arXiv subjects

Jie Zheng

Publications and source records attributed to Jie Zheng.

At least 55 records · Page 3Linked to original sources

arXiv2025

Dynamically Polarized SERF Atomic Comagnetometer

Atomic spin sensors are essential for beyond-the-standard-model exploration, biomagnetic measurement, and quantum navigation. While the traditional DC mode spin-exchange relaxation-free (SERF) comagnetometer achieves ultrahigh sensitivity, further improvements require suppressing technical noise and surpassing standard quantum limit. In this work, we develop a K-Rb-$^{21}$Ne SERF atomic comagnetometer that dynamically polarizes the electron and nuclear spins, shielding signals from direct interference by pump light. We establish a three-phase evolutionary model for hybrid spin ensemble dynamics, yielding a complete analytical solution, and analyze the responses to various spin perturbations. Additionally, we achieve an averaged 38.5 $\%$ suppression of the polarization noise and identify the key factors that limit sensitivity improvements. The dynamically polarized comagnetometer exhibits effective suppression of technical noise and holds the potential to overcome quantum noise limit, while offering promising applications in exploring new physics and precise magnetic field measurements.

physics.atom-ph↗

arXiv2025

Architecture of Tianyu Software: Relative Photometry as a Case Study

Tianyu telescope, an one-meter robotic optical survey instrument to be constructed in Lenghu, Qinghai, China, is designed for detecting transiting exoplanets, variable stars and transients. It requires a highly automated, optimally distributed, easily extendable, and highly flexible software to enable the data processing for the raw data at rates exceeding 500MB/s. In this work, we introduce the architecture of the Tianyu pipeline and use relative photometry as a case to demonstrate its high scalability and efficiency. This pipeline is tested on the data collected from Muguang observatory and Xinglong observatory. The pipeline demonstrates high scalability, with most processing stages increasing in throughput as the number of consumers grows. Compared to a single consumer, the median throughput of image calibration, alignment, and flux extraction increases by 41%, 257%, and 107% respectively when using 5 consumers, while image stacking exhibits limited scalability due to I/O constraints. In our tests, the pipeline was able to detect two transiting sources. Besides, the pipeline captures variability in the light curves of nine known and two previously unknown variable sources in the testing data. Meanwhile, the differential photometric precision of the light curves is near the theoretical limitation. These results indicate that this pipeline is suitable for detecting transiting exoplanets and variable stars. This work builds the fundation for further development of Tianyu software. Code of this work is available at https://github.com/ruiyicheng/Tianyu_pipeline.

astro-ph.IM↗

arXiv2025

Unsupervised Machine Learning for Classifying CHIME Fast Radio Bursts and Investigating Empirical Relations

Fast Radio Bursts (FRBs) are highly energetic millisecond-duration astrophysical phenomena typically categorized as repeaters or non-repeaters. However, observational limitations may result in misclassifications, potentially leading to a higher proportion of repeaters than currently identified. In this study, we leverage unsupervised machine learning techniques to classify FRBs using data from the CHIME/FRB catalogs, including both the first catalog and a recent repeater catalog. By employing Uniform Manifold Approximation and Projection for dimensionality reduction and clustering algorithms (k-means and Hierarchical Density-Based Spatial Clustering of Applications with Noise), we successfully segregate repeaters and non-repeaters into distinct clusters, identifying over 100 potential repeater candidates. Our analysis reveals several empirical relations within the clusters, including the ${\rm log \,}Δt_{sc}-{\rm log \,}Δt_{rw}$, ${\rm log \,}Δt_{sc}-{\rm log \,}T_B$, and $r - γ$ correlations, where ${Δt_{sc}, Δt_{rw}, T_B, r, γ}$ represent scattering time, rest-frame width, brightness temperature, spectral running, and spectral index, respectively. The Chow test results reveal that while some repeaters and non-repeaters share similar empirical relationships, the overall distinctions between the two groups remain significant, reinforcing the classification of FRBs into repeaters and non-repeaters. These findings provide new insights into the physical properties and emission mechanisms of FRBs. This study demonstrates the effectiveness of unsupervised learning in classifying FRBs and identifying potential repeaters, paving the way for more precise investigations into their origins and applications in cosmology. Future improvements in observational data and machine learning methodologies are expected to further enhance our understanding of FRBs.

astro-ph.HE↗

arXiv2025

The Mini-SiTian Array: Prospects of Searching for Tidal Disruption Events

We assess the detectability of tidal disruption events (TDEs) using mock observations from the Mini-SiTian array. We select 100 host galaxy samples from a simulated galaxy catalog based on specific criteria such as redshift, BH mass, and event rate. Taking into account the site conditions and survey strategy, we simulate observations over a 440 deg$^2$ field. The results indicate that $0.53\pm 0.73$ TDEs can be detected per year when observing in both $g$ and $r$ bands with 300-second exposures every 3 days. Applying this method to the SiTian project, we expect to discover approximately 204 TDEs annually, heralding a new era in TDE science.

astro-ph.HE↗

arXiv2025

The Mini-SiTian Array: Imaging Processing Pipeline

As a pathfinder of the SiTian project, the Mini-SiTian (MST) array, employed three commercial CMOS cameras, represents a next-generation, cost-effective optical time-domain survey project. This paper focuses primarily on the precise data processing pipeline designed for wide-field, CMOS-based devices, including the removal of instrumental effects, astrometry, photometry, and flux calibration. When applying this pipeline to approximately 3000 observations taken in the Field 02 (f02) region by MST, the results demonstrate a remarkable astrometric precision of approximately 70--80\,mas (about 0.1\,pixel), an impressive calibration accuracy of approximately 1\,mmag in the MST zero points, and a photometric accuracy of about 4\,mmag for bright stars. Our studies demonstrate that MST CMOS can achieve photometric accuracy comparable to that of CCDs, highlighting the feasibility of large-scale CMOS-based optical time-domain surveys and their potential applications for cost optimization in future large-scale time-domain surveys, like the SiTian project.

astro-ph.IM↗

arXiv2025

The Mini-SiTian Array: first-two-year operation

The SiTian project, designed to utilize 60 telescopes distributed across multiple sites in China, is a next-generation time-domain survey initiative. As a pathfinder for the SiTian project, the Mini-SiTian (MST) has been proposed and implemented to test the SiTian's brain and data pipeline, and to evaluate the feasibility of its technology and science cases. Mounted at the Xinglong Observatory, the MST project comprises three 30 cm telescopes and has been operated since Nov. 2022. Each telescope of the MST possesses a large field of view, covering $2.29^{\circ}$ $\times$ $1.53^{\circ}$ FOV, and is equipped with $g'$, $r'$ and $i'$ filters, respectively. Acting as the pioneer of the forthcoming SiTian project, the MST is dedicated to the discovery of variable stars, transients, and outburst events, and has already obtained some interesting scientific results. In this paper, we will summarize the first-two-year operation of the MST project.

astro-ph.IM↗

arXiv2025

CellVTA: Enhancing Vision Foundation Models for Accurate Cell Segmentation and Classification

Cell instance segmentation is a fundamental task in digital pathology with broad clinical applications. Recently, vision foundation models, which are predominantly based on Vision Transformers (ViTs), have achieved remarkable success in pathology image analysis. However, their improvements in cell instance segmentation remain limited. A key challenge arises from the tokenization process in ViTs, which substantially reduces the spatial resolution of input images, leading to suboptimal segmentation quality, especially for small and densely packed cells. To address this problem, we propose CellVTA (Cell Vision Transformer with Adapter), a novel method that improves the performance of vision foundation models for cell instance segmentation by incorporating a CNN-based adapter module. This adapter extracts high-resolution spatial information from input images and injects it into the ViT through a cross-attention mechanism. Our method preserves the core architecture of ViT, ensuring seamless integration with pretrained foundation models. Extensive experiments show that CellVTA achieves 0.538 mPQ on the CoNIC dataset and 0.506 mPQ on the PanNuke dataset, which significantly outperforms the state-of-the-art cell segmentation methods. Ablation studies confirm the superiority of our approach over other fine-tuning strategies, including decoder-only fine-tuning and full fine-tuning. Our code and models are publicly available at https://github.com/JieZheng-ShanghaiTech/CellVTA.

cs.CV↗

arXiv2025

Interpretable High-order Knowledge Graph Neural Network for Predicting Synthetic Lethality in Human Cancers

Synthetic lethality (SL) is a promising gene interaction for cancer therapy. Recent SL prediction methods integrate knowledge graphs (KGs) into graph neural networks (GNNs) and employ attention mechanisms to extract local subgraphs as explanations for target gene pairs. However, attention mechanisms often lack fidelity, typically generate a single explanation per gene pair, and fail to ensure trustworthy high-order structures in their explanations. To overcome these limitations, we propose Diverse Graph Information Bottleneck for Synthetic Lethality (DGIB4SL), a KG-based GNN that generates multiple faithful explanations for the same gene pair and effectively encodes high-order structures. Specifically, we introduce a novel DGIB objective, integrating a Determinant Point Process (DPP) constraint into the standard IB objective, and employ 13 motif-based adjacency matrices to capture high-order structures in gene representations. Experimental results show that DGIB4SL outperforms state-of-the-art baselines and provides multiple explanations for SL prediction, revealing diverse biological mechanisms underlying SL inference.

cs.LG↗

arXiv2025

Cancer Vaccine Adjuvant Name Recognition from Biomedical Literature using Large Language Models

Motivation: An adjuvant is a chemical incorporated into vaccines that enhances their efficacy by improving the immune response. Identifying adjuvant names from cancer vaccine studies is essential for furthering research and enhancing immunotherapies. However, the manual curation from the constantly expanding biomedical literature poses significant challenges. This study explores the automated recognition of vaccine adjuvant names using Large Language Models (LLMs), specifically Generative Pretrained Transformers (GPT) and Large Language Model Meta AI (Llama). Methods: We utilized two datasets: 97 clinical trial records from AdjuvareDB and 290 abstracts annotated with the Vaccine Adjuvant Compendium (VAC). GPT-4o and Llama 3.2 were employed in zero-shot and few-shot learning paradigms with up to four examples per prompt. Prompts explicitly targeted adjuvant names, testing the impact of contextual information such as substances or interventions. Outputs underwent automated and manual validation for accuracy and consistency. Results: GPT-4o attained 100% Precision across all situations while exhibiting notable improve in Recall and F1-scores, particularly with incorporating interventions. On the VAC dataset, GPT-4o achieved a maximum F1-score of 77.32% with interventions, surpassing Llama-3.2-3B by approximately 2%. On the AdjuvareDB dataset, GPT-4o reached an F1-score of 81.67% for three-shot prompting with interventions, surpassing Llama-3.2-3 B's maximum F1-score of 65.62%. Conclusion: Our findings demonstrate that LLMs excel at identifying adjuvant names, including rare variations of naming representation. This study emphasizes the capability of LLMs to enhance cancer vaccine development by efficiently extracting insights. Future work aims to broaden the framework to encompass various biomedical literature and enhance model generalizability across various vaccines and adjuvants.

cs.CL↗

arXiv2025

The Gas-to-Dust Ratio Investigation in the Massive Star-Forming region M17

M17 is a well-known massive star-forming region, and its Gas-to-Dust Ratio (GDR) may vary significantly compared to the other areas. The mass of gas can be traced by the ${\rm CO}$ emission observed in the \emph{Milky Way Imaging Scroll Painting (MWISP) project}. The dust mass can be traced by analyzing the interstellar extinction magnitude obtained from the \emph{United Kingdom Infrared Telescope (UKIRT)}. We computed the ratio ${W({\rm CO})/A_V}$: for ${A_V \le }$ 10 mag, ${{ W(^{12}{\rm CO})/ A_V}= (6.27 \pm 0.19)}$ ${\mathrm{{K \cdot km/s} \cdot mag^{-1}}}$ and ${{ W(^{13}{\rm CO})/ A_V} = (0.75 \pm 0.72)}$ ${ \mathrm{{K \cdot km/s} \cdot mag^{-1}}}$; whereas for ${{A_V} \ge 10}$ mag, ${{ W(^{12}{\rm CO})/ A_V} = (15.8 \pm 0.06) }$ ${\mathrm{{K \cdot km/s} \cdot mag^{-1}}}$ and ${{ W(^{13}{\rm CO})/ A_V} = (3.11 \pm 0.25)}$ ${ \mathrm{{K \cdot km/s} \cdot mag^{-1}}}$. Then, we converted the ${W({\rm CO})/A_V}$ into ${N(\rm H)/A_V}$. Using the WD01 model, we derived the GDR: for ${A_V \le }$ 10 mag, the GDRs were ${118 \pm 9}$ for ${^{12}{\rm CO}}$ and ${83 \pm 62}$ for ${^{13}{\rm CO}}$, comparable to those of the Milky Way; however, for ${A_V \ge }$ 10 mag, the GDRs increased significantly to ${296 \pm 3}$ for ${^{12}{\rm CO}}$ and ${387 \pm 40}$ for ${^{13}{\rm CO}}$, approximately three times higher than those of the Milky Way. In the discussion, we compared the results of this work with previous studies and provided a detailed discussion of the influence of massive stars and other factors on GDR.

astro-ph.GA↗

arXiv2025

The Stellar Abundances and Galactic Evolution Survey (SAGES). II. Machine Learning-Based Stellar parameters for 21 million stars from the First Data Release

Stellar parameters for large samples of stars play a crucial role in constraining the nature of stars and stellar populations in the Galaxy. An increasing number of medium-band photometric surveys are presently used in estimating stellar parameters. In this study, we present a machine-learning approach to derive estimates of stellar parameters, including [Fe/H], logg, and Teff, based on a combination of medium-band and broad-band photometric observations. Our analysis employs data primarily sourced from the SAGE Survey , which aims to observe much of the Northern Hemisphere. We combine the $uv$-band data from SAGES DR1 with photometric and astrometric data from Gaia EDR3, and apply the random forest method to estimate stellar parameters for approximately 21 million stars. We are able to obtain precisions of 0.09 dex for [Fe/H], 0.12 dex for logg, and 70 K for Teff. Furthermore, by incorporating 2MASS and WISE infrared photometric and GALEX ultraviolet data, we are able to achieve even higher precision estimates for over 2.2 million stars. These results are applicable to both giant and dwarf stars. Building upon this mapping, we construct a foundational dataset for research on metal-poor stars, the structure of the Milky Way, and beyond. With the forthcoming release of additional bands from SAGE Survey such DDO51 and H-alpha, this versatile machine learning approach is poised to play an important role in upcoming surveys featuring expanded filter sets

astro-ph.SR↗

arXiv2024

Cosmological constraints on dark energy models using DESI BAO 2024

Recently, the measurements of baryon acoustic oscillations (BAO) by the Dark Energy Spectroscopic Instrument (DESI) indicate a potential deviation from the standard $Λ$CDM model. Some studies suggest that the data points from the luminous red galaxies (LRG) survey in DESI BAO data may contribute to this discrepancy. In this work, our main goal is to investigate whether this deviation is caused by the parameterization of the equation of state (EoS) of dark energy (DE). Hence, we have examined four popular parameterized dark energy models in our analysis: the Chevallier-Polarski-Linder (CPL), Barboza-Alcaniz (BA), Jassal-Bagla-Padmanabhan (JBP), and Feng-Shen-Li-Li (FSLL) parameterizations. Considering that LRG1 and LRG2 data points may lead to deviation from the $Λ$CDM model, we use two versions of DESI BAO data, differing in whether these data points are included. Additionally, to break the parameter degeneracies and obtain robust constraint results, we introduce Type Ia supernovae (SNe Ia) and quasars (QSO) in our analysis. Our findings indicate that in these parameterizations, the deviation from ($w_0$,$w_{1}$)=(-1,0) becomes more pronounced when using the combined data from DESI BAO, SNe Ia, and QSO compilations. Here, $w_{0}$ and $w_{1}$ represent the EoS of DE. It suggests that the parameterizations of the EoS of DE have little impact on the deviation from the $Λ$CDM model. Besides, our analysis potentially hints that dark energy may have dynamic properties. In addition, the results obtained from different BAO datasets demonstrate that the LRG1 and LRG2 data points do indeed contribute to a deviation from the $Λ$CDM model. Finally, according to the statistical criteria, the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), the joint constraints provide substantial observational support to the BA and FSLL models.

astro-ph.CO↗

arXiv2024

The Stellar Abundances and Galactic Evolution Survey (SAGES) III -- The g/r/i-band Data Release

The Stellar Abundances and Galactic Evolution Survey (SAGES) is a multi-band survey that covers the northern sky area of ~12000 deg2. Nanshan One-meter Wide-field Telescope (NOWT) of Xinjiang Astronomical Observatory (XAO) carried out observations on g/r/i bands. We present here the survey strategy, data processing, catalog construction, and database schema. The observations of NOWT started in 2016 August and was completed in 2018 January, total 17827 frames were obtained and ~4600 deg2 sky areas were covered. In this paper, we released the catalog of the data in the g/r/i bands observed with NOWT. In total, there are 109,197,578 items of the source records. The catalog is the supplement for the SDSS for the bright end, and the combination of our catalog and these catalogs could be helpful for source selections for other surveys and the Milky Way sciences, e.g., white dwarf candidates and stellar flares.

astro-ph.GA↗

arXiv2024

Unveiling the Multifaceted GRB 200613A: Prompt Emission Dynamics, Afterglow Evolution, and the Host Galaxy's Properties

We present our optical observations and multi-wavelength analysis of the GRB\,200613A detected by \texttt{Fermi} satellite. Time-resolved spectral analysis of the prompt $γ$-ray emission was conducted utilizing the Bayesian block method to determine statistically optimal time bins. Based on the Bayesian Information Criterion (BIC), the data generally favor the Band+Blackbody (short as BB) model. We speculate that the main Band component comes from the Blandford-Znajek mechanism, while the additional BB component comes from the neutrino annihilation process. The BB component becomes significant for a low-spin, high-accretion rate black hole central engine, as evidenced by our model comparison with the data. The afterglow light curve exhibits typical power-law decay, and its behavior can be explained by the collision between the ejecta and constant interstellar medium (ISM). Model fitting yields the following parameters: $E_{K,iso} = (2.04^{+11.8}_{-1.50})\times 10^{53}$ erg, $Γ_0=354^{+578}_{-217}$, $p=2.09^{+0.02}_{-0.03}$, $n_{18}=(2.04^{+9.71}_{-1.87})\times 10^{2}$ cm$^{-3}$, $θ_j=24.0^{+6.50}_{-5.54}$ degree, $ε_e=1.66^{+4.09}_{-1.39})\times 10^{-1}$ and $ε_B=(7.76^{+48.5}_{-5.9})\times 10^{-6}$. In addition, we employed the public Python package \texttt{Prospector} perform a spectral energy distribution (SED) modeling of the host galaxy. The results suggest that the host galaxy is a massive galaxy ($\log(M_\ast / M_\odot)=11.75^{+0.10}_{-0.09}$) with moderate star formation rate ($\mbox{SFR}=22.58^{+13.63}_{-7.22} M_{\odot}$/yr). This SFR is consistent with the SFR of $\sim 34.2 M_{\odot}$ yr$^{-1}$ derived from the [OII] emission line in the observed spectrum.

astro-ph.HE↗

arXiv2024

Probing the shape of the primordial curvature power spectrum and the energy scale of reheating with pulsar timing arrays

The stochastic gravitational wave background (SGWB) provides a unique opportunity to probe the early Universe, potentially encoding information about the primordial curvature power spectrum and the energy scale of reheating. Recent observations by collaborations such as NANOGrav, PPTA, EPTA+InPTA, and CPTA have detected a stochastic common-spectrum signal, which may originate from scalar-induced gravitational waves (SIGWs) generated by primordial curvature perturbations during inflation. In this study, we explore the hypothesis that the NANOGrav signal is sourced by SIGWs and aim to constrain the shape of the primordial curvature power spectrum and the reheating energy scale using the NANOGrav 15-year data set. We model the primordial curvature power spectrum with a lognormal form and focus on the case where the equation of state during reheating is $w=1/6$, corresponding to an inflaton potential $V(ϕ) \sim ϕ^{14/5}$. Employing Bayesian inference, we obtain posterior distributions for the lognormal power spectrum parameters and the reheating temperature. Our results indicate a narrow peak in the primordial power spectrum ($Δ< 0.001$ at 90\% confidence) and a lower bound on the reheating temperature ($T_{\rm rh} \geq 0.1 {\rm GeV}$), consistent with Big Bang Nucleosynthesis constraints. The best-fit SIGW energy density spectrum exhibits a distinct turning point around $f \sim 10^{-8.1}\,{\rm Hz}$, corresponding to the transition from reheating to the radiation-dominated era. This feature, combined with the sharp high-frequency decrease due to the narrow primordial power spectrum peak, offers a unique signature for probing early Universe properties.

astro-ph.CO↗

arXiv2024

SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System for GNN-based Synthetic Lethal Prediction

Synthetic Lethal (SL) relationships, though rare among the vast array of gene combinations, hold substantial promise for targeted cancer therapy. Despite advancements in AI model accuracy, there is still a significant need among domain experts for interpretive paths and mechanism explorations that align better with domain-specific knowledge, particularly due to the high costs of experimentation. To address this gap, we propose an iterative Human-AI collaborative framework with two key components: 1) Human-Engaged Knowledge Graph Refinement based on Metapath Strategies, which leverages insights from interpretive paths and domain expertise to refine the knowledge graph through metapath strategies with appropriate granularity. 2) Cross-Granularity SL Interpretation Enhancement and Mechanism Analysis, which aids experts in organizing and comparing predictions and interpretive paths across different granularities, uncovering new SL relationships, enhancing result interpretation, and elucidating potential mechanisms inferred by Graph Neural Network (GNN) models. These components cyclically optimize model predictions and mechanism explorations, enhancing expert involvement and intervention to build trust. Facilitated by SLInterpreter, this framework ensures that newly generated interpretive paths increasingly align with domain knowledge and adhere more closely to real-world biological principles through iterative Human-AI collaboration. We evaluate the framework's efficacy through a case study and expert interviews.

cs.HC↗

arXiv2024

Tissue-Contrastive Semi-Masked Autoencoders for Segmentation Pretraining on Chest CT

Existing Masked Image Modeling (MIM) depends on a spatial patch-based masking-reconstruction strategy to perceive objects'features from unlabeled images, which may face two limitations when applied to chest CT: 1) inefficient feature learning due to complex anatomical details presented in CT images, and 2) suboptimal knowledge transfer owing to input disparity between upstream and downstream models. To address these issues, we propose a new MIM method named Tissue-Contrastive Semi-Masked Autoencoder (TCS-MAE) for modeling chest CT images. Our method has two novel designs: 1) a tissue-based masking-reconstruction strategy to capture more fine-grained anatomical features, and 2) a dual-AE architecture with contrastive learning between the masked and original image views to bridge the gap of the upstream and downstream models. To validate our method, we systematically investigate representative contrastive, generative, and hybrid self-supervised learning methods on top of tasks involving segmenting pneumonia, mediastinal tumors, and various organs. The results demonstrate that, compared to existing methods, our TCS-MAE more effectively learns tissue-aware representations, thereby significantly enhancing segmentation performance across all tasks.

eess.IV↗

arXiv2024

Joint observations of late universe probes: cosmological parameter constraints from gravitational wave and Type Ia supernova data

The growing tensions between the early Universe and the late Universe increasingly highlight the importance of developing precise probes for late cosmology. As significant late-Universe probes, Type Ia supernovae (SNe Ia) and gravitational waves (GWs) can provide measurements of relative and absolute distances, respectively. Their complementary nature is likely to break the degeneracies among cosmological parameters, thereby yielding more precise constraints. In this study, we use 43 gravitational-wave sources from the Third LIGO-Virgo-KAGRA Gravitational-Wave Transient Catalog (GWTC-3) and 1590 SNe Ia from Pantheon+ compilation to constrain the dark energy models, as an attempt to achieve precise late-Universe cosmological constraints. For the dark siren GW event, we estimate the corresponding redshift using the binary black hole redshift distribution model. The combination of GW and SNe Ia data could provide the precision on the Hubble constant (H0) and the present matter density (Omega_m) of approximately 20% and 8% for the LambdaCDM model. If we consider the equation of state of dark energy (w), the combination sample constrains the precision of w to approximately 30%. Although the combination of GW and SNe Ia observations effectively breaks degeneracies among various cosmological parameters, yielding more stringent constraints, the precision of these constraints still does not meet the stringent standards required by precision cosmology. However, it is reasonable to anticipate that, in the near future, the joint observations of GWs and SNe Ia will become a powerful tool, particularly in the late Universe, for the precise measurement of cosmological parameters.

astro-ph.CO↗