arXiv subjects

Jiadong Li

Publications and source records attributed to Jiadong Li.

At least 19 recordsLinked to original sources

arXiv2026

Mass-dependent multiplicity fraction in low mass stars revealed by Gaia astrometry

Stellar multiplicity at different orbital periods is probed by different techniques: radial velocities at the shortest periods, direct imaging at the longest, and astrometry in between. Gaia DR3 provides unprecedented astrometric information to constrain binary populations with periods of tens to thousands of days. Beyond the small fraction of direct orbit solutions, Gaia publishes coarser astrometric diagnostics for all objects, such as on-sky accelerations, jerks, or the single-star goodness-of-fit measure, RUWE. We show that these diagnostics together are sensitive to a far wider range of orbital periods than orbit solutions alone. Built on the Gaiamock emulator, we develop a forward-modelling framework to constrain rates of multiplicity from Gaia data, using all astrometric solution types. We apply this framework to $366~027$ primaries of $0.2$--$0.75\,M_\odot$ between $50$ and $200$ pc, including vastly more M-dwarfs than previous multiplicity studies. For fiducial models of the period and mass-ratio distributions, the inferred multiplicity fraction is approximately constant at ${\sim}45\%$ from the solar-type regime down to $0.4\,M_\odot$, only then dropping to ${\sim}15\%$ by $0.2\,M_\odot$. This mass-multiplicity relationship revises the conventional picture of a monotonically rising mass--multiplicity relation, and aligns the upper M-dwarf regime with solar-type stars rather than placing it on a continuous decline.

astro-ph.SR↗

arXiv2026

A Probabilistic Framework for Population Studies of the Solar Neighborhood: Application to SDSS-V and Gaia

Studies of the Solar Neighborhood require spectroscopic follow-up of stars identified in astrometric surveys to fully characterize their physical properties. The SDSS-V Solar Neighborhood Census (SNC) is a dedicated program to observe stars within 100~pc. However, due to competing observing programs and fiber assignment constraints, the resulting sample carries severe and complex selection effects. A framework is presented for characterizing the selection function of the SDSS-V SNC relative to the Gaia Catalog of Nearby Stars (GCNS), along with a forward modeling method to infer the properties of stellar subpopulations across the GCNS-defined 100 pc sample. The selection function is based on a method that models the selection probability as a function of sky position, Gaia G magnitude, and BP-RP. The resulting detection probabilities faithfully reproduce the known survey planning logic. This work further introduces the concept of a "subpopulation probability" -- a grid of posterior estimates across the Hertzsprung-Russell (HR) diagram representing the likelihood that a GCNS member belongs to a given SDSS-V defined subpopulation. The framework is validated with a mock dataset and its scientific utility is demonstrated through two applications using data from the Data Release 19: mapping H$\alpha$ emission across the HR diagram and measuring the variation of stellar density with mass and metallicity. These results illustrate how statistically robust population studies can be conducted with an incomplete spectroscopic survey when the selection function is well characterized. The code is made publicly available with this work, which will serve as an important tool for future studies.

astro-ph.SR↗

arXiv2026

Physics-Informed Generative Solver: Bridging Data-Driven Priors and Conservation Laws for Stable Spatiotemporal Field Reconstruction

Reconstructing continuous physical fields from sparse measurements is a central inverse problem, but data-driven generative models can produce states that violate governing dynamics. We introduce a physics-informed generative solver that separates stable prior learning from inference-time enforcement of conservation laws. Martingale-Regularized Score Matching regularizes score pretraining with a Score Fokker-Planck constraint, yielding a dynamically stable prior. Physics-Informed Implicit Score Sampling then guides denoising trajectories by gradients of physical residuals, projecting samples toward admissible manifolds without retraining. In acoustics, the method co-generates pressure and particle velocity from sparse sensors, enabling dense virtual arrays that suppress spatial aliasing. The same framework generalizes to real-world ERA5 meteorological fields under extreme sparsity. Together, this work establishes a rigorous and generalizable paradigm for solving high-dimensional inverse problems, bridging the gap between generative artificial intelligence and first-principles science.

cs.LG↗

arXiv2026

oMEGACat. X. Shedding light on the disrupted dwarf galaxy of Omega Centauri

Omega Centauri ($ω\,$Cen) is the most massive and chemically complex star cluster in the Milky Way and is widely regarded as the surviving nuclear star cluster of an accreted dwarf galaxy. However, its parent host remains uncertain. Here, we investigate a scenario in which Sequoia, Thamnos, and Gaia--Enceladus (GE) are debris from a single disrupted progenitor, the $ω\,$Dwarf, whose nucleus survives today as $ω\,$Cen. Using APOGEE and GALAH abundances together with Gaia astrometry, we reconstruct the chemical structure across this progenitor adopting orbital energy as a proxy for pre-merger radius. We find that the chemically evolved (younger Al-N-He-rich) population is strongly concentrated toward the inner regions, representing a population formed after/during the merger, while the primordial population represents a dwarf-galaxy-like population, supporting a common dwarf-galaxy origin for its components. The metallicity profile shows an inverted U-shaped gradient similar to those observed in present-day nucleated dwarf galaxies. At the same time, the inner regions ($ω\,$Cen+Thamnos) are more $α$-enhanced than the outskirts, pointing to shorter and more efficient star formation and indicating that the nucleus may have assembled through the merger of inspiraling globular clusters. Neutron-capture abundances reveal a Eu-rich, r-process-dominated outskirts and inner regions enhanced in [Ba/Eu] and [La/Eu], requiring delayed enrichment and more complex chemical evolution. Finally, our analysis shows that Sequoia and Thamnos naturally fit an outside-in stripping sequence around $ω\,$Cen, whereas the connection with GE remains unsure.

astro-ph.GA↗

arXiv2026

Dormant black hole candidates from Gaia DR3 summary diagnostics

We present a rigorous identification of candidates for dormant black holes (BHs) and neutron stars (NSs) in binaries using summary statistics from Gaia DR3, rather than full orbital solutions. Although Gaia astrometric orbits have already revealed a small sample of compact object binaries, many systems remain undetected due to stringent quality cuts imposed on the published orbits. Using a forward-modelling framework that simulates Gaia observables, in particular the re-normalised unit weight error (ruwe) and radial velocity (RV) scatter, we infer posterior distributions for companion mass and orbital period via MCMC sampling, marginalising over nuisance orbital parameters. We validate our approach by comparing the predicted masses and periods against full orbit solutions from DR3, and by successfully recovering known compact object binaries as promising candidates. The method is best suited for systems with red giant primaries, which have more reliable Gaia RV scatter and a light centroid more likely dominated by one component, compared to main-sequence stars, and they are less likely to be triples with short-period inner binaries, which produce confounding signatures. We applied the method to three million giants and identify 389 systems with best-fit companion masses $\gtrsim 3\,M_\odot$. Recovery simulations suggest our selection method is substantially more sensitive than the DR3 non-single-star catalogue, particularly for binaries with periods below 1 year and above $\sim 6$ years. These candidates represent promising targets for spectroscopic follow-up and Gaia DR4 analysis to confirm the presence of compact objects. Candidate main-sequence stars with massive companions face a larger set of confounding effects. Therefore, we present an analogous catalogue of 279 additional main-sequence candidates only as an appendix.

astro-ph.SR↗

arXiv2026

Stellar initial mass function in the 100-pc solar neighbourhood

The stellar initial mass function (IMF) is among the most fundamental distributions in astrophysics, defined as the mass spectrum of stars produced in a single star-formation event. Even in the solar neighbourhood, where measurements can be conducted via star counting, disentangling the IMF from observational effects remains challenging. In this work we introduce a new parametrisation of the stellar IMF in the 100-pc solar neighbourhood, leveraging the high-precision astrometric and photometric data from \textsl{Gaia} DR3: we model the colour-magnitude diagram of the field star population while accounting for observational uncertainties, Malmquist bias, Lutz-Kelker bias, variations in the mass-luminosity relation arising from metallicity differences, and the effects of unresolved binaries. In particular, we synthesise the binary population with a process imitating the dynamical evolution observed in star clusters to enforce that all components are drawn from the same IMF, while simultaneously recovering the observed present-day mass-ratio distribution. We determine an averaged stellar IMF over $0.25<m<1.0~M_{\odot}$ that aligns with canonical IMFs but achieves significantly tighter constraints: $α_1=0.75^{+0.06}_{-0.04}$, $α_2=2.07^{+0.04}_{-0.03}$, and a break point at $m_{\mathrm{break}}=0.40^{+0.01}_{-0.01}$ $\mathrm{M_{\odot}}$. Our inference also yields an averaged binary fraction over $0.25<m<1.0~M_{\odot}$ of approximately 26\%, and constrains the \textsl{Gaia} DR3 angular resolution to $1.11^{+0.11}_{-0.08}$ arcsec. We also provide the $ξ$-parameter for our IMF, which is $0.5070_{-0.0096}^{+0.0068}$, to facilitate direct comparison with other IMF determinations.

astro-ph.GA↗

arXiv2026

Detection and classification of astronomical sources with Astro-RetinaNet in crowded stellar fields

Upcoming next-generation sky surveys will detect large number of faint objects with magnitudes larger than 25. When objects are crowded within a limited a field of view, blending becomes unavoidable. Blending leads to the omission of many sources during photometry in these fields, which cause an underestimates of tens of percent in crowded fields, and remains a major challenge for existing source-extraction techniques. Although artificial neural networks had shown promising results in the detection and classification in wide-field surveys, they often fail with severely blended stars. We developed a robust deep learning model, Astro-RetinaNet, based on the Retinanet algorithm to detect and classify blended sources in single-band astronomical images. After training and evaluating the performance of our network on simulated images, we find precision of 0.96, 0.89,0.70, 0.50,0.75 for single star, 2-star, 3-star, 4-star and 5-or-more star blending cases, respectively, with star number density $\sim$22000 stars per $\rm arcmin^2$. We compare our method's detection capability and completeness both on CSST simulated NGC 2298 images and HST observed M31 images. In crowded and non-crowded stellar fields of simulated NGC 2298, our results show that the model can recover $82\%$ and $95\%$ sources respectively at magnitude ($i$ band) of 25, while for SExtractor and Photutils the completeness reduces to $20\%, 59\%$ and $60\%, 88\%$ respectively. In the M31 case, as faint as 27 magnitude ($F814W$) in a crowded field, Astro-RetinaNet detects 2,224 sources, significantly outperforming Photutils and SExtractor by factors of 3.4 and 7.1, respectively.

astro-ph.IM↗

arXiv2026

Variations in the Milky Way's Stellar Mass Function at [Fe/H] < -1

We present the first determination of the Galactic stellar mass function (MF) for low-mass stars (0.2-0.5 M_sun) at metallicities [Fe/H] < -1. A sample of ~53,000 stars was selected as metal-poor on the basis of both their halo-like orbits and their spectroscopic [Fe/H] from Gaia DR3 BP/RP (XP) spectra. These metallicity estimates for low-mass stars were enabled by calibrating Gaia XP spectra with stellar parameters from SDSS-V. For -1.5 < [Fe/H] < -1, we find that the MF below 0.5 M_sun exhibits a "bottom-heavy" power-law slope of alpha ~ -1.6. We tentatively find that at even lower metallicities, the MF becomes very bottom-light, with a near-flat power-law slope of alpha ~ 0 that implies a severe deficit of low-mass stars. This metallicity-dependent variation is insensitive to the adopted stellar evolution model. These results show that the Galactic low-mass MF is not universal, with variations in the metal-poor regime. A further calibration of XP metallicities in the regime of M < 0.5 M_sun and [Fe/H] < -1.5 will be essential to verify these tentative low-metallicity trends.

astro-ph.GA↗

arXiv2025

Stellar Parameters of BOSS M dwarfs in SDSS-V DR19

We utilized the Stellar LAbel Machine (SLAM), a data-driven model based on Support Vector Regression, to derive stellar parameters ([Fe/H], $T_{\rm eff}$, and $\log{g}$) for SDSS-V M dwarfs using low-resolution optical spectra (R$\sim$2000) obtained with the BOSS spectrographs. These parameters are calibrated using LAMOST F, G or K dwarf companions ([Fe/H]), and APOGEE Net ($T_{\rm eff}$ and $\log{g}$), respectively. Comparisons of SLAM predicted [Fe/H] values between two components of M+M dwarfs wide binaries show no bias but with a scatter of 0.11 dex. Further comparisons with two other works, which also calibrated the [Fe/H] of M dwarfs by using the F/G/K companions, reveal biases of -0.06$\pm$0.16 dex and 0.02$\pm$0.14 dex, respectively. The SLAM-derived effective temperatures agree well with the temperature which is calibrated by using interferometric angular diameters (bias: -27$\pm$92 K) and those of the LAMOST (bias: -34$\pm$65 K), but are systematically lower than those from an empirical relationship between the color index and $T_{\rm eff}$ by 146$\pm$45 K. The SLAM surface gravity aligns well with those of LAMOST (bias: -0.01$\pm$0.07 dex) and those derived from the stellar mass and radius (bias: -0.04$\pm$0.09 dex). Finally, we investigated a bias in [Fe/H] between SLAM and APOGEE ASPCAP. It depends on ASPCAP's [Fe/H] and $T_{\rm eff}$, we provide an equation to correct the ASPCAP metallicities.

astro-ph.SR↗

arXiv2025

The Initial mass function of field stars with mass $\leq$ 1 $M_{\odot}$ varies with metallicity

We investigated a volume-limited sample of LAMOST main-sequence stars with masses from 0.25 to 1 $M_{\odot}$ and distances of 150-350 pc to explore how the stellar initial mass function (IMF) varies with metallicity. We corrected the spectroscopic selection function by comparing the stellar number densities with the photometric ones at the same colour and magnitude. From these corrected number density distributions, we derived IMFs for each metallicity sub-samples. Fitting a broken power-law function in each IMF with a fixed break point at 0.525 $M_{\odot}$, we found the power-law indices increase with [Fe/H] for both mass regimes: $α_1$ (mass $\leq$ 0.525 $M_{\odot}$) rises from 0.54 $\pm$ 0.21 to 1.40 $\pm$ 0.07 and $α_2$ (mass>0.525 $M_{\odot}$) grows from 1.40 $\pm$ 0.16 to 1.86 $\pm$ 0.04 as [Fe/H] varies from -1 to +0.5 dex. It demonstrates that low-mass stars make up a larger fraction in metal-rich environments than in metal-poor ones. We performed simulations to assess the impact of unresolved binaries on the IMF power-law indices. After correction, the binary-adjusted $α$ values retained a similar metallicity-dependent trend. Furthermore, by examining the IMF of the aggregate sample, we found the corrected indices ($α_{\rm{1,corr}} = 1.48 \pm 0.03$ , $α_{\rm{2,corr}} = 2.17 \pm 0.03$) are consistent with Kroupa's IMF values ($α_1 = 1.3 \pm 0.5$ and $α_2 = 2.3 \pm 0.3$). Finally, we verified the robustness of our results by testing different break points and mass bin sizes, confirming that the IMF's dependence on [Fe/H] remains consistent.

astro-ph.SR↗

arXiv2025

The Silent Majority: The Interacting Post-Common-Envelope Binaries Underlying Cataclysmic Variables

We analyze the orbital period distribution of post-common-envelope white-dwarf-main-sequence (WDMS) binaries by cross-matching the new spectroscopic Gaia DR3 WDMS catalog with TESS light curves, and applying a uniform periodicity search and vetting pipeline. We identify 107 periodic systems, including 74 eclipsing binaries (32 new) and 33 binaries exhibiting only sinusoidal variations. Injection-recovery tests and a forward detectability model yield a completeness-corrected distribution that is well-described by a two-component function: a log-period Gaussian peaking at $P_{\rm orb} \approx 4.1 $ h with $σ\approx 1.8$ h, plus a rising component that begins near $P_{\rm orb}\approx12.9$ h. We refer to this extended component as the long-period tail. It consists exclusively of detached non-interacting post-common-envelope binaries (PCEBs) that likely emerged from the common envelope and have not yet initiated mass transfer. In contrast, the short-period Gaussian is dominated by interacting or near-contact systems (including 22 known cataclysmic variables), consistent with high Roche-lobe filling factors. From the completeness-corrected distribution we infer that $29.8\%\pm4.5\%$ of the spatially unresolved WDMSs in our parent catalog are close PCEBs. Binary population synthesis models with high common-envelope efficiencies overproduce long-period systems and fail to reproduce the sharp peak, whereas lower efficiencies ($αλ\leq 0.3$) match the peak more closely, yet still underpredict the tail. Our results hint at a large, currently under-classified reservoir of pre-cataclysmic variables and weakly accreting binaries, and provide new constraints on common-envelope physics.

astro-ph.SR↗

arXiv2025

Millions of Main-Sequence Binary Stars from Gaia BP/RP Spectra

We present the main-sequence binary (MSMS) Catalog derived from Gaia Data Release 3 BP/RP (XP) spectra. Leveraging the vast sample of low-resolution Gaia XP spectra, we develop a forward modeling approach that maps stellar mass and photometric metallicity to XP spectra using a neural network. Our methodology identifies binary systems through statistical comparison of single- and binary-star model fits, enabling detection of binaries with mass ratios between 0.4 and 1.0 and flux ratios larger than 0.1. From an initial sample of 35 million stars within 1 kpc, we identify 14 million binary candidates and define a high-confidence "golden sample" of 1 million binary systems. This large, homogeneous sample enables detailed statistical analysis of binary properties across diverse Galactic environments, providing new insights into binary star formation and evolution. In addition, the $χ^2$ comparison allows us to distinguish stars with luminous companions from single stars or binaries with dark companions, such as white dwarfs, neutron stars and black hole candidates, improving our understanding of compact object populations.

astro-ph.SR↗

arXiv2025

Differentiable Stellar Atmospheres with Physics-Informed Neural Networks

We present Kurucz-a1, a physics-informed neural network (PINN) that emulates 1D stellar atmosphere models under Local Thermodynamic Equilibrium (LTE), addressing a critical bottleneck in differentiable stellar spectroscopy. By incorporating hydrostatic equilibrium as a physical constraint during training, Kurucz-a1 creates a differentiable atmospheric structure solver that maintains physical consistency while achieving computational efficiency. Kurucz-a1 can achieve superior hydrostatic equilibrium and more consistent with the solar observed spectra compared to ATLAS-12 itself, demonstrating the advantages of modern optimization techniques. Combined with modern differentiable radiative transfer codes, this approach enables data-driven optimization of universal physical parameters across diverse stellar populations-a capability essential for next-generation stellar astrophysics.

astro-ph.SR↗

arXiv2025

Identification of 30,000 White Dwarf-Main Sequence binaries candidates from Gaia DR3 BP/RP(XP) low-resolution spectra

White dwarf-main sequence (WDMS) binary systems are essential probes for understanding binary stellar evolution and play a pivotal role in constraining theoretical models of various transient phenomena. In this study, we construct a catalog of WDMS binaries using Gaia DR3's low-resolution BP/RP (XP) spectra. Our approach integrates a model-independent neural network for spectral modelling with Gaussian Process Classification to accurately identify WDMS binaries among over 10 million stars within 1 kpc. This study identify approximately 30,000 WDMS binary candidates, including ~1,700 high-confidence systems confirmed through spectral fitting. Our technique is shown to be effective at detecting systems where the main-sequence star dominates the spectrum - cases that have historically challenged conventional methods. Validation using GALEX photometry reinforces the reliability of our classifications: 70\% of candidates with an absolute magnitude $M_{G} > 7$ exhibit UV excess, a characteristic signature of white dwarf companions. Our all-sky catalog of WDMS binaries expands the available dataset for studying binary evolution and white dwarf physics and sheds light on the formation of WDMS.

astro-ph.SR↗

arXiv2024

The Variation of the Galaxy-Wide IMF for Low-Mass Stars: Modeling and Observational Insights

The Stellar Initial Mass Function (IMF) characterizes the mass distribution of newly formed stars in various cosmic environments, serving as a fundamental assumption in astrophysical research. Recent findings challenge the prevalent notion of a universal and static IMF, proposing instead that the IMF's shape is contingent upon the star formation environment. In this study, we analyze the galaxy-wide variation of the IMF for low-mass stars in both dwarf and massive galaxies with diverse observational methods. Despite systematic discrepancies between different approaches, an IMF model with a metallicity-dependent slope for the low-mass stars aligns with the majority of observations, indicating a high degree of uniformity in the star formation processes across the universe. We also emphasize the need for a more comprehensive understanding of the variation of the low-mass IMF, considering measurement biases and factors beyond metallicity.

astro-ph.GA↗

arXiv2024

CURLING - I. The Influence of Point-like Image Approximation on the Outcomes of Cluster Strong Lens Modeling

Cluster-scale strong lensing is a powerful tool for exploring the properties of dark matter and constraining cosmological models. However, due to the complex parameter space, pixelized strong lens modeling in galaxy clusters is computationally expensive, leading to the point-source approximation of strongly lensed extended images, potentially introducing systematic biases. Herein, as the first paper of the ClUsteR strong Lens modelIng for the Next-Generation observations (CURLING) program, we use lensing ray-tracing simulations to quantify the biases and uncertainties arising from the point-like image approximation for JWST-like observations. Our results indicate that the approximation works well for reconstructing the total cluster mass distribution, but can bias the magnification measurements near critical curves and the constraints on the cosmological parameters, the total matter density of the Universe $Ω_{\rm m}$, and dark energy equation of state parameter $w$. To mitigate the biases, we propose incorporating the extended surface brightness distribution of lensed sources into the modeling. This approach reduces the bias in magnification from 46.2 per cent to 0.09 per cent for $μ\sim 1000$. Furthermore, the median values of cosmological parameters align more closely with the fiducial model. In addition to the improved accuracy, we also demonstrate that the constraining power can be substantially enhanced. In conclusion, it is necessary to model cluster-scale strong lenses with pixelized multiple images, especially for estimating the intrinsic luminosity of highly magnified sources and accurate cosmography in the era of high-precision observations.

astro-ph.CO↗

arXiv2024

The Present-Day Mass Function of Star Clusters in the Solar Neighborhood

This work analyses the present-day mass function (PDMF) of 93~star clusters utilizing Gaia DR3 data, with membership determined by the StarGo machine learning algorithm. The impact of unresolved binary systems on mass estimation is rigorously assessed, adopting three mass ratio profiles for correction. The PDMF is characterized by the power-law index, $α$, derived through a robust maximum likelihood method that avoids biases associated with data binning. The value of $α$ for stars between the completeness limited mass of Gaia with a mean 0.3 $M_\odot$ for our cluster samples and 2 $M_\odot$, exhibits stability for clusters younger than 200 Myr, decreasing for older clusters, particularly when considering stars within the half-mass radius. The PDMF of these star clusters is consistent with a dynamically evolved Kroupa IMF via the loss of low-mass stars. Cluster morphology shows a correlation with $α$, as $α$ values exhibit a decreasing trend from filamentary to tidal-tail clusters, mirroring the sequence of increasing cluster age. The dependence of $α$ on total cluster mass is weak, with a subtle increase for higher-mass clusters, especially outside the half-mass radius. We do not observe a correlation between $α$ and the mean metallicity of the clusters. Younger clusters have lower metallicity compared to their older counterparts, which indicates that the older clusters might have migrated to the solar neighbourhood from the inner disk. A comparison with numerical models incorporating a black hole population suggests the need for observations of distant, older, massive open clusters to determine whether or not they contain black holes.

astro-ph.GA↗

arXiv2024

BioDrone: A Bionic Drone-based Single Object Tracking Benchmark for Robust Vision

Single object tracking (SOT) is a fundamental problem in computer vision, with a wide range of applications, including autonomous driving, augmented reality, and robot navigation. The robustness of SOT faces two main challenges: tiny target and fast motion. These challenges are especially manifested in videos captured by unmanned aerial vehicles (UAV), where the target is usually far away from the camera and often with significant motion relative to the camera. To evaluate the robustness of SOT methods, we propose BioDrone -- the first bionic drone-based visual benchmark for SOT. Unlike existing UAV datasets, BioDrone features videos captured from a flapping-wing UAV system with a major camera shake due to its aerodynamics. BioDrone hence highlights the tracking of tiny targets with drastic changes between consecutive frames, providing a new robust vision benchmark for SOT. To date, BioDrone offers the largest UAV-based SOT benchmark with high-quality fine-grained manual annotations and automatically generates frame-level labels, designed for robust vision analyses. Leveraging our proposed BioDrone, we conduct a systematic evaluation of existing SOT methods, comparing the performance of 20 representative models and studying novel means of optimizing a SOTA method (KeepTrack KeepTrack) for robust SOT. Our evaluation leads to new baselines and insights for robust SOT. Moving forward, we hope that BioDrone will not only serve as a high-quality benchmark for robust SOT, but also invite future research into robust computer vision. The database, toolkits, evaluation server, and baseline results are available at http://biodrone.aitestunion.com.

cs.CV↗