arXiv subjects

Nan Li

Publications and source records attributed to Nan Li.

At least 91 records · Page 5Linked to original sources

arXiv2025

CSST Strong Lensing Preparation: Cosmological Constraints Forecast from CSST Galaxy-Scale Strong Lensing

Strong gravitational lensing by galaxies is a powerful tool for studying cosmology and galaxy structure. The China Space Station Telescope (CSST) will revolutionize this field by discovering up to $\sim$100,000 galaxy-scale strong lenses, a huge increase over current samples. To harness the statistical power of this vast dataset, we forecast its cosmological constraining power using the gravitational-dynamical mass combination method. We create a realistic simulated lens sample and test how uncertainties in redshift and velocity dispersion measurements affect results under ideal, optimistic, and pessimistic scenarios. We find that increasing the sample size from 100 to 10,000 systems dramatically improves precision: in the $Λ$CDM model, the uncertainty on the matter density parameter, $Ω_m$, drops from 0.2 to 0.01; in the $w$CDM model, the uncertainty on the dark energy equation of state, $w$, decreases from 0.3 to 0.04. With 10,000 lenses, our constraints on dark energy are twice as tight as those from the latest DESI BAO measurements. We also compare two parameter estimation techniques -- MultiNest sampling and Bayesian Hierarchical Modeling (BHM). While both achieve similar precision, BHM provides more robust estimates of intrinsic lens parameters, whereas MultiNest is about twice as fast. This work establishes an efficient and scalable framework for cosmological analysis with next-generation strong lensing surveys.

astro-ph.CO↗

arXiv2025

Mock Observations for the CSST Mission: Multi-Channel Imager--The Cluster Field

The Multi-Channel Imager (MCI), one of the instruments aboard the China Survey Space Telescope (CSST), is designed to simultaneously observe the sky in three filters, covering wavelengths from the near-ultraviolet (NUV) to the near-infrared (NIR). With its large field of view ($7.5^{\prime}\times7.5^{\prime}$), MCI is particularly well-suited for observing galaxy clusters, providing a powerful tool for investigating galaxy evolution, dark matter and dark energy through gravitational lensing. Here we present a comprehensive simulation framework of a strong lensing cluster as observed by MCI, aiming to fully exploit its capabilities in capturing lensing features. The framework simulates a strong lensing cluster from the CosmoDC2 catalog, calculating the gravitational potential and performing ray-tracing to derive the true positions, shapes and light distribution of galaxies within the cluster field. Additionally, the simulation incorporates intra-cluster light (ICL) and spectral energy distributions (SEDs), enabling further strong lensing analyses, such as ICL seperation from galaxy light and mass reconstruction combining strong and weak lensing measurements. This framework provides a critical benchmark for testing the MCI data pipeline and maximizing its potential in galaxy cluster research.

astro-ph.IM↗

arXiv2025

Mock Observations for the CSST Mission: End-to-End Performance Modeling of Optical System

This study presents a comprehensive end-to-end simulation analysis of the optical imaging performance of the China Survey Space Telescope (CSST) under in-orbit conditions. An integrated system model incorporating five static and two dynamic error sub-models was established. Wavefront errors were calculated for each sub-model and compared to the integrated system error to quantify the individual contributions to image degradation. At the detector level, wavefront error, point spread function (PSF), and ellipticity were evaluated across the full field of view (FOV). The average radius of 80\% encircled energy (REE80) of the PSF under full-error conditions was determined for 25 field points, yielding a value of 0.114 arcseconds. Furthermore, the calculations indicate a correlation between the wavefront distribution and the ellipticity distribution within the optical system. By optimizing the wavefront distribution, it is possible to adjust the ellipticity distribution of the PSF across the full FOV. The end-to-end simulation approach adopted in this paper provides a theoretical foundation for improving the image quality in large-aperture, off-axis space telescopes.

astro-ph.IM↗

arXiv2025

Mock Observations for the CSST Mission: Main Surveys--An Overview of Framework and Simulation Suite

The Chinese Space Station Survey Telescope (CSST) is a flagship space-based observatory. Its main survey camera is designed to conduct high spatial resolution near-ultraviolet to near-infrared imaging and low-resolution spectroscopic surveys. To maximize the scientific output of CSST, we have developed a comprehensive, high-fidelity simulation pipeline for reproducing both imaging and spectroscopic observations. This paper presents an overview of the simulation framework, detailing its implementation and components. Built upon the GalSim package and incorporating the latest CSST instrumental specifications, our pipeline generates pixel-level mock observations that closely replicate the expected instrumental and observational conditions. The simulation suite integrates realistic astrophysical object catalogs, instrumental effects, point spread function (PSF) modeling, and observational noises to produce accurate synthetic data. We describe the key processing stages of the simulation, from constructing the input object catalogs to modeling the telescope optics and detector responses. Furthermore, we introduce the most recent release of simulated datasets, which provide a crucial testbed for data processing pipeline developments, calibration strategies, and scientific analyses, ensuring that CSST will meet its stringent requirements. Our pipeline serves as a vital tool for optimizing CSST main survey strategies and ensuring robust cosmological measurements.

astro-ph.IM↗

arXiv2025

The Size Evolution and the Size-Mass Relation of Lyman-Alpha Emitters across $3 \lesssim z < 7$ as Observed by JWST

Understanding the morphological structures of Lyman-alpha emitters (LAEs) is crucial for unveiling their formation pathways and the physical origins of Ly$α$ emission. However, the evolution of their sizes and structural scaling relations remains debated. In this study, we analyze a large sample of 876 spectroscopically confirmed LAEs at $3 \lesssim z < 7$, selected from the MUSE, VANDELS, and CANDELSz7 surveys in the GOODS-S, UDS, and COSMOS fields. Utilizing James Webb Space Telescope (JWST) NIRCam imaging data, we measure their rest-frame UV and optical V-band effective radii ($R_{\rm e}$) through two-dimensional Sérsic profile fitting. Our results show that these LAEs are generally compact, with a median $R_{\rm e,UV}$ of 0.50$^{+0.30}_{-0.24}$ kpc and a median $R_{\rm e,V}$ of 0.57$^{+0.33}_{-0.24}$ kpc. The size evolution follows $R_{\rm e,UV} \propto (1 + z)^{-0.91 \pm 0.10}$ and $R_{\rm e,V} \propto (1 + z)^{-0.93 \pm 0.18}$, respectively. Their UV and optical sizes are statistically comparable, indicating negligible UV-to-optical color gradients. For the first time, we establish the rest-frame optical size-mass relation for LAEs at $z>3$, finding slopes comparable to typical star-forming galaxies (SFGs), but with slightly smaller sizes at a given stellar mass. These results provide important clues for understanding structural evolution of LAEs in the early universe.

astro-ph.GA↗

arXiv2025

Direct and mediated dipole-dipole interactions in a reconfigurable array of optical traps

Optically levitated nanoparticles in vacuum experience both electrostatic and light-induced dipole-dipole interactions, offering a versatile platform to explore mesoscopic entanglement and many-body dynamics. A significant challenge in optical trap arrays is to achieve site-resolved, point-to-point tunability: adjusting the laser parameters of a single trap typically induces global cross-talk to neighboring sites, hindering independent control. Inspired by tunable couplers in superconducting circuits, we implement an ancillary nanoparticle that functions as a coupler between two target nanoparticles. Within a reconfigurable three-particle array, we demonstrate broad tunability of the direct dipole-dipole interaction by controlling the phase and position of the traps. In addition, we observe spectral signatures consistent with mediated interactions between the target particles via the ancillary one, manifested as mode participation beyond the uncoupled response. Our results establish a practical route to tailored, site-resolved control in multi-particle optical trap arrays, expanding the optical-binding toolbox and opening opportunities for programmable oscillator networks relevant to macroscopic quantum mechanics and precision sensing.

physics.optics↗

arXiv2025

C-RAN Advanced: From a Network Cooperation Perspective

Future mobile networks in the sixth generation (6G) are poised for a paradigm shift from conventional communication services toward comprehensive information services, driving the evolution of radio access network (RAN) architectures toward enhanced cooperation, intelligence, and service orientation. Building upon the concept of centralized, collaborative, cloud, and clean RAN (C-RAN), this article proposes a novel cooperative, intelligent, and service-based RAN (CIS-RAN) architecture. Focusing on cooperation, CIS-RAN extends the traditional cooperative communication paradigm by further integrating cooperative sensing and cooperative artificial intelligence (AI). To improve both performance and effectiveness across diverse application scenarios, CIS-RAN enhances network cooperation throughout the entire process of acquisition, transmission, and processing, thereby enabling efficient information acquisition, diverse cooperative interactions, and intelligent fusion decision-making. Key technologies are discussed, with network cooperative multiple-input multiple-output (MIMO) examined as a case study, demonstrating superior performance over traditional architectures, as demonstrated by numerical results. Future research directions are outlined, emphasizing the continued exploration and advancement of the CIS-RAN architecture, particularly in enhancing network cooperation.

eess.SP↗

arXiv2025

CSST Slitless Spectra: Target Detection and Classification with YOLO

Addressing the spatial uncertainty and spectral blending challenges in CSST slitless spectroscopy, we present a deep learning-driven, end-to-end framework based on the You Only Look Once (YOLO) models. This approach directly detects, classifies, and analyzes spectral traces from raw 2D images, bypassing traditional, error-accumulating pipelines. YOLOv5 effectively detects both compact zero-order and extended first-order traces even in highly crowded fields. Building on this, YOLO11 integrates source classification (star/galaxy) and discrete astrophysical parameter estimation (e.g., redshift bins), showcasing complete spectral trace analysis without other manual preprocessing. Our framework processes large images rapidly, learning spectral-spatial features holistically to minimize errors. We achieve high trace detection precision (YOLOv5) and demonstrate successful quasar identification and binned redshift estimation (YOLO11). This study establishes machine learning as a paradigm shift in slitless spectroscopy, unifying detection, classification, and preliminary parameter estimation in a scalable system. Future research will concentrate on direct, continuous prediction of astrophysical parameters from raw spectral traces.

astro-ph.IM↗

arXiv2025

TernaryCLIP: Efficiently Compressing Vision-Language Models with Ternary Weights and Distilled Knowledge

Recent years have witnessed an increasing interest in image-text contrastive modeling, exemplified by models such as Contrastive Language-Image Pretraining (CLIP). In this paper, we propose the TernaryCLIP, a lightweight computational framework that converts connection weights of both vision and text encoders of CLIP into the ternary format, instead of full-precision or floating ones. TernaryCLIP incorporates quantization-aware training and distillation modules, preventing precision degradation and enabling low-cost and high-efficiency computations. Comprehensive experiments demonstrate that TernaryCLIP can achieve up to 99\% ternarized weights with 1.58-bit representation, 16.98 $\times$ compression ratio, 2.3 $\times$ inference acceleration, 16 $\times$ storage reduction, 10 $\times$ memory optimization, and 60\% sparsity while maintaining promising performance on zero-shot image classification and image-text retrieval tasks across 41 commonly used datasets. Our work highlights the feasibility of extreme quantization for large multimodal models, supporting effective and efficient deployment on resource-constrained devices. The model and code can be accessed from Hugging Face and GitHub.

cs.CV↗

arXiv2025

Pixel-level modelling of group-scale strong lens CASSOWARY 19

We present the first high-precision model for the group-scale strong lensing system CASSOWARY 19 (CSWA19), utilising images from the Hubble Space Telescope (HST). Sixteen member galaxies identified via the red-sequence method, and the main halo, all modelled as the dual Pseudo Isothermal Elliptical profile (dPIE), are incorporated into a parametric lens model alongside an external shear field. To model the system, we adopt the PyAutoLens software package, employing a progressive search chain strategy for realizing the transition of source model from multiple Sérsic profiles to a brightness-adaptive pixelization, which uses 1000 pixels in the source plane to reconstruct the background source corresponding to 177,144 image pixels in the image plane. Our results indicate that the total mass within the Einstein radius is $M_{θ_\mathrm{E}}$ $\approx 1.41\times10^{13}$M$_{\odot}$ and the average slope of the total mass density $ρ(r)\propto r^{-γ}$ is $\tildeγ=1.33$ within the effective radius. This slope is shallower than those measured in galaxies and groups but is closer to those of galaxy clusters. In addition, our approach successfully resolves the two merging galaxies in the background source and yields a total magnification of $μ=103.18^{+0.23}_{-0.19}$, which is significantly higher than the outcomes from previous studies of CSWA19. In summary, our research demonstrates the effectiveness of the brightness-adaptive pixelization source reconstruction technique for modelling group-scale strong lensing systems. It can serve as a technical reference for future investigations into pixel-level modelling of the group- and cluster-scale strong lensing systems.

astro-ph.GA↗

arXiv2025

Spec-Driven AI for Science: The ARIA Framework for Automated and Reproducible Data Analysis

The rapid expansion of scientific data has widened the gap between analytical capability and research intent. Existing AI-based analysis tools, ranging from AutoML frameworks to agentic research assistants, either favor automation over transparency or depend on manual scripting that hinders scalability and reproducibility. We present ARIA (Automated Research Intelligence Assistant), a spec-driven, human-in-the-loop framework for automated and interpretable data analysis. ARIA integrates six interoperable layers, namely Command, Context, Code, Data, Orchestration, and AI Module, within a document-centric workflow that unifies human reasoning and machine execution. Through natural-language specifications, researchers define analytical goals while ARIA autonomously generates executable code, validates computations, and produces transparent documentation. Beyond achieving high predictive accuracy, ARIA can rapidly identify optimal feature sets and select suitable models, minimizing redundant tuning and repetitive experimentation. In the Boston Housing case, ARIA discovered 25 key features and determined XGBoost as the best performing model (R square = 0.93) with minimal overfitting. Evaluations across heterogeneous domains demonstrate ARIA's strong performance, interpretability, and efficiency compared with state-of-the-art systems. By combining AI for research and AI for science principles within a spec-driven architecture, ARIA establishes a new paradigm for transparent, collaborative, and reproducible scientific discovery.

cs.AI↗

arXiv2025

CURLING -- II. Improvement on the $H_{0}$ Inference from Pixelized Cluster Strong Lens Modeling

Strongly lensed supernovae (glSNe) provide a powerful, independent method to measure the Hubble constant, $H_{0}$, through time delays between their multiple images. The accuracy of this measurement depends critically on both the precision of time delay estimation and the robustness of lens modeling. In many current cluster-scale modeling algorithms, all multiple images used for modeling are simplified as point sources to reduce computational costs. In the first paper of the CURLING program, we demonstrated that such a point-like approximation can introduce significant uncertainties and biases in both magnification reconstruction and cosmological inference. In this study, we explore how such simplifications affect $H_0$ measurements from glSNe. We simulate a lensed supernova at $z=1.95$, lensed by a galaxy cluster at $z=0.336$, assuming time delays are measured from LSST-like light curves. The lens model is constructed using JWST-like imaging data, utilizing both Lenstool and a pixelated method developed in CURLING. Under a fiducial cosmology with $H_0=70\rm \ km \ s^{-1}\ Mpc^{-1}$, the Lenstool model yields $H_0=69.91^{+6.27}_{-5.50}\rm \ km\ s^{-1}\ Mpc^{-1}$, whereas the pixelated framework improves the precision by over an order of magnitude, $H_0=70.39^{+0.82}_{-0.60}\rm \ km \ s^{-1}\ Mpc^{-1}$. Our results indicate that in the next-generation observations (e.g., JWST), uncertainties from lens modeling dominate the error budget for $H_0$ inference, emphasizing the importance of incorporating the extended surface brightness of multiple images to fully leverage the potential of glSNe for cosmology.

astro-ph.CO↗

arXiv2025

Fabrication and Characterization of X-ray TES Detectors Based on Annular AlMn Alloy Films

AlMn alloy flms are widely fabricated into superconducting transition edge sensors (TESs) for the detection of cosmic microwave background radiation. However, the application in X-ray or gamma-ray detection based on AlMn TES is rarely reported. In this study, X-ray TES detectors based on unique annular AlMn flms are devel-oped. The fabrication processes of TES detectors are introduced in detail. The char-acteristics of three TES samples are evaluated in a dilution refrigerator. The results demonstrate that the I-V characteristics of the three annular TES detectors are highly consistent. The TES detector with the smallest absorber achieved the best energy resolution of 11.0 eV @ 5.9 keV, which is inferior to the theoretical value. The dis-crepancy is mainly attributed to the larger readout electronics noise than expected.

physics.ins-det↗

arXiv2025

Introduction to the Chinese Space Station Survey Telescope (CSST)

The Chinese Space Station Survey Telescope (CSST) is an upcoming Stage-IV sky survey telescope, distinguished by its large field of view (FoV), high image quality, and multi-band observation capabilities. It can simultaneously conduct precise measurements of the Universe by performing multi-color photometric imaging and slitless spectroscopic surveys. The CSST is equipped with five scientific instruments, i.e. Multi-band Imaging and Slitless Spectroscopy Survey Camera (SC), Multi-Channel Imager (MCI), Integral Field Spectrograph (IFS), Cool Planet Imaging Coronagraph (CPI-C), and THz Spectrometer (TS). Using these instruments, CSST is expected to make significant contributions and discoveries across various astronomical fields, including cosmology, galaxies and active galactic nuclei (AGN), the Milky Way and nearby galaxies, stars, exoplanets, Solar System objects, astrometry, and transients and variable sources. This review aims to provide a comprehensive overview of the CSST instruments, observational capabilities, data products, and scientific potential.

astro-ph.IM↗

arXiv2025

Building Data-Driven Occupation Taxonomies: A Bottom-Up Multi-Stage Approach via Semantic Clustering and Multi-Agent Collaboration

Creating robust occupation taxonomies, vital for applications ranging from job recommendation to labor market intelligence, is challenging. Manual curation is slow, while existing automated methods are either not adaptive to dynamic regional markets (top-down) or struggle to build coherent hierarchies from noisy data (bottom-up). We introduce CLIMB (CLusterIng-based Multi-agent taxonomy Builder), a framework that fully automates the creation of high-quality, data-driven taxonomies from raw job postings. CLIMB uses global semantic clustering to distill core occupations, then employs a reflection-based multi-agent system to iteratively build a coherent hierarchy. On three diverse, real-world datasets, we show that CLIMB produces taxonomies that are more coherent and scalable than existing methods and successfully capture unique regional characteristics. We release our code and datasets at https://anonymous.4open.science/r/CLIMB.

cs.AI↗

arXiv2025

Electric Dipole Moments from Axion-Like Particle Dark Matter Background

New one- and two-loop contributions to the lepton's and nucleon's EDM, which are induced by an axion-like particle dark matter background, are examined. These contributions include effects from CP-violating ALP interactions with photons, leptons and nucleons. The contribution to the EDM is so larger it leads to new constraints on the CP violating couplings of axion-like particles, if the axion-like particle mass is smaller than $10^{-11} $ eV.

hep-ph↗

arXiv2025

GEMINUS: Dual-aware Global and Scene-Adaptive Mixture-of-Experts for End-to-End Autonomous Driving

End-to-end autonomous driving requires adaptive and robust handling of complex and diverse traffic environments. However, prevalent single-mode planning methods attempt to learn an overall policy while struggling to acquire diversified driving skills to handle diverse scenarios. Therefore, this paper proposes GEMINUS, a Mixture-of-Experts end-to-end autonomous driving framework featuring a Global Expert and a Scene-Adaptive Experts Group, equipped with a Dual-aware Router. Specifically, the Global Expert is trained on the overall dataset, possessing robust performance. The Scene-Adaptive Experts are trained on corresponding scene subsets, achieving adaptive performance. The Dual-aware Router simultaneously considers scenario-level features and routing uncertainty to dynamically activate expert modules. Through the effective coupling of the Global Expert and the Scene-Adaptive Experts Group via the Dual-aware Router, GEMINUS achieves both adaptability and robustness across diverse scenarios. GEMINUS outperforms existing methods in the Bench2Drive closed-loop benchmark and achieves state-of-the-art performance in Driving Score and Success Rate, even with only monocular vision input. The code is available at https://github.com/newbrains1/GEMINUS.

cs.CV↗

arXiv2025

DeMeVa at LeWiDi-2025: Modeling Perspectives with In-Context Learning and Label Distribution Learning

This system paper presents the DeMeVa team's approaches to the third edition of the Learning with Disagreements shared task (LeWiDi 2025; Leonardelli et al., 2025). We explore two directions: in-context learning (ICL) with large language models, where we compare example sampling strategies; and label distribution learning (LDL) methods with RoBERTa (Liu et al., 2019b), where we evaluate several fine-tuning methods. Our contributions are twofold: (1) we show that ICL can effectively predict annotator-specific annotations (perspectivist annotations), and that aggregating these predictions into soft labels yields competitive performance; and (2) we argue that LDL methods are promising for soft label predictions and merit further exploration by the perspectivist community.

cs.CL↗