

cs.SD↗

arXiv2026

Timing Jitter Induced by Stochastic Baseline Fluctuations in High-Count-Rate Superconducting Nanowire Single-Photon Detectors

Superconducting nanowire single-photon detectors (SNSPDs) have demonstrated timing jitter in the few-picosecond regime, yet their timing resolution deteriorates substantially under high-count-rate operation. Existing interpretations mainly attribute this degradation to deterministic waveform distortions, such as multiphoton responses and pulse pile-up, yet the experimentally observed jitter broadening at high count rates cannot be fully accounted for within this picture. Here, we show that stochastic baseline fluctuations arising from finite-memory readout dynamics constitute an intrinsic source of the count-rate-dependent timing jitter in SNSPD systems. For stochastically arriving photons, overlapping recovery responses accumulate in the readout chain and generate statistically fluctuating baselines, which are converted into timing uncertainty through threshold-based timing extraction. We develop a stochastic-process framework that quantitatively connects photon statistics, readout dynamics, and timing jitter. The framework predicts characteristic scaling behaviors, including a nonmonotonic dependence of baseline fluctuations under pulsed excitation with a maximum near half of the repetition frequency. These predictions are quantitatively verified through systematic variations of count rate, circuit time constant, and detector dynamical properties. Our results identify stochastic baseline dynamics as a fundamental mechanism limiting timing resolution in high-count-rate SNSPD operation and provide a general framework for optimizing finite-memory high-speed photon-counting systems.

physics.app-ph↗

arXiv2026

Facet-Level Persona Control by Trait-Activated Routing with Contrastive SAE for Role-Playing LLMs

Personality control in Role-Playing Agents (RPAs) is commonly achieved via training-free methods that inject persona descriptions and memory through prompts or retrieval-augmented generation, or via supervised fine-tuning (SFT) on persona-specific corpora. While SFT can be effective, it requires persona-labeled data and retraining for new roles, limiting flexibility. In contrast, prompt- and RAG-based signals are easy to apply but can be diluted in long dialogues, leading to drifting and sometimes inconsistent persona behavior. To address this, we propose a contrastive Sparse AutoEncoder (SAE) framework that learns facet-level personality control vectors aligned with the Big Five 30-facet model. A new 15,000-sample leakage-controlled corpus is constructed to provide balanced supervision for each facet. The learned vectors are integrated into the model's residual space and dynamically selected by a trait-activated routing module, enabling precise and interpretable personality steering. Experiments on Large Language Models (LLMs) show that the proposed method maintains stable character fidelity and output quality across contextualized settings, outperforming Contrastive Activation Addition (CAA) and prompt-only baselines. The combined SAE+Prompt configuration achieves the best overall performance, confirming that contrastively trained latent vectors can enhance persona control while preserving dialogue coherence. Dataset is available at: https://github.com/lunat5078/BigFive-Personality-Facets-Dataset

cs.CL↗

arXiv2026

WFST Astrometric Calibration -- I. Modeling Global Geometric Distortion with Zernike Polynomials

Accurate modeling of geometric distortion is essential for precise astrometric calibration in wide-field imaging surveys. We present a self-calibration method based on Zernike polynomials, applied to imaging data from the Wide Field Survey Telescope (WFST). Our approach constructs a global geometric distortion (GD) model from the position offsets of stars in the WFST r-band relative to Gaia DR3, achieving a median systematic uncertainty of below 10 mas for individual exposures. The correspondence between Zernike polynomials and optical aberrations reveals that the global GD of WFST is dominated by coma, inherent to the optical design, while rapid variations are likely attributed to the atmospheric dispersion corrector. Applying this method to 82 exposures from a single night (20250218), we find that the relative positions of the WFST CCDs remain stable, with standard deviations of less than 0.1 pixel in translation and 1.8 arcsec in rotation. The corrected WFST astrometric system is thereby tied to the Gaia DR3 coordinate frame, with further refinements to be presented in future work.

astro-ph.IM↗

arXiv2026

WFST Supernovae in the First Year: I. Statistical Study of 16 Early-phase Type Ia Supernovae from the Pilot Survey

In this paper we present 16 early-phase type Ia supernovae (SNe Ia) discovered during the pilot survey of the 2.5-meter Wide Field Survey Telescope (WFST-PS) from March 4 to July 10, 2024, including three SNe Ia with early-excess emission features (EExSNe Ia). The discovery magnitude of the 16 WFST-PS early-phase SNe is at least 3 mag fainter than their peak brightness. A large scatter of color indices is found in approximately the first 10 days of supernova explosions, indicating diverse photometric behaviors in the early phase. Three EExSNe Ia show relatively brighter peak luminosities and longer rise time compared to those of non-EExSNe Ia. The results indicate that current theoretical models require further refinement to fully capture the early photometric evolution of SNe Ia. Based on the initial high-cadence ugr-band data from the WFST-PS survey, we emphasize that early near-ultraviolet (NUV) observations are indispensable for placing tight constraints on the explosion mechanisms and progenitor systems of SNe Ia.

astro-ph.HE↗

arXiv2026

WFST Supernovae in the First Year: II. SN 2024aedt: Systematical Study of a Transitional Type Ia Supernova

We present comprehensive photometric and spectroscopic observations of a transitional type Ia SN 2024aedt, discovered by the 2.5-meter Wide Field Survey Telescope (WFST) within one day of the explosion. Its light curve is characterized by a peak absolute magnitude of $M_B = -18.49 \pm 0.03$ mag and a decline rate of $Δm_{15}(B) = 1.53 \pm 0.36$ mag, placing the object on the $Δm_{15}(B)$--$M_B$ diagram in the transition region between normal and subluminous SNe Ia. Furthermore, the early-color evolution and host galaxy environment of SN 2024aedt underscore its transitional nature, sharing properties with both normal and 91bg-like SNe Ia. Light-curve modeling with MOSFiT yields a synthesized $^{56}\mathrm{Ni}$ mass of $0.414 \pm 0.042\,M_{\odot}$ and a total ejecta mass of $0.548 \pm 0.108\,M_{\odot}$. A comparison with theoretical models suggests that the evolutionary trend can be broadly explained by both delayed-detonation (DDT) and double-detonation (DDet) scenarios while possible early-excess emissions predicted by DDet cannot be identified given the limited detections soon after the SN explosion. Although the overall spectral evolution of SN 2024aedt is similar to that of other transitional SNe Ia, the spectroscopic comparison reveals diversity in the early-phase blue-end features, which becomes more homogeneous at later phases. The result indicates the importance of early-time observations in understanding the origin of SN Ia diversity.

astro-ph.HE↗

arXiv2026

Illuminating the Mass Gap Through Deep Optical Constraint on a Neutron Star Merger Candidate S250206dm

The gravitational wave (GW) event S250206dm, as the first well-localized neutron star merger candidate potentially located in the mass gap, presented a unique opportunity to probe the electromagnetic signatures from such a system. Here we report a deep, multiband search with the new 2.5-meter Wide Field Survey Telescope (WFST), covering about 64% of the localization region up to a 5-sigma limiting magnitude of 23 mag. In total, 12 potential candidates have been identified while none of them are likely related to S250206dm. This non-detection provides the most stringent constraint to date on any associated kilonova. Crucially, an AT 2017gfo-like event at 269 Mpc can be excluded by WFST observations alone. Based on ejecta mass limits, a neutron star-black hole with a large mass ratio (Q >= 3.2) is disfavored. This optical-derived constraint on the mass ratio reaches, for the first time, a precision comparable to that inferred from the GW signal. This work presents the best observation of this type of events until now, and demonstrates the power of rapid, deep follow-up observations to constrain the properties of compact binary progenitors, offering key insights into the constituents of the mass gap.

astro-ph.HE↗

arXiv2026

The 2.5-meter Wide Field Survey Telescope Real-time Data Processing Pipeline I: From raw data to alert distribution

The Wide Field Survey Telescope (WFST) is a dedicated photometric surveying facility built jointly by the University of Science and Technology of China (USTC) and the Purple Mountain Observatory (PMO). Since many of its scientific objectives rely on near-real-time data for effective analysis, prompt processing of WFST images is of great significance. To meet this need, we adapted the Rubin Observatory Legacy Survey of Space and Time (LSST) science pipelines to handle the data collected by WFST. This paper presents the complete data processing workflow, from ingestion of raw images to the distribution of alerts, and details the primary data products generated by our pipeline. Researchers using data processed by this pipeline can refer to this document to fully understand the data processing procedures.

astro-ph.IM↗

arXiv2025

The Statistical Analysis Of The Galactic Open Clusters' Structure

We present a systematic investigation of 1,481 Galactic open clusters (OCs) through the application of the Limepy dynamical model, from which we derive the fundamental structural parameters of OCs. We conduct the statistical analyses on the structural parameters with clusters' ages and locations within the Milky Way. Our results reveal the higher concentration in the cluster centeris associated with the sharper truncation at the periphery of cluster, which is consistent with previous findings for globular clusters(GCs). We further find the systematic increase of the lower limit of clusters' half-mass radius (Rh) with age. Our results also show that OCs located at larger vertical distances from the Galactic plane systematically display higher central concentrations. Our findings collectively suggest that the structural characteristics of OCs are shaped by both intrinsic evolutionary processes and interactions with the Galactic environment. During the evolution of star clusters, the combined effects of mass segregation and tidal stripping lead to the systematic pattern between central concentration and outer truncation. Clusters of different ages and locations within the Milky Way undergo different evolutionary histories, resulting in correlations between the Rh and age, as well as between central concentration and galactic location.

astro-ph.GA↗

arXiv2025

Detailed Chemical Abundance Analysis of the Brightest Stars in the Turranburra and Willka Yaku Stellar Streams

We present a detailed chemical abundance analysis of the three brightest known stars from each of the Turranburra and Willka Yaku stellar streams using high-resolution Magellan/MIKE spectra. Abundances for 27 elements, ranging from carbon to dysprosium, were derived. Our results support the original classification that Turranburra, with a low average metallicity of $\mathrm{[Fe/H]=-2.45} \pm 0.07$, likely originates from a dwarf-galaxy progenitor. Willka Yaku has a low average metallicity of $\mathrm{[Fe/H]=-2.35 \pm 0.03}$ with a small scatter in the abundances, consistent with a globular cluster progenitor as suggested by previous studies. Both streams exhibit mild enhancements in neutron-capture elements, with averages of $\mathrm{[Eu II/Fe]}=$ $0.47 \pm{0.09}$ for Turranburra and $0.44 \pm{0.05}$ for Willka Yaku, consistent with enrichment from an $r$-process event. A similar enrichment is observed in other stellar streams, and we further discuss this signature as it relates to the potential enrichment histories of these two streams.

astro-ph.SR↗

arXiv2025

NeKo: Cross-Modality Post-Recognition Error Correction with Tasks-Guided Mixture-of-Experts Language Model

Construction of a general-purpose post-recognition error corrector poses a crucial question: how can we most effectively train a model on a large mixture of domain datasets? The answer would lie in learning dataset-specific features and digesting their knowledge in a single model. Previous methods achieve this by having separate correction language models, resulting in a significant increase in parameters. In this work, we present Mixture-of-Experts as a solution, highlighting that MoEs are much more than a scalability tool. We propose a Multi-Task Correction MoE, where we train the experts to become an ``expert'' of speech-to-text, language-to-text and vision-to-text datasets by learning to route each dataset's tokens to its mapped expert. Experiments on the Open ASR Leaderboard show that we explore a new state-of-the-art performance by achieving an average relative 5.0% WER reduction and substantial improvements in BLEU scores for speech and translation tasks. On zero-shot evaluation, NeKo outperforms GPT-3.5 and Claude-Opus with 15.5% to 27.6% relative WER reduction in the Hyporadise benchmark. NeKo performs competitively on grammar and post-OCR correction as a multi-task model.

cs.CL↗

arXiv2025

SpeechIQ: Speech-Agentic Intelligence Quotient Across Cognitive Levels in Voice Understanding by Large Language Models

We introduce Speech-based Intelligence Quotient (SIQ) as a new form of human cognition-inspired evaluation pipeline for voice understanding large language models, LLM Voice, designed to assess their voice understanding ability. Moving beyond popular voice understanding metrics such as word error rate (WER), SIQ examines LLM Voice across three cognitive levels motivated by Bloom's Taxonomy: (1) Remembering (i.e., WER for verbatim accuracy); (2) Understanding (i.e., similarity of LLM's interpretations); and (3) Application (i.e., QA accuracy for simulating downstream tasks). We demonstrate that SIQ not only quantifies voice understanding abilities but also provides unified comparisons between cascaded methods (e.g., ASR LLM) and end-to-end models, identifies annotation errors in existing benchmarks, and detects hallucinations in LLM Voice. Our framework represents a first-of-its-kind intelligence examination that bridges cognitive principles with voice-oriented benchmarks, while exposing overlooked challenges in multi-modal training. Our code and data will be open source to encourage future studies.

cs.CL↗

arXiv2025

Multiwavelength Observations of the Apparently Non-repeating FRB 20250316A

The physical origin of fast radio bursts (FRBs) remains uncertain. Although multiwavelength observations have been widely conducted, only Galactic FRB~20200428D is associated with an X-ray burst from the magnetar SGR J1935+2154. Here, we present multiwavelength follow-up observations of the nearby bright FRB~20250316A, including the Five-hundred-meter Aperture Spherical radio Telescope (FAST), Einstein Probe (EP) X-ray mission, Chandra X-ray Observatory, Wide Field Survey Telescope (WFST) and Space Variable Object Monitor/Visible Telescope (SVOM/VT). The 13.08-hour FAST follow-up campaign without pulse detection requires an energy distribution flatter than those of well-known repeating FRBs, suggesting that this burst is likely a one-off event. A prompt EP follow-up and multi-epoch observational campaign totaling $>$ 100 ks led to the detection of an X-ray source within the angular resolution of its Follow-up X-ray Telescope (FXT, $10^{\prime\prime}$). A subsequent Chandra observation revealed this source to be offset by $7^{\prime\prime}$ from the FRB position, and established a 0.5-10 keV flux upper limit of $7.6\times 10^{-15}$ $\rm erg\,cm^{-2}\,s^{-1}$ at the FRB position, corresponding to $\sim 10^{39}$ $\rm erg\,s^{-1}$ at the 40 Mpc distance of the host galaxy NGC~4141. These results set one of the most stringent limits on X-ray emission from a non-repeating FRB, disfavoring ultra-luminous X-ray sources (ULXs) as counterparts of apparently one-off FRBs and offering critical insights into afterglow models. Our study suggests that an arcsecond localization of both the FRB and its potential X-ray counterpart is essential for exploring the X-ray counterpart of an FRB.

astro-ph.HE↗

arXiv2025

A Star's Death by a Thousand Cuts: The Runaway Periodic Eruptions of AT2023uqm

Stars on bound orbits around a supermassive black hole may undergo repeated partial tidal disruption events (rpTDEs), producing periodic flares. While several candidates have been suggested, definitive confirmation of these events remains elusive. We report the discovery of AT2023uqm, a nuclear transient that has exhibited at least five periodic optical flares, making it only the second confirmed case of periodicity after ASASSN-14ko. Uniquely, the flares from AT2023uqm show a nearly exponential increase in energy--a "runaway" phenomenon signaling the star's progressive destruction. This behavior is consistent with rpTDEs of low-mass, main-sequence stars or evolved giant stars. Multiwavelength observations and spectroscopic analysis of the two most recent flares reinforce its interpretation as an rpTDE. Intriguingly, each flare displays a similar double-peaked structure, potentially originating from a double-peaked mass fallback rate or two discrete collisions per orbit. The extreme ratio of peak separation to orbital period draws attention to the possibility of a giant star being disrupted, which could be distinguished from a low-mass main-sequence star by its future mass-loss evolution. Our analysis demonstrates the power of rpTDEs to probe the properties of disrupted stars and the physical processes of tidal disruption, though it is currently limited by our knowledge of these events. AT2023uqm emerges as the most compelling rpTDE thus far, serving as a crucial framework for modeling and understanding these phenomena.

astro-ph.HE↗

arXiv2025

OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLM

Advancing machine intelligence requires developing the ability to perceive across multiple modalities, much as humans sense the world. We introduce OmniVinci, an initiative to build a strong, open-source, omni-modal LLM. We carefully study the design choices across model architecture and data curation. For model architecture, we present three key innovations: (i) OmniAlignNet for strengthening alignment between vision and audio embeddings in a shared omni-modal latent space; (ii) Temporal Embedding Grouping for capturing relative temporal alignment between vision and audio signals; and (iii) Constrained Rotary Time Embedding for encoding absolute temporal information in omni-modal embeddings. We introduce a curation and synthesis pipeline that generates 24M single-modal and omni-modal conversations. We find that modalities reinforce one another in both perception and reasoning. Our model, OmniVinci, outperforms Qwen2.5-Omni with +19.05 on DailyOmni (cross-modal understanding), +1.7 on MMAR (audio), and +3.9 on Video-MME (vision), while using just 0.2T training tokens - a 6 times reduction compared to Qwen2.5-Omni's 1.2T. We finally demonstrate omni-modal advantages in downstream applications spanning robotics, medical AI, and smart factory.

cs.CV↗