arXiv subjects

Yan Gong

Publications and source records attributed to Yan Gong.

At least 55 records · Page 3Linked to original sources

arXiv2025

MCI: Multi-Channel Imager on the Chinese Space Station Survey Telescope

The Multi-Channel Imager (MCI) is a powerful near-ultraviolet (NUV) and visible imager onboard the Chinese Space Station Survey Telescope (CSST). The MCI provides three imaging channels, which are the NUV channel, the Blue channel and the Red channel, with the wavelength range of 255-430 nm, 430-700 nm, and 700-1000 nm, respectively. MCI's three channels can target the same field simultaneously, which is unique compared to other imagers onboard the Hubble Space Telescope (HST) or the James Webb Space Telescope (JWST). Each channel employs a CCD focal plane of 9216 x 9232 pixels and $\sim$7\arcmin.5 x 7\arcmin.5 field of view (FOV), which are about $\gtrsim 4$ times greater than the FOVs of HST imagers. The MCI's three channels feature unprecedented sensitivities and field of views complement the NUV and visible capabilities of the CSST for high-precision photometry and weak-signal detection, which would help build a new standard-star system and the deepest UV-Optical exposures for CSST. Rich filter sets of MCI would help explore other sciences such as local emission line mapping, high-z Ly$α$ emitters searching, etc. Here we present key design features, results of current ground tests, and suggested observing strategies of the MCI.

astro-ph.IM↗

arXiv2025

Forecasting the Constraint on the Hu-Sawicki $f(R)$ Modified Gravity in the CSST $3\times2$pt Photometric Survey

We forecast the constraint on the Hu-Sawicki $f(R)$ model from the photometric survey operated by the Chinese Space Station Survey Telescope (CSST). The simulated $3\times2$pt data of galaxy clustering, weak lensing, and galaxy-galaxy lensing measurements within 100 deg$^{2}$ are used in the analysis. The mock observational maps are constructed from a light cone, redshift sampling and noise. The angular power spectra are measured with pseudo-$C_\ell$ estimators and compared to theory in the same basis using validated weighting functions and an analytic covariance matrix that includes Gaussian, connected non-Gaussian, and super-sample terms. We model the theoretical spectra using two methods. The first one uses MGCAMB to compute the linear modified-gravity clustering power spectra, and the second one adopts the FREmu emulator with a baseline of nonlinear $Λ$CDM prescription. Parameter inference is performed with Cobaya, and the cosmological and modified-gravity parameters are sampled within the emulator training domain, which is jointly fitted with the systematic parameters. We find that the predictions from the two methods are in good agreement at the overlapping large scales, and the emulator method can correctly provide additional high-$\ell$ information. The $1σ$ upper bounds of $\log_{10}|f_{R0}|$ are found to be $<-5.42$ for cosmic shear only case and $<-5.29$ for the 100 deg$^2$ CSST $3\times2$pt probe. The full CSST photometric survey with 17,500 deg$^2$ survey area is expected to further improve the constraint precision by about one order of magnitude. Our results demonstrate that the CSST $3\times2$pt survey can deliver strict tests on $f(R)$ gravity.

astro-ph.CO↗

arXiv2025

The Milky Way Imaging Scroll Painting Survey: Data Release 1

We present the first data release (DR1) of the Milky Way Imaging Scroll Painting (MWISP) survey, a mapping in the J=(1-0) transition lines of 12CO, 13CO, and C18O toward the northern Galactic plane during 2011-2022. The MWISP survey was conducted using the PMO 13.7 m telescope at a spatial resolution of approximately 50" and a velocity resolution of 0.16 km/s at 115 GHz. DR1 fully covered 2310 square degrees within the Galactic longitude (l) and latitude (b) range of 9.75 deg =< l =< 229.75 deg and |b| =< 5.25 deg. The surveyed area was divided into cell units of 30'x30' for practical purposes and On-The-Fly (OTF) mapping was performed toward each target cell unit. The data were regridded into a regular 3D datacube in l-b-V_LSR with a pixel size of 30" in l-b axes and 0.16 km/s in theV_LSR axis. The median rms noise is 0.47 K, 0.25 K, and 0.25 K for 12CO, 13CO, and C18O, respectively. The equivalent 3 sigma sensitivity in 12CO luminosity is approximately 0.23 K km/s, making MWISP the most sensitive survey of its kind. In this paper, we describe the survey data, including the calibration, data cleaning, data mosaic processes, and the data products. The final mosaicked data cubes contain about 3.33x10^7 spectra (pixels) for each CO isotopologue line. Color composite images, made from the intensities of the isotopologue lines, and some concise descriptions are provided. We constructed a molecular cloud catalog based on the mosaicked 12CO data cube using the clustering algorithm DBSCAN, detecting 103,517 molecular clouds, 10,790 of which exhibit 13CO emission and 304 of which show C18O emission. Based on the histogram of voxel brightness temperature, we estimated a total 12CO flux of 7.69+/-0.38x10^7 K km/s arcmin^2, 82% of which is captured by the DBSCAN algorithm. The data, together with the cloud sample, provide unique information on molecular gas in the northern Milky Way.

astro-ph.GA↗

arXiv2025

Photo-$z$ Estimation with Normalizing Flow

Accurate photometric redshift (photo-$z$) estimation is a key challenge in cosmology, as uncertainties in photo-$z$ directly limit the scientific return of large-scale structure and weak lensing studies, especially in upcoming Stage IV surveys. The problem is particularly severe for faint galaxies with sparse spectroscopic training data. In this work, we introduce nflow-$z$, a novel photo-$z$ estimation method using the powerful machine learning technique of normalizing flow. nflow-$z$ explicitly models the redshift probability distribution conditioned on the observables such as fluxes and colors. We build two nflow-$z$ implementations, dubbed cINN and cNSF, and compare their performance. We demonstrate the effectiveness of nflow-$z$ on several datasets, including a CSST mock, the COSMOS2020 catalog, and samples from DES Y1, SDSS, and DESCaLS. Our evaluation against state-of-the-art algorithms shows that nflow-$z$ performs favorably. For instance, cNSF surpasses Random Forest, Multi-Layer Perceptron, and Convolutional Neutral Network on the CSST mock test. We also achieve a ~30% improvement over official results for the faint DESCaLS sample and outperform conditional Generative Adversarial Network and Mixture Density Network methods on the DES Y1 dataset test. Furthermore, nflow-$z$ is computationally efficient, requiring only a fraction of the computing time of some of the competing algorithms. Our algorithm is particularly effective for the faint sample with sparse training data, making it highly suitable for upcoming Stage IV surveys.

astro-ph.IM↗

arXiv2025

DiffPixelFormer: Differential Pixel-Aware Transformer for RGB-D Indoor Scene Segmentation

Indoor semantic segmentation is fundamental to computer vision and robotics, supporting applications such as autonomous navigation, augmented reality, and smart environments. Although RGB-D fusion leverages complementary appearance and geometric cues, existing methods often depend on computationally intensive cross-attention mechanisms and insufficiently model intra- and inter-modal feature relationships, resulting in imprecise feature alignment and limited discriminative representation. To address these challenges, we propose DiffPixelFormer, a differential pixel-aware Transformer for RGB-D indoor scene segmentation that simultaneously enhances intra-modal representations and models inter-modal interactions. At its core, the Intra-Inter Modal Interaction Block (IIMIB) captures intra-modal long-range dependencies via self-attention and models inter-modal interactions with the Differential-Shared Inter-Modal (DSIM) module to disentangle modality-specific and shared cues, enabling fine-grained, pixel-level cross-modal alignment. Furthermore, a dynamic fusion strategy balances modality contributions and fully exploits RGB-D information according to scene characteristics. Extensive experiments on the SUN RGB-D and NYUDv2 benchmarks demonstrate that DiffPixelFormer-L achieves mIoU scores of 54.28% and 59.95%, outperforming DFormer-L by 1.78% and 2.75%, respectively. Code is available at https://github.com/gongyan1/DiffPixelFormer.

cs.CV↗

arXiv2025

Mock Observations for the CSST Mission: HSTDM--Synthetic Data Generation

The High Sensitivity Terahertz Detection Module (HSTDM), a key component of the backend modules on board the China Space Station Telescope (CSST), will offer great opportunities for the discovery of Terahertz Astronomy, with implications that extend well beyond China to the global astronomical community. It is imperative that the raw data collected by HSTDM undergoes meticulous calibration and processing through the HSTDM data processing pipeline (HSTDM pipeline for short) to ensure the accuracy and effectiveness of the final science data to be archived for further research. This process necessitates that the HSTDM pipeline address instrumental artifacts and effects as well as the coordination of data flow of the scheduled observing sequences under all observing modes of HSTDM within the CSST automated processing environment. As the understanding of CSST HSTDM data processing develops during the pipeline development stage, it becomes essential to assess the accuracy, the robustness and the performance of the HSTDM pipeline under all observing modes of HSTDM so that components of the HSTDM pipeline be rationally added, removed, amended or extended within the modular framework. In this paper, we develop practical simulation methods to facilitate this need. The contribution of synthetic data generation of HSTDM observation includes two parts: 1. HSTDM instrumental effect simulation based on both real testing profiles and simulated models; 2. Observing data flow generation based on HSTDM observing mode scenario. The simulation methods have been implemented and shown to be practical in testing the HSTDM pipeline during the development stage.

astro-ph.IM↗

arXiv2025

DGFusion: Dual-guided Fusion for Robust Multi-Modal 3D Object Detection

As a critical task in autonomous driving perception systems, 3D object detection is used to identify and track key objects, such as vehicles and pedestrians. However, detecting distant, small, or occluded objects (hard instances) remains a challenge, which directly compromises the safety of autonomous driving systems. We observe that existing multi-modal 3D object detection methods often follow a single-guided paradigm, failing to account for the differences in information density of hard instances between modalities. In this work, we propose DGFusion, based on the Dual-guided paradigm, which fully inherits the advantages of the Point-guide-Image paradigm and integrates the Image-guide-Point paradigm to address the limitations of the single paradigms. The core of DGFusion, the Difficulty-aware Instance Pair Matcher (DIPM), performs instance-level feature matching based on difficulty to generate easy and hard instance pairs, while the Dual-guided Modules exploit the advantages of both pair types to enable effective multi-modal feature fusion. Experimental results demonstrate that our DGFusion outperforms the baseline methods, with respective improvements of +1.0\% mAP, +0.8\% NDS, and +1.3\% average recall on nuScenes. Extensive experiments demonstrate consistent robustness gains for hard instance detection across ego-distance, size, visibility, and small-scale training scenarios.

cs.CV↗

arXiv2025

GEMMA-SQL: A Novel Text-to-SQL Model Based on Large Language Models

Text-to-SQL systems enable users to interact with structured databases using natural language, eliminating the need for specialized programming knowledge. In this work, we introduce GEMMA-SQL, a lightweight and efficient text-to-SQL model built upon the open-source Gemma 2B architecture. Unlike many large language models (LLMs), GEMMA-SQL is fine-tuned in a resource-efficient, iterative manner and can be deployed on low-cost hardware. Leveraging the SPIDER benchmark for training and evaluation, GEMMA-SQL combines multiple prompting strategies, including few-shot learning, to enhance SQL query generation accuracy. The instruction-tuned variant, GEMMA-SQL Instruct, achieves 66.8% Test-Suite accuracy and 63.3% Exact Set Match accuracy, outperforming several state-of-the-art baselines such as IRNet, RYANSQL, and CodeXDavinci. The proposed approach demonstrates that effective prompt design and targeted instruction tuning can significantly boost performance while maintaining high scalability and adaptability. These results position GEMMA-SQL as a practical, open-source alternative for robust and accessible text-to-SQL systems.

cs.CL↗

arXiv2025

Oxygen isotopes reveal low-mass star dominance in the Small Magellanic Cloud

Oxygen isotope abundances and their ratios are fingerprints of stellar evolution and therefore provide a powerful tool in tracing the enrichment history of galaxies. However, their behavior in low-metallicity dwarf galaxies remains largely unexplored. The Small Magellanic Cloud (SMC), a nearby analog of young high-redshift galaxies, offers an ideal laboratory to investigate this regime. Using the Atacama Compact Array, we observed the $J=2\to 1$ transitions of $^{12}$CO, $^{13}$CO, C$^{18}$O, and C$^{17}$O from the massive star-forming region LIRS~36 (aka N12A), achieving the first detection of C$^{17}$O in the SMC. This detection enables the first direct measurement of the $^{18}$O/$^{17}$O abundance ratio of 0.87$\pm$0.26 in this galaxy, substantially lower than all values in the literature, including molecular clouds in the Milky Way and other galaxies. Such a low ratio of $^{18}$O/$^{17}$O, together with a high $^{13}$CO/C$^{18}$O ratio, indicates chemical enrichment dominated by low-mass stars, consistent with the observed paucity of high-mass stars in the SMC. We suggest that the SMC is governed by a top-light integrated galaxy-wide initial mass function, predicted by the SMC's persistently low star-formation activities.

astro-ph.GA↗

arXiv2025

Text-Enhanced Panoptic Symbol Spotting in CAD Drawings

With the widespread adoption of Computer-Aided Design(CAD) drawings in engineering, architecture, and industrial design, the ability to accurately interpret and analyze these drawings has become increasingly critical. Among various subtasks, panoptic symbol spotting plays a vital role in enabling downstream applications such as CAD automation and design retrieval. Existing methods primarily focus on geometric primitives within the CAD drawings to address this task, but they face following major problems: they usually overlook the rich textual annotations present in CAD drawings and they lack explicit modeling of relationships among primitives, resulting in incomprehensive understanding of the holistic drawings. To fill this gap, we propose a panoptic symbol spotting framework that incorporates textual annotations. The framework constructs unified representations by jointly modeling geometric and textual primitives. Then, using visual features extract by pretrained CNN as the initial representations, a Transformer-based backbone is employed, enhanced with a type-aware attention mechanism to explicitly model the different types of spatial dependencies between various primitives. Extensive experiments on the real-world dataset demonstrate that the proposed method outperforms existing approaches on symbol spotting tasks involving textual annotations, and exhibits superior robustness when applied to complex CAD drawings.

cs.CV↗

arXiv2025

Detecting dark matter substructure with lensed quasars in optical bands

Flux ratios of multiple images in strong gravitational lensing systems provide a powerful probe of dark matter substructure. Optical flux ratios of lensed quasars are typically affected by stellar microlensing, and thus studies of dark matter substructure often rely on emission regions that are sufficiently extended to avoid microlensing effects. To expand the accessible wavelength range for studying dark matter substructure through flux ratios and to reduce reliance on specific instruments, we confront the challenges posed by microlensing and propose a method to detect dark matter substructure using optical flux ratios of lensed quasars. We select 100 strong lensing systems consisting of 90 doubles and 10 quads to represent the overall population and adopt the Kolmogorov--Smirnov (KS) test as our statistical method. By introducing different types of dark matter substructure into these strong lensing systems, we demonstrate that using quads alone provides the strongest constraints on dark matter and that several tens to a few hundred independent flux ratio measurements from quads can be used to study the properties of dark matter substructure and place constraints on dark matter parameters. Furthermore, we suggest that the use of multi-band flux ratios can substantially reduce the required number of quads. Such sample sizes will be readily available from ongoing and upcoming wide-field surveys.

astro-ph.CO↗

arXiv2025

The Diamond Ring in Cygnus X: Advanced stage of an expanding bubble of ionised carbon

The "Diamond Ring" in Cygnus X, southwest of the DR21 ridge, is a nearly circular structure of $\sim$6 pc in diameter, prominent in FIR emission and enclosed by clumpy molecular clouds traced in CO. It hosts an HII region, visible in cm emission, and resembles a classical expanding HII bubble routinely seen in the 158 $μ$m [CII] line. However, SOFIA FEEDBACK observations in the spectrally resolved [CII] line reveal instead a slightly tilted ring of $\sim$10$^3$ M$_\odot$ expanding slowly at $\sim$1.3 km s$^{-1}$, with a bulk line-of-sight (LOS) velocity near $-2$ km s$^{-1}$. The central "Diamond" is an unrelated dense clump at $\sim$7 km s$^{-1}$. The driving source, classified from IR spectroscopy, is a B0.5e star that powers the HII region. Unlike typical 3D shells, this marks the first case where we detect only a slowly expanding CII ring. We suggest the HII region and CII bubble, initially formed by a massive star, expanded outward from a flat slab of molecular gas nearly in the plane of the sky. The ring is now confined by swept-up material of the slab, while shell components moving perpendicular to the LOS have dissipated, leading to a reduction in expansion. Dedicated simulations tracing the evolution of the CII bubble support this geometry, consistent with previous reports of HII region evolution in flat molecular clouds. We propose that the "Diamond Ring" represents the terminal phase of an expanding CII bubble driven by stellar winds and thermal pressure.

astro-ph.GA↗

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

Scientific Objectives of the Xue-shan-mu-chang 15-meter Submillimeter Telescope

Submillimeter astronomy is poised to revolutionize our understanding of the Universe by revealing cosmic phenomena hidden from optical and near-infrared observations, particularly those associated with interstellar dust, molecular gas, and star formation. The Xue-shan-mu-chang 15-meter submillimeter telescope (XSMT-15m), to be constructed at a premier high-altitude site (4813 m) in Qinghai, China, marks a major milestone for Chinese astronomy, establishing the China mainland's first independently developed, world-class submillimeter facility. Equipped with state-of-the-art instruments, XSMT-15m will address a diverse range of frontier scientific questions spanning extragalactic astronomy, Galactic structure, time-domain astrophysics, and astrochemistry. In synergy with current and forthcoming observatories, XSMT-15m will illuminate the formation and evolution of galaxies, unravel the physical and chemical processes shaping the interstellar medium, and explore transient phenomena in the submillimeter regime. These capabilities will advance our understanding across extragalactic astronomy, Galactic ecology, astrochemistry, and time-domain astrophysics, inaugurating a new era for submillimeter research in China and the northern hemisphere.

astro-ph.GA↗

arXiv2025

Causal Spatial Quantile Regression

Treatment effects in a wide range of economic, environmental, and epidemiological applications often vary across space, and understanding the heterogeneity of causal effects across space and outcome quantiles is a critical challenge in spatial causal inference. To effectively capture spatial heterogeneity in distributional treatment effects, we propose a novel semiparametric neural network-based causal framework leveraging deep spatial quantile regression and then construct a plug-in estimator for spatial quantile treatment effects (SQTE). This framework incorporates an efficient adjustment procedure to mitigate the impact of spatial hidden confounders. Extensive simulations across various scenarios demonstrate that our methodology can accurately estimate SQTE, even with the presence of spatial hidden confounders. Additionally, the spatial confounding adjustment procedure effectively reduces neighborhood spatial patterns in the residuals. We apply this method to assess the spatially varying quantile treatment effects of maternal smoking on newborn birth weight in North Carolina, United States. Our findings consistently show negative effects across all birth weight quantiles, with particularly severe impacts observed in the lower quantile regions.

stat.ME↗

arXiv2025

Progressive Bird's Eye View Perception for Safety-Critical Autonomous Driving: A Comprehensive Survey

Bird's-Eye-View (BEV) perception has become a foundational paradigm in autonomous driving, enabling unified spatial representations that support robust multi-sensor fusion and multi-agent collaboration. As autonomous vehicles transition from controlled environments to real-world deployment, ensuring the safety and reliability of BEV perception in complex scenarios - such as occlusions, adverse weather, and dynamic traffic - remains a critical challenge. This survey provides the first comprehensive review of BEV perception from a safety-critical perspective, systematically analyzing state-of-the-art frameworks and implementation strategies across three progressive stages: single-modality vehicle-side, multimodal vehicle-side, and multi-agent collaborative perception. Furthermore, we examine public datasets encompassing vehicle-side, roadside, and collaborative settings, evaluating their relevance to safety and robustness. We also identify key open-world challenges - including open-set recognition, large-scale unlabeled data, sensor degradation, and inter-agent communication latency - and outline future research directions, such as integration with end-to-end autonomous driving systems, embodied intelligence, and large language models.

cs.RO↗

arXiv2025

Stable at Any Speed: Speed-Driven Multi-Object Tracking with Learnable Kalman Filtering

Multi-object tracking (MOT) enables autonomous vehicles to continuously perceive dynamic objects, supplying essential temporal cues for prediction, behavior understanding, and safe planning. However, conventional tracking-by-detection methods typically rely on static coordinate transformations based on ego-vehicle poses, disregarding ego-vehicle speed-induced variations in observation noise and reference frame changes, which degrades tracking stability and accuracy in dynamic, high-speed scenarios. In this paper, we investigate the critical role of ego-vehicle speed in MOT and propose a Speed-Guided Learnable Kalman Filter (SG-LKF) that dynamically adapts uncertainty modeling to ego-vehicle speed, significantly improving stability and accuracy in highly dynamic scenarios. Central to SG-LKF is MotionScaleNet (MSNet), a decoupled token-mixing and channel-mixing MLP that adaptively predicts key parameters of SG-LKF. To enhance inter-frame association and trajectory continuity, we introduce a self-supervised trajectory consistency loss jointly optimized with semantic and positional constraints. Extensive experiments show that SG-LKF ranks first among all vision-based methods on KITTI 2D MOT with 79.59% HOTA, delivers strong results on KITTI 3D MOT with 82.03% HOTA, and outperforms SimpleTrack by 2.2% AMOTA on nuScenes 3D MOT.

cs.CV↗

arXiv2025

Possible Orthorhombic Phase of Ta$_2$O$_5$ under High Pressures

A potential orthorhombic phase of Ta$_2$O$_5$, designated as Y-Ta$_2$O$_5$, is predicted under high-pressure conditions through density functional theory (DFT) calculations combined with structural search algorithms. This phase, consisting of four formula units per unit cell ($Z = 4$), exhibits the highest known Ta-O coordination numbers. Y-Ta$_2$O$_5$ is found to be the most energetically favorable form of Ta$_2$O$_5$ in the pressure range of approximately 70 GPa to at least 200 GPa. Both standard DFT-GGA and higher-accuracy GW calculations reveal that Y-Ta$_2$O$_5$ is a wide bandgap semiconductor with a direct bandgap. Additionally, nuclear quantum effects (NQEs) introduce nontrivial corrections to external pressure at fixed volumes, underscoring their significance in high-pressure phase stability analyses.

cond-mat.mtrl-sci↗