arXiv subjects

Hongliang Yan

Publications and source records attributed to Hongliang Yan.

At least 19 recordsLinked to original sources

arXiv2026

A Generalist Model Including Evolved Star Mass and Age

Determining precise stellar ages and masses for evolved giants is crucial for Galactic archaeology but challenged by spectral degeneracies. Gaia's low-resolution XP spectra offer a unique opportunity to infer these parameters on a massive scale using data-driven methods. We extend a transformer-based astronomical foundation model to evolved stars, establishing a unified framework to simultaneously predict atmospheric parameters ($T_{\mathrm{eff}}$, $\log g$, $[\mathrm{M}/\mathrm{H}]$) and evolutionary labels (mass, age) with physical consistency. Treating spectra as token sequences, we integrated mass and age into the model's vocabulary. The model is trained on Gaia XP spectra cross-matched with the APOGEE DR17 DistMass catalog. Our generative approach enables flexible input handling, including spectral inpainting and parameter-to-spectrum generation. On an independent test set, the model achieves a prediction scatter of $\sigma \approx 0.114 \, M_{\odot}$ for mass and $\sigma \approx 1.334$ Gyr for age. Beyond numerical accuracy, it successfully reproduces the giant branch's mass-luminosity relation and autonomously disentangles interstellar extinction from intrinsic temperature variations without explicit physical priors. It also robustly recovers missing spectral data and estimates reliable uncertainties. Validating that foundation models can internalize stellar physics from data, this physically-aware, probabilistic framework offers a powerful tool for unraveling Milky Way history using large-scale spectroscopic surveys.

astro-ph.SR↗

arXiv2026

A Tale of Two Origins: In-Situ versus Accreted Nitrogen-Rich Field Stars in the MW

Spectroscopic surveys have identified significant numbers of metal-poor nitrogen-rich (N-rich) field stars. These stars are strong candidates for escapees from globular clusters (GCs), as their distinctive nitrogen enhancement mirrors the chemical patterns observed in some of the members of GCs. As part of the effort to characterize their chemodynamical properties, we derived abundances for up to 25 elements in a sample of 33 N-rich field giant stars (18 of them are studied for the first time) using high-resolution optical spectroscopy. We confirm their elevated abundances of N, Na, and Al, strongly supporting a GC origin. Given that Galactic GCs themselves formed within diverse progenitor galaxies, we sought to identify the ancestral systems of these N-rich field stars. By analyzing their dynamical parameters, we separated the sample into high-energy (HE) and low-energy (LE) groups. The HE group exhibits lower [{\alpha}/Fe] and enhanced r-process abundances compared to the LE group. This indicates that the HE stars likely escaped from GCs accreted from massive dwarf galaxies (e.g., Gaia-Sausage-Enceladus), while the LE stars probably originated from in-situ GCs. We also find that the chemical pattern of these N-rich stars with [Fe/H] {\lessapprox} -1.0 are similar to the high-redshift ''N-emitters''. Furthermore, orbital integrations revealed a close encounter between one N-rich field star and the globular cluster NGC 6235. Our work demonstrates the potential of using chemodynamical analyses to trace Galactic assembly through chemical peculiar stars, while highlighting that larger samples and more precise data in the future are crucial to establish definitive origins.

astro-ph.GA↗

arXiv2025

scMRDR: A scalable and flexible framework for unpaired single-cell multi-omics data integration

Advances in single-cell sequencing have enabled high-resolution profiling of diverse molecular modalities, while integrating unpaired multi-omics single-cell data remains challenging. Existing approaches either rely on pair information or prior correspondences, or require computing a global pairwise coupling matrix, limiting their scalability and flexibility. In this paper, we introduce a scalable and flexible generative framework called single-cell Multi-omics Regularized Disentangled Representations (scMRDR) for unpaired multi-omics integration. Specifically, we disentangle each cell's latent representations into modality-shared and modality-specific components using a well-designed $\beta$-VAE architecture, which are augmented with isometric regularization to preserve intra-omics biological heterogeneity, adversarial objective to encourage cross-modal alignment, and masked reconstruction loss strategy to address the issue of missing features across modalities. Our method achieves excellent performance on benchmark datasets in terms of batch correction, modality alignment, and biological signal preservation. Crucially, it scales effectively to large-scale datasets and supports integration of more than two omics, offering a powerful and flexible solution for large-scale multi-omics data integration and downstream biological discovery.

q-bio.QM↗

arXiv2025

Meridional Circulation II: A Unified Mechanism for Lithium Depletion in Solar Analogs and the Lithium Dip in Mid-F Cluster Stars

The behavior of lithium (Li) in Population I main sequence stars challenges standard stellar theory. Two phenomena stand out: the solar Li problem which extends to Li depletion in solar analogs and the Li dip observed in mid-F stars within open clusters. Building on the meridional circulation-driven radial mixing framework previously developed to explain Li-enriched red clump stars, we explore its relevance to Li depletion on the main sequence. First, our models reproduce the observed $A(\text{Li})$-Age correlation in solar analogs. Through detailed isochrone analysis, we find good agreement between the simulated and observed $A(\text{Li})$-$T_{\text{eff}}$ relationships within the solar analog parameter space. However, the predicted solar Li abundance ($\sim 1.5\,\text{dex}$) is still higher than current solar measurements. Second, our models partially explain the Li dip phenomenon in mid-F cluster stars. The models accurately reproduce Li distributions on the cool side of the Li dip in most clusters and capture the Li behaviors on the hot side observed in systems like the Hyades. However, we identify limitations in the models' ability to fully reproduce the dip morphology, particularly due to the rotation velocity distribution of sample stars in this temperature zone.

astro-ph.SR↗

arXiv2025

Effect of Matter Accretion on Lithium Enhancement of Giants

A subset of low-mass giants ($<2.2\,M_{\odot}$) exhibit anomalous lithium enhancement behavior, which is still an open topic. Given that more massive giants retain more surface lithium, increasing mass by accreting circumstellar matter could be a channel to enrich lithium. We evaluate this process in the current work. Using MESA, we construct a model of matter accretion, including mass loss, that evolves a star from the main sequence turnoff to the red giant branch tip. The mean accretion rate is estimated from the upper limit of the accreted mass and the evolutionary time of the star during this period, and a grid of accretion rates is constructed. We separately consider their effects on the lithium enhancement of giants, both in terms of the mass and the composition of accretion. Accreting matter with higher lithium abundances has a promoting effect on the lithium enhancement of giants. The accreted matter with excess lithium alleviates the dilution of lithium in the convective envelope during the first dredge-up. The added mass results in lower temperatures at the bottom of the convective envelope, which likewise weakens the depletion of surface lithium. Weak accretion of circumstellar matter is a possible route to lithium enhancement for giants, and it predicts an upper limit on the lithium abundance of $\rm \sim 2.5\,dex$. However, the mass increment it requires poses a potential challenge to real astrophysical environments. Such accretion suppresses lithium dilution and depletion of the star during the first dredge-up, thus exhibiting lithium enhancement behavior.

astro-ph.SR↗

arXiv2025

Phase II of the LAMOST-Kepler/K2 Survey. II. Time Domain of Medium-resolution Spectroscopic Observations from 2018 to 2023

The LAMOST-Kepler/K2 Medium-Resolution Spectroscopic Survey (LK-MRS) conducted time-domain medium-resolution spectroscopic observations of 20 LAMOST plates in the Kepler and K2 fields from 2018 to 2023, a phase designated as LK-MRS-I. A catalog of stellar parameters for a total of 36,588 stars, derived from the spectra collected during these five years, including the effective temperature, the surface gravity, the metallicity, the {\alpha}-element abundance, the radial velocity, and v sin i of the target stars, is released, together with the weighted averages and uncertainties. At S/N = 10, the measurement uncertainties are 120 K, 0.18 dex, 0.13 dex, 0.08 dex, 1.9 km/s, and 4.0 km/s for the above parameters, respectively. Comparisons with the parameters provided by the APOGEE and GALAH surveys validate the effective temperature and surface gravity measurements, showing minor discrepancies in metallicity and {\alpha}-element abundance values. We identified some peculiar star candidates, including 764 metal-poor stars, 174 very metal-poor stars, and 30 high-velocity stars. Moreover, we found 2,333 stars whose radial velocity seems to be variable. Using Kepler/K2 or TESS photometric data, we confirmed 371 periodic variable stars among the radial velocity variable candidates and classified their variability types. LK-MRS-I provides spectroscopic data being useful for studies of the Kepler and K2 fields. The LK-MRS project will continue collecting time-domain medium-resolution spectra for target stars during the third phase of LAMOST surveys, providing data to support further scientific research.

astro-ph.SR↗

arXiv2025

Unevolved Li-rich stars at low metallicity: a possible formation pathway through novae

A small fraction of low-mass stars have been found to have anomalously high Li abundances. Although it has been suggested that mixing during the red giant branch phase can lead to Li production, this method of intrinsic Li production cannot explain Li-rich stars that have not yet undergone the first dredge-up. To obtain clues about the origin of such stars, we present a detailed chemical abundance analysis of four unevolved Li-rich stars with $-2.1 < [\mathrm{Fe/H}] < -1.3$ and $2.9<A({\rm Li})<3.6$, $0.7-1.4$ dex higher Li abundance than typical unevolved metal-poor stars. One of the stars, Gaia DR3 6334970766103389824 (D25_6334), was serendipitously found in the stellar stream ED-3, and the other three stars have been reported to have massive ($M\gtrsim 1.3\,\mathrm{M_\odot}$) non-luminous companions. We show that three of the four stars exhibit abundance patterns similar to those of known unevolved Li-rich stars, namely normal abundances in most elements except for Li and Na. These abundance similarities suggest a common origin for the unevolved Li-rich stars and low-mass metal-poor stars with massive compact companions. We also made the first detection of N abundance to unevolved Li-rich stars in D25_6334, and found that it is significantly enhanced ($[\mathrm{N/Fe}]=1.3$). The observed abundance pattern of D25_6334, spanning from C to Si, indicates that its surface has been polluted by an intermediate-mass former companion star or a nova system that involves a massive ONe white dwarf. Using a population synthesis model, we show that the nova scenario can lead to the observed level of Li enhancement and also provide an explanation for Li-rich stars without companions and those with massive compact companions.

astro-ph.SR↗

arXiv2024

The temporal and spatial variations of lithium abundance in the Galactic disc

This study investigates the temporal and spatial variations in lithium abundance within the Milky Way using a sample of 22,034 main-sequence turn-off (MSTO) stars and subgiants, characterised by precise stellar ages, 3D NLTE (non-local thermodynamic equilibrium) lithium abundances, and birth radii. Our results reveal a complex variation in lithium abundance with stellar age: a gradual increase from 14 Gyr to 6 Gyr, followed by a decline between 6 Gyr and 4.5 Gyr, and a rapid increase thereafter. We find that young Li-rich stars (ages $<$ 4 Gyr, A(Li) $>$ 2.7 dex) predominantly originate from the outer disc. By binning the sample according to guiding center radius and z$_{\rm max}$, we observe that these young Li-rich stars migrate radially to the local and inner discs. In addition, the stars originating from the inner disc experienced a rapid Li enrichment process between 8 Gyr and 6 Gyr. Our analysis suggests that the age range of Li-dip stars is 4-5 Gyr, encompassing evolution stages from MSTO stars to subgiants. The Galactic radial profile of A(Li) (with respect to birth radius), as a function of age, reveals three distinct periods: 14-6 Gyr ago, 6-4 Gyr ago, and 4-1 Gyr ago. Initially, the lithium abundance gradient is positive, indicating increasing Li abundance with birth radius. During the second period, it transitions to a negative and broken gradient, mainly affected by Li-dip stars. In the final period, the gradient reverts to a positive trend.

astro-ph.GA↗

arXiv2024

LayerMatch: Do Pseudo-labels Benefit All Layers?

Deep neural networks have achieved remarkable performance across various tasks when supplied with large-scale labeled data. However, the collection of labeled data can be time-consuming and labor-intensive. Semi-supervised learning (SSL), particularly through pseudo-labeling algorithms that iteratively assign pseudo-labels for self-training, offers a promising solution to mitigate the dependency of labeled data. Previous research generally applies a uniform pseudo-labeling strategy across all model layers, assuming that pseudo-labels exert uniform influence throughout. Contrasting this, our theoretical analysis and empirical experiment demonstrate feature extraction layer and linear classification layer have distinct learning behaviors in response to pseudo-labels. Based on these insights, we develop two layer-specific pseudo-label strategies, termed Grad-ReLU and Avg-Clustering. Grad-ReLU mitigates the impact of noisy pseudo-labels by removing the gradient detrimental effects of pseudo-labels in the linear classification layer. Avg-Clustering accelerates the convergence of feature extraction layer towards stable clustering centers by integrating consistent outputs. Our approach, LayerMatch, which integrates these two strategies, can avoid the severe interference of noisy pseudo-labels in the linear classification layer while accelerating the clustering capability of the feature extraction layer. Through extensive experimentation, our approach consistently demonstrates exceptional performance on standard semi-supervised learning benchmarks, achieving a significant improvement of 10.38% over baseline method and a 2.44% increase compared to state-of-the-art methods.

cs.LG↗

arXiv2024

BEACON: Benchmark for Comprehensive RNA Tasks and Language Models

RNA plays a pivotal role in translating genetic instructions into functional outcomes, underscoring its importance in biological processes and disease mechanisms. Despite the emergence of numerous deep learning approaches for RNA, particularly universal RNA language models, there remains a significant lack of standardized benchmarks to assess the effectiveness of these methods. In this study, we introduce the first comprehensive RNA benchmark BEACON (\textbf{BE}nchm\textbf{A}rk for \textbf{CO}mprehensive R\textbf{N}A Task and Language Models). First, BEACON comprises 13 distinct tasks derived from extensive previous work covering structural analysis, functional studies, and engineering applications, enabling a comprehensive assessment of the performance of methods on various RNA understanding tasks. Second, we examine a range of models, including traditional approaches like CNNs, as well as advanced RNA foundation models based on language models, offering valuable insights into the task-specific performances of these models. Third, we investigate the vital RNA language model components from the tokenizer and positional encoding aspects. Notably, our findings emphasize the superiority of single nucleotide tokenization and the effectiveness of Attention with Linear Biases (ALiBi) over traditional positional encoding methods. Based on these insights, a simple yet strong baseline called BEACON-B is proposed, which can achieve outstanding performance with limited data and computational resources. The datasets and source code of our benchmark are available at https://github.com/terry-r123/RNABenchmark.

q-bio.QM↗

arXiv2024

Distribution and evolution of Li abundance in red clump stars can be explained by the internal gravity waves

The study of Li phenomena in red clump (RC) stars can give us a deeper understanding of the structure and evolution of stars. \citet{2022ApJ...933...58C} explained the RC Li abundance distributions naturally using only standard post main sequence (MS) Li evolution models when the distribution of progenitor masses and the depletion of Li during the MS observed in MS stars were considered, thus neither extra Li depletion nor Li creation mechanism is required. Nevertheless, it is interesting to consider the effects of mixing caused by some extra mechanisms. By constructing different models, we find that the mixing caused by internal gravity waves can explain the observed Li abundances of RC stars with low mass progenitors. To explain that, we rely on the extra mixing induced by internal gravity waves that are excited at the bottom of the convective envelope at the red giant branch (RGB) stage. During the RGB stage, introducing the internal gravity waves can improve the diffusion coefficient and strengthen the mixing effect. The effective enrichment of Li occurs at the late RGB stage and requires the diffusion coefficient of H-burning shell to reach $\rm \sim 10^{8}\,cm^{2}\,s^{-1}$. Our models predict that the Li abundance decreases from $\rm \sim 1.5\,dex$ to $\rm \sim 0.0\,dex$ at the end of core He-burning stage, thereby revealing the $\sim 99\%$ of the observed Li abundance distribution. The thermohaline mixing regulates the Li abundance of RGB stars, which combines with the internal gravity waves can explain the Li abundances of most giants.

astro-ph.SR↗

arXiv2024

Convective Mixing: The Formation Channel of Li-rich Giants

Increasing observed data indicate that part of giants has abnormally high lithium (Li) inside their surface, and their proportion is around 1%. Instead of pursuing the feasible mechanisms for extra Li enrichment, we focus on how to inhibit Li depletion from the main sequence (MS) phase of giants. With this in mind, we find that convective mixing is capable of achieving this goal and forming Li-rich giants, which is mainly attributed to the convection model with the convective boundary defined by the Ledoux criterion. Another factor in the formation of Li-rich giants in our convection models is related to the Li abundances of their progenitors. If the Li abundances of the progenitors exceed the meteoritic value (3.3 dex), then the majority of giants will be rich in Li. This is the general pattern of stellar Li abundance evolution without factoring in extra Li depletion. We propose that other Li depletion processes should also be adopted in the future, as they may be the key to the 1% puzzle.

astro-ph.SR↗

arXiv2023

Toward Understanding BERT-Like Pre-Training for DNA Foundation Models

With the success of large-scale pre-training in language tasks, there is an increasing trend of applying it to the domain of life sciences. In particular, pre-training methods based on DNA sequences have received increasing attention because of their potential to capture general information about genes. However, existing pre-training methods for DNA sequences largely rely on direct adoptions of BERT pre-training from NLP, lacking a comprehensive understanding and a specifically tailored approach. To address this research gap, we provide the first empirical study with three insightful observations. Based on the empirical study, we notice that overlapping tokenizer can benefit the fine-tuning of downstream tasks but leads to inadequate pre-training with fast convergence. To unleash the pre-training potential, we introduce a novel approach called RandomMask, which gradually increases the task difficulty of BERT-like pre-training by continuously expanding its mask boundary, forcing the model to learn more knowledge. RandomMask is simple but effective, achieving state-of-the-art performance across 6 downstream tasks. RandomMask achieves a staggering 68.16\% in Matthew's correlation coefficient for Epigenetic Mark Prediction, a groundbreaking increase of 19.85\% over the baseline and a remarkable 3.69\% improvement over the previous state-of-the-art result.

cs.AI↗

arXiv2023

Rethinking Cross-Domain Pedestrian Detection: A Background-Focused Distribution Alignment Framework for Instance-Free One-Stage Detectors

Cross-domain pedestrian detection aims to generalize pedestrian detectors from one label-rich domain to another label-scarce domain, which is crucial for various real-world applications. Most recent works focus on domain alignment to train domain-adaptive detectors either at the instance level or image level. From a practical point of view, one-stage detectors are faster. Therefore, we concentrate on designing a cross-domain algorithm for rapid one-stage detectors that lacks instance-level proposals and can only perform image-level feature alignment. However, pure image-level feature alignment causes the foreground-background misalignment issue to arise, i.e., the foreground features in the source domain image are falsely aligned with background features in the target domain image. To address this issue, we systematically analyze the importance of foreground and background in image-level cross-domain alignment, and learn that background plays a more critical role in image-level cross-domain alignment. Therefore, we focus on cross-domain background feature alignment while minimizing the influence of foreground features on the cross-domain alignment stage. This paper proposes a novel framework, namely, background-focused distribution alignment (BFDA), to train domain adaptive onestage pedestrian detectors. Specifically, BFDA first decouples the background features from the whole image feature maps and then aligns them via a novel long-short-range discriminator.

cs.CV↗

arXiv2023

NLTE analysis for Y I and Y II in atmospheres of F-G-K stars

The non-local thermodynamical equilibrium (NLTE) line formation of Y I and Y II is considered in 1D LTE model atmospheres of F-G-K-type stars. The model atom was constructed with the most up-to-date atomic data, including quantum cross sections and rate coefficients for transitions in inelastic collisions of Y I and Y II with hydrogen atoms. For seven reference stars, we obtained an agreement between NLTE abundances inferred from the two ionization stages, while the difference in LTE abundance (Y I - Y II) can reach up to -0.31 dex. In the atmospheres of F-G-K-type stars, for both Y I and Y II lines, the NLTE abundance corrections are positive. In solar metallicity stars, the NLTE abundance corrections for Y II lines do not exceed 0.12 dex, while in atmospheres of metal-poor stars they do not exceed 0.21 dex. For Y I lines, the NLTE abundance corrections can reach up to 0.5 dex. We determined the yttrium NLTE abundances for a sample of 65 F and G dwarfs and subgiants in the -2.62~$\leq$~[Fe/H]~$\leq$~+0.24 metallicity range, using high-resolution spectra. For stars with [Fe/H]~$\leq$~-1.5, [Y/Fe] versus [Fe/H] diagram reveals positive trend with an average value of [Y/Fe]~$\simeq$~0. For metal-poor stars, among Sr, Y, and Zr, the arrangement [Sr/Fe] < [Y/Fe] < [Zr/Fe] remains consistent. The current study is useful for the Galactic chemical evolution research. The model atom will be applied for NLTE yttrium abundance determination in very metal-poor stars studied with LAMOST and Subaru.

astro-ph.SR↗

arXiv2023

The radio detection and accretion properties of the peculiar nuclear transient AT 2019avd

AT 2019avd is a nuclear transient detected from infrared to soft X-rays, though its nature is yet unclear. The source has shown two consecutive flaring episodes in the optical and the infrared bands and its second flare was covered by X-ray monitoring programs. During this flare, the UVOT/Swift photometries revealed two plateaus: one observed after the peak and the other one appeared ~240 days later. Meanwhile, our NICER and XRT/Swift campaigns show two declines in the X-ray emission, one during the first optical plateau and one 70-90 days after the optical/UV decline. The evidence suggests that the optical/UV could not have been primarily originated from X-ray reprocessing. Furthermore, we detected a timelag of ~16-34 days between the optical and UV emission, which indicates the optical likely comes from UV reprocessing by a gas at a distance of 0.01-0.03 pc. We also report the first VLA and VLBA detection of this source at different frequencies and different stages of the second flare. The information obtained in the radio band - namely a steep and a late-time inverted radio spectrum, a high brightness temperature and a radio-loud state at late times - together with the multiwavelength properties of AT 2019avd suggests the launching and evolution of outflows such as disc winds or jets. In conclusion, we propose that after the ignition of black hole activity in the first flare, a super-Eddington flaring accretion disc formed and settled to a sub-Eddington state by the end of the second flare, associated with a compact radio outflow.

astro-ph.HE↗

arXiv2022

Li-rich Giants in LAMOST Survey. III. The statistical analysis of Li-rich giants

The puzzle of Li-rich giant is still unsolved, contradicting the prediction of the standard stellar models. Although the exact evolutionary stages play a key role in the knowledge of Li-rich giants, a limited number of Li-rich giants have been taken with high-quality asteroseismic parameters to clearly distinguish the stellar evolutionary stages. Based on the LAMOST Data Release 7 (DR7), we applied a data-driven neural network method to derive the parameters for giant stars, which contain the largest number of Li-rich giants. The red giant stars are classified into three stages of Red Giant Branch (RGB), Primary Red Clump (PRC), and Secondary Red Clump (SRC) relying on the estimated asteroseismic parameters. In the statistical analysis of the properties (i.e. stellar mass, carbon, nitrogen, Li-rich distribution, and frequency) of Li-rich giants, we found that: (1) Most of the Li-rich RGB stars are suggested to be the descendants of Li-rich pre-RGB stars and/or the result of engulfment of planet or substellar companions; (2) The massive Li-rich SRC stars could be the natural consequence of Li depletion from the high-mass Li-rich RGB stars. (3) Internal mixing processes near the helium flash can account for the phenomenon of Li-rich on PRC that dominated the Li-rich giants. Based on the comparison of [C/N] distributions between Li-rich and normal PRC stars, the Li-enriched processes probably depend on the stellar mass.

astro-ph.SR↗

arXiv2022

Overview of the LAMOST survey in the first decade

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), also known as the Guoshoujing Telescope, is a major national scientific facility for astronomical research located in Xinglong, China. Beginning with a pilot survey in 2011, LAMOST has been surveying the night sky for more than 10 years. The LAMOST survey covers various objects in the Universe, from normal stars to peculiar ones, from the Milky Way to other galaxies, and from stellar black holes and their companions to quasars that ignite ancient galaxies. Until the latest data release 8, the LAMOST survey has released spectra for more than 10 million stars, ~220,000 galaxies, and ~71,000 quasars. With this largest celestial spectra database ever constructed, LAMOST has helped astronomers to deepen their understanding of the Universe, especially for our Milky Way galaxy and the millions of stars within it. In this article, we briefly review the characteristics, observations, and scientific achievements of LAMOST. In particular, we show how astrophysical knowledge about the Milky Way has been improved by LAMOST data.

astro-ph.GA↗