arXiv subjects

Ye Ding

Publications and source records attributed to Ye Ding.

13 recordsLinked to original sources

arXiv2026

A pilot study on the CSST astrometric capability: Detecting astrometric binaries with Gaia synergy via simulated data

Context. The China Space-station Survey Telescope (CSST) will provide deep, wide-field epoch astrometry during its 10-year mission. Astrometric binary orbits constrain the masses of stellar and compact-object components. Orbital recovery depends on astrometric precision and temporal coverage. Combining CSST and Gaia data extends the baseline and improves binary detection. Aims. We evaluate CSST, Gaia, and joint astrometry for binary-candidate selection and 12-parameter (12p) orbit fitting at faint magnitudes ($g>17.8$). We also test how regular CSST cadences affect the yield of 12p fits satisfying our criteria. Methods. We constructed a mock catalog, simulated CSST and Gaia epoch astrometry, and fitted five-parameter (5p) single-star models to derive astrometric diagnostics, proper-motion anomaly features, and observational-sampling features. A four-stage histogram-based gradient-boosting classifier used these features to select candidates for 12p orbit fitting and assessment. Results. On the independent test set, the classifier reaches a precision of 0.802 and a recall of 0.181 among eligible true binaries. In the scenario-specific fitted samples, joint astrometry raises the fiducial fraction from 6.76% for Gaia alone to 10.46%; for fitted binaries with $P_{\rm true}>15{\rm yr}$, it rises from 2.37% to 6.78%. The current CSST schedule yields few fiducial fits, while idealized regular cadences increase the yield mainly at $g\lesssim21$. Conclusions. In the simulation, joint CSST and Gaia epoch astrometry yields higher fractions of fitted unresolved binaries satisfying the stated criteria than Gaia-only solution. A practical strategy is to select candidates from 5p diagnostics and astrometric anomalies, obtain more regular CSST follow-up observations, and then fit 12p orbital models and apply the selection criteria.

astro-ph.IM↗

arXiv2026

Optical-morphology-based assessment of astrometric quality in Gaia-CRF3 quasars

Context. Several studies have shown that host-galaxy structure or extended optical morphology in AGNs can induce spurious parallaxes and proper motions in Gaia DR3. However, it remains unclear whether source morphology also introduces systematic errors into the celestial reference frame constructed from Gaia data. Aims. We aim to provide a Gaia-independent external morphological indicator for Gaia-CRF3 sources and to use it to quantify the astrometric systematics associated with source morphology. Methods. Using morphological parameters derived from DESI, SDSS, and SkyMapper, together with the PS1-PSC point-source score as a common reference scale, we used XGBoost to infer external morphological scores for Gaia-CRF3 sources. We then developed a multi-survey fusion scheme to combine the four survey-based point-source scores into a single composite score that measures the degree to which each source departs from the morphology of an ideal point source. Results. We obtained morphological scores for 1,607,490 Gaia-CRF3 sources, corresponding to a completeness of 99.59\% with respect to the full Gaia-CRF3 catalogue. The score ranges from 0 to 1 and remains reliable for sources with $G<20.85$ mag. Based on this indicator, we find that AGNs with strongly non-point-like morphology induce a parallax zero-point shift of about $-43.7\,\mu$as, which cannot be effectively removed by the current parallax zero-point correction model. We also find that reference-source subsamples selected in different score ranges exhibit significantly different all-sky proper motion fields. For the high-purity point-source subsample with \texttt{point\_score} > 0.95, the total frame spin amplitude is reduced by 15.8\% relative to that of the full Gaia-CRF3 sample.

astro-ph.GA↗

arXiv2026

Refining the Gaia DR3 Parallax Zero-point: A Hybrid Approach Combining Global Parametric Correction with Local Refinement

The Gaia Data Release 3 (GDR3) parallaxes are affected by a complex bias that depends on stellar magnitude, color, and celestial position, with amplitudes reaching tens of microarcseconds ($\mu$as). Standard global parametric models (e.g., Lindegren et al. 2021, hereafter L21) effectively remove large-scale trends but struggle to resolve small-scale spatial systematics due to functional rigidity. We aim to construct a flexible, data-driven calibration map that eliminates these residual local systematics without imposing rigid functional forms. We propose a "Global Pre-correction + Local Refinement" hybrid strategy. First, we utilize the L21 model as a baseline to remove the dominant magnitude and color-dependent biases. Second, we model the residual zero-point using a Local Non-parametric method based on a Sliding Window technique. This approach fits local trends using k-nearest neighbors from quasars (for faint stars, G>18) and wide binaries combined with Large Magellanic Cloud (LMC) (for bright stars, G < 18). Our hybrid model demonstrates significant improvements over the standard L21 solution. Validation against different samples reveals a remarkably flat residual map with near-zero bias across the full sky. Our mathematical attempt at calibrating the parallax zero-point is expected to provide a useful reference for the zero-point correction in future Gaia DR4, and to help move towards a physical resolution of this issue.

astro-ph.IM↗

arXiv2026

Constraining the inclination of binary system orbits with the astrometric excess noise from Gaia DR3

Orbital inclination is crucial in determining the binary mass. The astrometric excess noise contains the orbital motion information, which can be used to constrain the inclination. We aim to constrain the orbital inclination of a binary system by combining radial velocity measurements with the astrometric excess noise from the Gaia DR3 solution. The astrometric excess noise is directly related to the orbital parameters. For a binary system with a radial velocity solution, it can be treated as a function of the orbital inclination. Using the Gaia nominal scanning law and the estimated centroid uncertainties, we simulate Gaia astrometric epoch observations to reproduce the expected excess noise. By sampling different inclinations and comparing the resulting simulated excess noise with the value reported in Gaia DR3, we can constrain the inclination to a specific interval. We have developed a method to constrain the orbital inclination within a specific range, enabling a more accurate determination of the binary mass, particularly for spectroscopic binaries. Internal and external validations demonstrate the robustness of the method, although certain limitations remain. It is most reliable for systems exhibiting a strong astrometric signal of binary motion, while caution is required when applying it to binaries with weak astrometric wobbles or poorly sampled orbits.

astro-ph.IM↗

arXiv2025

Characterizing the astrometric quality of AGNs in Gaia-CRF3

Active Galactic Nuclei (AGNs), owing to their great distances and compact sizes, serve as fundamental anchors for defining the celestial reference frame. With about 1.9 million AGNs observed in Gaia DR3 at optical precision comparable to radio wavelengths, Gaia provides a solid foundation for constructing the next-generation, kinematically non-rotating optical reference frame. Accurate assessment of systematic residuals in AGN astrometry is therefore crucial. In this talk, we analysed the parallaxes and proper motions of Gaia DR3 AGNs to characterize systematic errors and their correlations with various physical and observational properties. A subset of Gaia-CRF3 AGNs exhibits significant astrometric offsets, mainly arising from dual or lensed quasars whose structural variations induce photocenter jitter, mimicking parallax and proper motion. Such sources must be carefully excluded from reference frame construction. To this end, we introduce an astrometric quality index for each source to quantify its astrometric reliability. The results reveal a strong correlation between lower quality index values and increasing errors in position, proper motion, and parallax, demonstrating that the proposed index provides an effective metric for selecting high-fidelity AGNs as primary reference sources.

astro-ph.GA↗

arXiv2025

A Geometric Method for Base Parameter Analysis in Robot Inertia Identification Based on Projective Geometric Algebra

This paper proposes a novel geometric method for analytically determining the base inertial parameters of robotic systems. The rigid body dynamics is reformulated using projective geometric algebra, leading to a new identification model named ``tetrahedral-point (TP)" model. Based on the rigid body TP model, coefficients in the regresoor matrix of the identification model are derived in closed-form, exhibiting clear geometric interpretations. Building directly from the dynamic model, three foundational principles for base parameter analysis are proposed: the shared points principle, fixed points principle, and planar rotations principle. With these principles, algorithms are developed to automatically determine all the base parameters. The core algorithm, referred to as Dynamics Regressor Nullspace Generator (DRNG), achieves $O(1)$-complexity theoretically following an $O(N)$-complexity preprocessing stage, where $N$ is the number of rigid bodies. The proposed method and algorithms are validated across four robots: Puma560, Unitree Go2, a 2RRU-1RRS parallel kinematics mechanism (PKM), and a 2PRS-1PSR PKM. In all cases, the algorithms successfully identify the complete set of base parameters. Notably, the approach demonstrates high robustness and computational efficiency, particularly in the cases of PKMs. Through the comprehensive demonstrations, the method is shown to be general, robust, and efficient.

cs.RO↗

arXiv2025

DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research.

physics.chem-ph↗

arXiv2025

Analysis of the Gaia Data Release 3 parallax bias at bright magnitudes

The combination of visual and spectroscopic orbits in binary systems enables precise distance measurements without additional assumptions, making them ideal for examining the parallax zero-point offset (PZPO) at bright magnitudes (G < 13) in Gaia. We compiled 249 orbital parallaxes from 246 binary systems and used Markov Chain Monte Carlo (MCMC) simulations to exclude binaries where orbital motion significantly impacts parallaxes. After removing systems with substantial parallax errors, large discrepancies between orbital and Gaia parallaxes, and selecting systems with orbital periods under 100 days, a final sample of 44 binaries was retained.The weighted mean PZPO for this sample is -38.9 $\pm$ 10.3 $\mu$as, compared to -58.0 $\pm$ 10.1 $\mu$as for the remaining systems, suggesting that orbital motion significantly affects parallax measurements. These formal uncertainties of the PZPO appear to be underestimated by a factor of approximately 2.0. For bright stars with independent trigonometric parallaxes from VLBI and HST, the weighted mean PZPOs are -14.8 $\pm$ 10.6 and -31.9 $\pm$ 14.1 $\mu$as, respectively. Stars with $G \leq 8$ exhibit a more pronounced parallax bias, with some targets showing unusually large deviations, likely due to systematic calibration errors in Gaia for bright stars. The orbital parallaxes dataset compiled in this work serves as a vital resource for validating parallaxes in future Gaia data releases.

astro-ph.SR↗

arXiv2024

Generation of Conservative Dynamical Systems Based on Stiffness Encoding

Dynamical systems (DSs) provide a framework for high flexibility, robustness, and control reliability and are widely used in motion planning and physical human-robot interaction. The properties of the DS directly determine the robot's specific motion patterns and the performance of the closed-loop control system. In this paper, we establish a quantitative relationship between stiffness properties and DS. We propose a stiffness encoding framework to modulate DS properties by embedding specific stiffnesses. In particular, from the perspective of the closed-loop control system's passivity, a conservative DS is learned by encoding a conservative stiffness. The generated DS has a symmetric attraction behavior and a variable stiffness profile. The proposed method is applicable to demonstration trajectories belonging to different manifolds and types (e.g., closed and self-intersecting trajectories), and the closed-loop control system is always guaranteed to be passive in different cases. For controllers tracking the general DS, the passivity of the system needs to be guaranteed by the energy tank. We further propose a generic vector field decomposition strategy based on conservative stiffness, which effectively slows down the decay rate of energy in the energy tank and improves the stability margin of the control system. Finally, a series of simulations in various scenarios and experiments on planar and curved motion tasks demonstrate the validity of our theory and methodology.

cs.RO↗

arXiv2024

Analysis of Gaia Data Release 3 Parallax bias in the Galactic plane

The systematic errors are inevitable in Gaia published astrometric data. Lindegren et al. (L21) proposed a global recipe to correct for the GEDR3 parallax zero point offset, which did not consider the Galactic plane. The applicability of their correction model to the Galactic plane remains uncertain. We attempt to have an independent investigation into the sample dependence of the L21 correction, and its applicability to the Galactic plane. We collect various samples, including quasars, binaries, and sources with parallaxes from other surveys or methods, to validate the L21 correction, especially in the Galactic plane. We conclude that the L21 correction exhibits sample dependence, and does not apply effectively to the Galactic plane. We present a new parallax bias correction applying to the Galactic plane, offering improvements over the existing L21 correction. The correction difference between L21 and this work can go up to 0.01 mas within certain ranges of magnitude and colour. This work provides an additional recipe for users of Gaia parallaxes, especially for sources located near the Galactic plane.

astro-ph.IM↗

arXiv2024

DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning

Personalized federated learning becomes a hot research topic that can learn a personalized learning model for each client. Existing personalized federated learning models prefer to aggregate similar clients with similar data distribution to improve the performance of learning models. However, similaritybased personalized federated learning methods may exacerbate the class imbalanced problem. In this paper, we propose a novel Dynamic Affinity-based Personalized Federated Learning model (DA-PFL) to alleviate the class imbalanced problem during federated learning. Specifically, we build an affinity metric from a complementary perspective to guide which clients should be aggregated. Then we design a dynamic aggregation strategy to dynamically aggregate clients based on the affinity metric in each round to reduce the class imbalanced risk. Extensive experiments show that the proposed DA-PFL model can significantly improve the accuracy of each client in three real-world datasets with state-of-the-art comparison methods.

cs.LG↗

arXiv2023

DeePMD-kit v2: A software package for Deep Potential models

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments.

physics.chem-ph↗

arXiv2020

The study of calibration for the hybrid pixel detector with single photon counting in HEPS-BPIX

The calibration process for the hybrid array pixel detector designed for High Energy Photon Source in China, we called HEPS-BPIX, is presented in this paper. Based on the threshold scanning, the relationship between energy and threshold is quantified for the threshold calibration. For the threshold trimming, the precise algorithm basing on LDAC characteristic and fast algorithm basing on LDAC scanning are proposed in this paper to study the performance of the threshold DACs which will be applied to the pixel. The threshold dispersion has been reduced from 46.28 mV without algorithm to 6.78 mV with the precise algorithm, whereas it is 7.61 mV with fast algorithm. For the temperature from 5 to 60 , the threshold dispersion of precise algorithm varies in the range of about 5.69 mV, whereas it is about 33.21 mV with the fast algorithm which can be re-corrected to 1.49 mV. The measurement results show that the fast algorithm could get the applicable threshold dispersion for a silicon pixel module and take a shorter time, while the precise algorithm could get better threshold dispersion, but time consuming. The temperature dependence of the silicon pixel module noise is also studied to assess the detector working status. The minimum detection energy can be reduced about 0.83 keV at a 20 lower temperature.

physics.ins-det↗