arXiv subjects

Jiao Li

Publications and source records attributed to Jiao Li.

At least 37 records · Page 2Linked to original sources

arXiv2025

Orbital Parameters of 665 Double-lined Spectroscopic Binaries in the LAMOST Medium-Resolution Survey

The period, mass ratio, eccentricity, and other orbital parameters are fundamental for investigating binary star evolution. However, the number of binaries with known orbital parameters remains limited. Utilizing the LAMOST-MRS survey, we derived orbital solutions for 665 SB2 binaries by fitting the radial velocities of 1119 SB2 systems with at least six observations, employing a modified version of Thejoker optimized for SB2 binaries. To ensure the reliability of the results, four selection criteria were applied: reduced chi-square, normalized mean absolute error, maximum phase gap, and RV distribution metric. After applying these criteria, 665 reliable orbits were retained. Comparison with Kepler, TESS, and ZTF light curve data shows excellent agreement, with discrepancies in some cases attributed to shorter pulsation periods observed in light curves. Additionally, good consistency is found between our periods and those of SB1 systems in Gaia data. These orbital solutions contribute to understanding binary star evolution and the statistical properties of binary populations.

astro-ph.SR↗

arXiv2025

Mass, Luminosity, and Stellar Age of Early-type Stars from the LAMOST Survey

Mass ($M$) and luminosity ($L$) are fundamental parameters of stars but can only be measured indirectly. Typically, effective temperature ($T_{\rm eff}$), surface gravity (${\rm log}\ g$) and metallicity ([M/H]) are derived from stellar spectra, then $M$, $L$ and stellar age ($t$) can be obtained by interpolating in the grid of stellar evolutionary models. In this paper, we use the Random Forest (RF) in combination with the evolutionary grid from PARSEC 1.2S to determine $M$, $L$, $t$ and initial mass ($M_{\rm i}$) for early-type main-sequence stars ($T_{\rm eff}\geq 7,000\ {\rm K}$) as identified from the LAMOST survey. The convex hull algorithm is employed to select the main-sequence stars. The results demonstrate that the prediction precision is $32\%$ and $9\%$ for $L$ and $M$, respectively, which is comparable to that achieved by fitting evolutionary tracks. Furthermore, the predicted $L$ also aligns with Gaia's observations, with a relative difference of $36\%$. The prediction for $t$ is relatively less accurate, indicating a difference of 0.44 Gyr for two components in wide binaries. This discrepancy is due to the inconsistent metallicity measurements. For the two sets of atmospheric parameters we used, the relative differences in $L$, $M$, $t$ and $M_{\rm i}$ are $29\%$, $7\%$, $36\%$ and $7\%$, respectively. The influence of metallicity on these parameters is analyzed, with the conclusion that metallicity has the greatest impact on $t$. Consequently, two catalogs are presented, which would be useful for studying stellar populations such as the luminosity function and initial mass function of early-type stars.

astro-ph.SR↗

arXiv2025

Surface brightness-color relations for red giant branch stars using asteroseismic radii and Gaia distances (the ARD method)

Aims. Asteroseismic radius and Gaia distance (ARD) method has been proposed to establish the SBCRs for late-type stars. Methods. We select Kepler RGB stars with high-precision asteroseismic radii (uncertainties < 1%) and cross-match them with 2MASS, APASS, and Gaia to obtain Johnson-B, Johnson-V, G, J, H, and Ks-band photometric data. After applying selection criteria, we obtain 626 RGB stars to build the SBCR. Among these, 100 RGBs are used as independent validation for the distance, and the remaining samples are used to fit the SBCR. Results. First, using 526 targets with asteroseismic radii and Gaia distances, nine SBCRs are proposed based on 2MASS (J, H, Ks), APASS (Johnson-B, Johnson-V), and Gaia (G) photometry. The average rms scatter in these relations is 0.075 mag, which corresponds to an uncertainty of approximately 3.5% in distance. These relations are further validated using 100 independent samples with Gaia distances, showing no bias, with a dispersion of approximately 3%. Compared to interferometric measurements, a systematic underestimation of 2.3% was observed, and the discrepancy decreases as the angular diameter increases. Additionally, the distances of eclipsing binaries in the Large Magellanic Cloud and Small Magellanic Cloud obtained using our SBCRs are generally consistent with those measured in the literature, with a dispersion of 1% and a slight overestimation of 1% to 2.5%. Conclusions. The ARD method capitalizes on two key advantages for precise stellar distance determination: a statistically robust sample of homogeneous RGB stars with low observational costs, and independent distance verification through Gaia data. Such SBCRs can be further calibrated and expanded more efficiently and effectively.

astro-ph.SR↗

arXiv2025

Detection of Ubiquitous Circumbinary Matter in Hot Subdwarfs Formed from Common-Envelope Ejections

The formation of compact binary systems is largely driven by their evolution through a common envelope (CE) phase, crucial for understanding phenomena such as type Ia supernovae and black hole mergers. Despite their importance, direct observational evidence for CE material has been elusive due to the transient nature of these envelopes. Numerical simulations suggest that some envelope material may persist post-ejection. In this study, we investigate circumstellar material (CSM) surrounding hot subdwarf (sdB) stars, focusing on material ejected during the CE phase of binary evolution. We analyze Ca II K absorption lines in 727 sdB candidates from the LAMOST-LRS survey, selecting 145 stars with strong absorption features, indicating the presence of CSM. We compare the velocities of the Ca II K lines with the systemic velocities of sdB binaries, confirming that the material originates from ejected common-envelope material. The results show that the CSM persists long after the CE event, suggesting the formation of a stable, long-lived circumstellar environment around sdB stars. This study enhances our understanding of the role of CSM in post-CE evolution and provides new insights into the physical processes shaping the evolution of sdB binaries.

astro-ph.SR↗

arXiv2024

The Red Supergiant Progenitor of the Type II Supernova 2024abfl

Linkage between core-collapse supernovae (SNe) and their progenitors is not fully understood and ongoing effort of searching and identifying the progenitors is needed. $\mathrm{SN\,2024abfl}$ is a recent Type II supernova exploded in the nearby star-bursting galaxy $\mathrm{NGC\,2146}$, which is also the host galaxy of $\mathrm{SN\,2018zd}$. From archival Hubble Space Telescope (HST) data, we have found a red source ($\mathrm{m_{F814W} \sim 25}$) near the location (angular distance $\leq 0.2"$) of $\mathrm{SN\,2024abfl}$ before its explosion. With F814W and F606W photometry, we found that the properties of this source matched a typical red supergiant (RSG) moderately reddened by interstellar dust at the distance of the host galaxy. We conclude that the $\mathrm{SN\,2024abfl}$ had an RSG progenitor with initial mass of $\mathrm{10M_{\odot}}$--$\mathrm{16\,M_{\odot}}$.

astro-ph.SR↗

arXiv2024

Discoveries and Properties of EL CVn-type Binaries in the TESS Survey

EL CVn-type systems represent a rare evolutionary stage in binary star evolution, providing ideal laboratories for investigating stable mass transfer processes and the formation of extremely low-mass white dwarfs (ELM WDs). The Transiting Exoplanet Survey Satellite (TESS) has delivered an extensive collection of high-precision time-domain photometric data, which is invaluable for studying EL CVn binaries. In this study, we identified 29 EL CVn systems from the TESS eclipsing binary catalogs (sectors 1-65), 11 of which are newly discovered. These systems consist of smaller, hotter pre-He white dwarfs and A/F main-sequence stars. The orbital periods of these binaries range from 0.64 to 2.5 days. Utilizing TESS light curves, Gaia distances, and multi-band photometric data (e.g., GALEX, 2MASS, WISE, SkyMapper), we modeled the light curves and spectral energy distributions to derive system parameters, including effective temperatures, masses, and radii. These systems were then compared with the white dwarf mass-period relation and the evolutionary tracks of ELM WDs. The comparison reveals that these binaries are consistent with the expected mass-period relation for white dwarfs and align well with the evolutionary tracks on the Teff-logg diagram for ELM WDs. This result suggests that these EL CVn systems likely formed through stable mass transfer processes. We provide a catalog of complete parameters for 29 EL CVn systems identified from the TESS survey. This catalog will serve as an essential resource for studying binary mass transfer, white dwarf formation, and pulsation phenomena in EL CVn-type systems.

astro-ph.SR↗

arXiv2024

Identifying Hierarchically Triple Star Systems with Gaia DR3 and LAMOST

Triple star systems are critical for understanding stellar dynamics and compact objects in astrophysics, yet confirmed hierarchical triples identified via spectroscopy remain limited. In this study, we identified 23 triple systems by cross-matching the Gaia DR3 non-single star catalog with LAMOST DR10 spectroscopic data; 18 of them are new discoveries. For two well-observed triples, we performed radial velocity curve fitting and light curve analysis to determine their orbital parameters, with inner and outer periods of 1.26 days and 656 days for one triple, and 3.42 days and 422 days for the other. We compared the results with other studies. We also analyzed the radial velocities (RVs) of these 23 triples, revealing a range of $V$ from approximately 40~km~s$^{-1}$ to 210~km~s$^{-1}$. Due to spectral resolution and detection limitations, velocity differences below 45~km~s$^{-1}$ in binaries and below 90~km~s$^{-1}$ in the inner binaries of triple systems are challenging to detect. Consequently, our detection range for inner orbital periods is restricted to 0.2--20 days, with the highest efficiency for periods under 10 days. These findings underscore the advantage of spectroscopic observations for identifying triple systems with short inner orbital periods.

astro-ph.SR↗

arXiv2024

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation

Semi-supervised learning (SSL) has made notable advancements in medical image segmentation (MIS), particularly in scenarios with limited labeled data and significantly enhancing data utilization efficiency. Previous methods primarily focus on complex training strategies to utilize unlabeled data but neglect the importance of graph structural information. Different from existing methods, we propose a graph-based clustering for semi-supervised medical image segmentation (GraphCL) by jointly modeling graph data structure in a unified deep model. The proposed GraphCL model enjoys several advantages. Firstly, to the best of our knowledge, this is the first work to model the data structure information for semi-supervised medical image segmentation (SSMIS). Secondly, to get the clustered features across different graphs, we integrate both pairwise affinities between local image features and raw features as inputs. Extensive experimental results on three standard benchmarks show that the proposed GraphCL algorithm outperforms state-of-the-art semi-supervised medical image segmentation methods.

cs.CV↗

arXiv2024

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation

In semi-supervised domain adaptation (SSDA), the model aims to leverage partially labeled target domain data along with a large amount of labeled source domain data to enhance its generalization capability for the target domain. A key advantage of SSDA is its ability to significantly reduce reliance on labeled data, thereby lowering the costs and time associated with data preparation. Most existing SSDA methods utilize information from domain labels and class labels but overlook the structural information of the data. To address this issue, this paper proposes a graph learning perspective (AGLP) for semi-supervised domain adaptation. We apply the graph convolutional network to the instance graph which allows structural information to propagate along the weighted graph edges. The proposed AGLP model has several advantages. First, to the best of our knowledge, this is the first work to model structural information in SSDA. Second, the proposed model can effectively learn domain-invariant and semantic representations, reducing domain discrepancies in SSDA. Extensive experimental results on multiple standard benchmarks demonstrate that the proposed AGLP algorithm outperforms state-of-the-art semi-supervised domain adaptation methods.

cs.CV↗

arXiv2024

SPEAK: Speech-Driven Pose and Emotion-Adjustable Talking Head Generation

Most earlier researches on talking face generation have focused on the synchronization of lip motion and speech content. However, head pose and facial emotions are equally important characteristics of natural faces. While audio-driven talking face generation has seen notable advancements, existing methods either overlook facial emotions or are limited to specific individuals and cannot be applied to arbitrary subjects. In this paper, we propose a novel one-shot Talking Head Generation framework (SPEAK) that distinguishes itself from the general Talking Face Generation by enabling emotional and postural control. Specifically, we introduce Inter-Reconstructed Feature Disentanglement (IRFD) module to decouple facial features into three latent spaces. Then we design a face editing module that modifies speech content and facial latent codes into a single latent space. Subsequently, we present a novel generator that employs modified latent codes derived from the editing module to regulate emotional expression, head poses, and speech content in synthesizing facial animations. Extensive trials demonstrate that our method ensures lip synchronization with the audio while enabling decoupled control of facial features, it can generate realistic talking head with coordinated lip motions, authentic facial emotions, and smooth head movements. The demo video is available: https://anonymous.4open.science/r/SPEAK-8A22

cs.CV↗

arXiv2024

Atrial Fibrillation Detection System via Acoustic Sensing for Mobile Phones

Atrial fibrillation (AF) is characterized by irregular electrical impulses originating in the atria, which can lead to severe complications and even death. Due to the intermittent nature of the AF, early and timely monitoring of AF is critical for patients to prevent further exacerbation of the condition. Although ambulatory ECG Holter monitors provide accurate monitoring, the high cost of these devices hinders their wider adoption. Current mobile-based AF detection systems offer a portable solution, however, these systems have various applicability issues such as being easily affected by environmental factors and requiring significant user effort. To overcome the above limitations, we present MobileAF, a novel smartphone-based AF detection system using speakers and microphones. In order to capture minute cardiac activities, we propose a multi-channel pulse wave probing method. In addition, we enhance the signal quality by introducing a three-stage pulse wave purification pipeline. What's more, a ResNet-based network model is built to implement accurate and reliable AF detection. We collect data from 23 participants utilizing our data collection application on the smartphone. Extensive experimental results demonstrate the superior performance of our system, with 97.9% accuracy, 96.8% precision, 97.2% recall, 98.3% specificity, and 97.0% F1 score.

cs.SD↗

arXiv2024

Open-World Test-Time Training: Self-Training with Contrast Learning

Traditional test-time training (TTT) methods, while addressing domain shifts, often assume a consistent class set, limiting their applicability in real-world scenarios characterized by infinite variety. Open-World Test-Time Training (OWTTT) addresses the challenge of generalizing deep learning models to unknown target domain distributions, especially in the presence of strong Out-of-Distribution (OOD) data. Existing TTT methods often struggle to maintain performance when confronted with strong OOD data. In OWTTT, the focus has predominantly been on distinguishing between overall strong and weak OOD data. However, during the early stages of TTT, initial feature extraction is hampered by interference from strong OOD and corruptions, resulting in diminished contrast and premature classification of certain classes as strong OOD. To address this, we introduce Open World Dynamic Contrastive Learning (OWDCL), an innovative approach that utilizes contrastive learning to augment positive sample pairs. This strategy not only bolsters contrast in the early stages but also significantly enhances model robustness in subsequent stages. In comparison datasets, our OWDCL model has produced the most advanced performance.

cs.LG↗

arXiv2024

KAN v.s. MLP for Offline Reinforcement Learning

Kolmogorov-Arnold Networks (KAN) is an emerging neural network architecture in machine learning. It has greatly interested the research community about whether KAN can be a promising alternative of the commonly used Multi-Layer Perceptions (MLP). Experiments in various fields demonstrated that KAN-based machine learning can achieve comparable if not better performance than MLP-based methods, but with much smaller parameter scales and are more explainable. In this paper, we explore the incorporation of KAN into the actor and critic networks for offline reinforcement learning (RL). We evaluated the performance, parameter scales, and training efficiency of various KAN and MLP based conservative Q-learning (CQL) on the the classical D4RL benchmark for offline RL. Our study demonstrates that KAN can achieve performance close to the commonly used MLP with significantly fewer parameters. This provides us an option to choose the base networks according to the requirements of the offline RL tasks.

cs.LG↗

arXiv2024

AcousAF: Acoustic Sensing-Based Atrial Fibrillation Detection System for Mobile Phones

Atrial fibrillation (AF) is characterized by irregular electrical impulses originating in the atria, which can lead to severe complications and even death. Due to the intermittent nature of the AF, early and timely monitoring of AF is critical for patients to prevent further exacerbation of the condition. Although ambulatory ECG Holter monitors provide accurate monitoring, the high cost of these devices hinders their wider adoption. Current mobile-based AF detection systems offer a portable solution. However, these systems have various applicability issues, such as being easily affected by environmental factors and requiring significant user effort. To overcome the above limitations, we present AcousAF, a novel AF detection system based on acoustic sensors of smartphones. Particularly, we explore the potential of pulse wave acquisition from the wrist using smartphone speakers and microphones. In addition, we propose a well-designed framework comprised of pulse wave probing, pulse wave extraction, and AF detection to ensure accurate and reliable AF detection. We collect data from 20 participants utilizing our custom data collection application on the smartphone. Extensive experimental results demonstrate the high performance of our system, with 92.8% accuracy, 86.9% precision, 87.4% recall, and 87.1% F1 Score.

cs.SD↗

arXiv2024

The First Photometric Analysis of Two Low Mass Ratio Contact Binary Systems In TESS Survey

Low mass-ratio (q) contact binary systems are progenitors of stellar mergers such as blue straggles (BS) or fast-rotating FK Com stars. In this study, we present the first light curve analysis of two newly identified low mass-ratio contact binary systems, TIC 55007847 and TIC 63597006, that are identified from TESS. Both stars are classified as A-subtype contact binaries. We obtained the precise orbit periods for the two objects by using the O-C method, i.e. P=0.6117108 d for TIC 55007847 and P=0.7008995 d for TIC 63597006, respectively, and found an obvious periodic signal in the O-C curve of TIC 63597006. We suggest that the periodic signal comes from a third body. We further use the Markov Chain Monte Carlo (MCMC) method with PHOEBE to derive the photometric solutions for the two binaries. The photometric solution for this object shows that the contribution of the third body is about 6%. Our analysis revealed that TIC 55007847 has an extremely low mass ratio of q=0.08. By calculating the ratio of spin angular momentum to the orbital angular momentum Js/Jo, we found that TIC 55007847 is very close to the instability threshold with Js/Jo = 0.31, indicating that it may merge into a single, fast-rotating star in the future. For TIC 63597006, q=0.14 and Js/Jo=0.15. This object is in a relatively stable evolutionary status at present.

astro-ph.SR↗

arXiv2024

Accurate adaptive deep learning method for solving elliptic problems

Deep learning method is of great importance in solving partial differential equations. In this paper, inspired by the failure-informed idea proposed by Gao et.al. (SIAM Journal on Scientific Computing 45(4)(2023)) and as an improvement, a new accurate adaptive deep learning method is proposed for solving elliptic problems, including the interface problems and the convection-dominated problems. Based on the failure probability framework, the piece-wise uniform distribution is used to approximate the optimal proposal distribution and an kernel-based method is proposed for efficient sampling. Together with the improved Levenberg-Marquardt optimization method, the proposed adaptive deep learning method shows great potential in improving solution accuracy. Numerical tests on the elliptic problems without interface conditions, on the elliptic interface problem, and on the convection-dominated problems demonstrate the effectiveness of the proposed method, as it reduces the relative errors by a factor varying from $10^2$ to $10^4$ for different cases.

math.NA↗

arXiv2024

A born ultramassive white dwarf-hot subdwarf super-Chandrasekhar candidate

Although supernovae is a well-known endpoint of an accreting white dwarf, alternative theoretical possibilities has been discussing broadly, such as the accretion-induced collapse (AIC) event as the endpoint of oxygen-neon (ONe) white dwarfs, either accreting up to or merging to excess the Chandrasekhar limit (the maximum mass of a stable white dwarf). AIC is an important channel to form neutron stars, especially for those unusual systems, which are hardly produced by core-collapse supernovae. However, the observational evidences for this theoretical predicted event and its progenitor are all very limited. In all of the known progenitors, white dwarfs increase in mass by accretion. Here, we report the discovery of an intriguing binary system Lan 11, consisted of a stripped core-helium-burning hot subdwarf and an unseen compact object of 1.08 to 1.35 $M_{\odot}$. Our binary population synthesis calculations, along with the absence of detection from the deep radio observations of the Five-hundred-meter Aperture Spherical Radio Telescope, strongly suggest that the latter is an ONe white dwarf. The total mass of this binary is 1.67 to 1.92 $M_{\odot}$}, significantly excessing the Chandrasekhar limit. The reproduction of its evolutionary history indicates that the unique system has undergone two phases of common envelope ejections, implying a born nature of this massive ONe white dwarf rather than an accretion growth from its companion. These results, together with short orbital period of this binary (3.65 hours), suggest that this system will merge in 500-540 Myr, largely triggering an AIC event, although the possibility of type Ia supernova cannot be fully ruled out. This finding greatly provides valuable constraints on our understanding of stellar endpoints, whatever leading to an AIC or a supernova.

astro-ph.SR↗

arXiv2024

Derivative Spectroscopy and its Application at Detecting the Weak Emission/Absorption Lines

The development of spectroscopic survey telescopes like Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), Apache Point Observatory Galactic Evolution Experiment and Sloan Digital Sky Survey has opened up unprecedented opportunities for stellar classification. Specific types of stars, such as early-type emission-line stars and those with stellar winds, can be distinguished by the profiles of their spectral lines. In this paper, we introduce a method based on derivative spectroscopy (DS) designed to detect signals within complex backgrounds and provide a preliminary estimation of curve profiles. This method exhibits a unique advantage in identifying weak signals and unusual spectral line profiles when compared to other popular line detection methods. We validated our approach using synthesis spectra, demonstrating that DS can detect emission signals three times fainter than Gaussian fitting. Furthermore, we applied our method to 579,680 co-added spectra from LAMOST Medium-Resolution Spectroscopic Survey, identifying 16,629 spectra with emission peaks around the Hα line from 10,963 stars. These spectra were classified into three distinct morphological groups, resulting in nine subclasses as follows. (1) Emission peak above the pseudo-continuum line (single peak, double peaks, emission peak situated within an absorption line, P Cygni profile, Inverse P Cygni profile); (2) Emission peak below the pseudo-continuum line (sharp emission peak, double absorption peaks, emission peak shifted to one side of the absorption line); (3) Emission peak between the pseudo-continuum line.

astro-ph.SR↗