arXiv subjects

A-Li Luo

Publications and source records attributed to A-Li Luo.

At least 19 recordsLinked to original sources

arXiv2026

KIC 6464285: A Solar-type Eclipsing Binary in a Hierarchical Triple System with Quasi-periodic Out-of-eclipse Modulations

We present the first detailed analysis of the solar-type triple system KIC 6464285. Combining long-term, high-precision photometry from Kepler, TESS, and ZTF with low-resolution spectra from LAMOST and near-infrared high-resolution spectra from SDSS/APOGEE, we performed a joint analysis of the light curves, eclipse timing variations (ETVs), and radial velocities. Spectroscopic analysis reveals the system to be triple-lined, with the inner binary's primary being a G-type main-sequence star and a mass ratio of $0.627(7)$. Light curve modeling indicates that the inner binary is detached, with filling factors of approximately 26\% and 11\% for the primary and secondary, respectively, and a tertiary light contribution of about 27\%. ETV analysis shows a significant light-travel-time effect (LTTE), consistent with the presence of the tertiary companion, whose minimum mass is estimated as $M_{3,\rm min}=0.74(1)~M_\odot$. The light curve exhibits a pronounced O'Connell effect and quasi-periodic variations, indicative of starspot activity modulating the photometry on a $\sim$131-day timescale. Kepler observations further reveal 30 superflares, each with total energies exceeding $10^{34}$ erg. This study presents detailed observational constraints on the orbital configuration, stellar properties, and magnetic activity of KIC 6464285, providing a benchmark for studies of hierarchical triple systems.

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arXiv2026

A Gaia-linked High-purity QSO Candidate Catalog in Selected Fields with Extinction-binned Calibration and Spectrum-informed Training

We present an extinction-calibrated, Gaia-source-level QSO candidate catalog for selected fields, designed as a high-purity input catalog for fiber-spectroscopic follow-up rather than as an all-sky QSO census. The deployed selector uses Gaia astrometry and photometry, optical/infrared catalog features, and E(B-V)-binned threshold calibration; spectra are used only during training via a source-grouped spectrum-teacher model. The sample definition is layered: a four-field core domain ladder provides the main validation baseline, four application/stress-test fields probe portability, and COSMOS is treated separately as an Extreme Deep boundary case. At the recommended conservative operating point, calibrated to a validation-set purity of 0.98, the P3 spectrum-informed catalog selector achieves a measured test-set purity of 0.9809 and a spectroscopic-label completeness of 0.8869 within the frozen Gaia-linked benchmark, whereas the Gaia official QSO probability yields a spectroscopic-label completeness of 0.4493 under the same threshold protocol. The evaluation protocol excludes downstream validation/test Gaia source IDs from teacher fitting and checkpoint selection, and uses teacher probabilities only for downstream training rows. Relative to the earlier P2 teacher, P3 yields a modest mean completeness gain across five seeds, with a small decrease in purity and a small increase in false positives; the gain is most evident in higher-extinction and faint-source diagnostics. The released product is a catalog and empirical selection-function data product with source identifiers, field-layer assignments, input-coverage flags, calibrated scores, threshold flags, validation metadata, and provenance/QC fields. In COSMOS, the Gaia-linked parent set is much shallower than COSMOS2020; the robust 39-object subset is interpreted as a purity-oriented priority list rather than a completeness measurement.

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arXiv2026

Spectra as Language: Large Language Models for Scalable Stellar Parameter and Abundance Inference

Stellar spectra encode key information on the physical properties and chemical compositions of stars. Accurate stellar parameter determination is essential for addressing major questions such as galaxy and stellar evolution. Large-scale spectroscopic surveys have accumulated unprecedented spectral data. Traditional feature extraction or model-fitting approaches struggle with high-dimensional, massive datasets, limited generalization, and computational inefficiency. Recent advances in large language models demonstrate strong generalization and feature-learning in tasks like natural language processing, DNA/RNA sequence analysis, and protein/chemical parsing. Stellar spectra are continuous sequential signals, enabling the transfer of language models to stellar spectroscopy. Here, we propose a two-stage large language model framework for stellar parameter inference, achieving accurate estimation of effective temperature, surface gravity, metallicity, and abundances of ~20 chemical elements. Scaling-law analyses show systematic performance improvements with increasing data, providing a scalable framework for forthcoming large-scale surveys.

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arXiv2026

PISP: Projected-Space Inference of Stellar Parameters

To improve the accuracy and efficiency of high-dimensional stellar parameter inference in large spectroscopic datasets, we propose a projection-assisted parameter-inference framework -- Projected-Space Inference of Stellar Parameters (PISP). PISP constructs an orthonormal basis and optimizes in the projected space, reducing the impact of parameter correlations on inference. The basis is constructed using either principal component analysis (PCA) or the active-subspace (AS) method and is combined with two inference strategies -- Non-L1, which optimizes the projection coefficients for a user-specified projected dimensionality, and L1, which introduces L1 regularization in the full projected space to adaptively select projection directions -- yielding four strategies: PCA-Non-L1, AS-Non-L1, PCA-L1, and AS-L1. For different computational scenarios, we implement two versions: PISP-CurveFit for fast single-spectrum inference and PISP-Adam for large-scale GPU-parallel inference. Using a fully connected neural network and a residual network as spectral emulators, we evaluate PISP on Kurucz synthetic spectra and on $722{,}896$ APOGEE DR$17$ observed spectra. Compared to the baseline strategy, PISP improves inference accuracy for multiple parameters across all emulator-optimizer combinations. In synthetic data, PCA-L1 performs best, reducing the standard deviation of differences ($\sigma(\Delta)$) by at least $0.01$ dex for $12$ of $20$ elemental abundances, with [N/H], [O/H], [Na/H], [Co/H], [P/H], [V/H], [Cu/H] showing $0.05$--$0.72$ dex reductions. In observed data, PCA-Non-L1 reduces $\sigma(\Delta)$ by $>30$ K for effective temperature and by at least $0.01$ dex for $9$ of $17$ elemental abundances, with [O/H], [Na/H], [V/H] showing $0.05$--$0.20$ dex reductions, while achieving a $\sim$$4\times$ efficiency gain, slightly outperforming PCA-L1.

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arXiv2026

PhDLspec: physical-prior embedded deep learning method for spectroscopic determination of stellar labels in high-dimensional parameter space

Unlocking the full physical information encoded in low-resolution spectra poses a significant challenge for astronomical survey analysis. Such a task demands modeling spectra and optimizing astrophysical parameters in high-dimensional space, as a consequence of line blending. Here we present PhDLspec -- a deep learning framework embedded with physical priors for stellar spectra modeling and analysis. By imposing differential spectra derived from ab initio stellar atmospheric model calculation on a transformer framework, PhDLspec can rigorously and precisely model stellar spectra by simultaneously taking into account more than 30 physical parameters, at a computational speed hundreds of times faster than ab initio model calculation. With such a flexible stellar modeling approach, PhDLspec can effectively derive ~30 stellar labels from a low-resolution spectrum using affordable optimization techniques. Application to LAMOST spectra (R~1800) yields stellar elemental abundances in good agreement with high-resolution spectroscopic surveys, following essential calibrations to correct systematic biases in elemental abundance estimates using wide binaries and reference high-resolution datasets. We provide a catalog of 25 elemental abundances for 116,611 subgiant stars with precise age estimates. The successful application of PhDLspec to LAMOST spectra for high-dimensional parameter determination sheds light on similar challenges faced by other surveys and disciplines.

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arXiv2025

Scalable Stellar Parameter Inference Using Python-based LASP: From CPU Optimization to GPU Acceleration

To enhance the efficiency, scalability, and cross-survey applicability of stellar parameter inference in large spectroscopic datasets, we present a modular, parallelized Python framework with automated error estimation, built on the LAMOST Atmospheric Parameter Pipeline (LASP) originally implemented in IDL. Rather than a direct code translation, this framework refactors LASP with two complementary modules: LASP-CurveFit, a new implementation of the LASP fitting procedure that runs on a CPU, preserving legacy logic while improving data I/O and multithreaded execution efficiency; and LASP-Adam-GPU, a GPU-accelerated method that introduces grouped optimization by constructing a joint residual function over multiple observed and model spectra, enabling high-throughput parameter inference across tens of millions of spectra. Applied to 10 million LAMOST spectra, the framework reduces runtime from 84 to 48 hr on the same CPU platform and to 7 hr on an NVIDIA A100 GPU, while producing results consistent with those from the original pipeline. The inferred errors agree well with the parameter variations from repeat observations of the same target (excluding radial velocities), while the official empirical errors used in LASP are more conservative. When applied to DESI DR1, our effective temperatures and surface gravities agree better with APOGEE than those from the DESI pipeline, particularly for cool giants, while the latter performs slightly better in radial velocity and metallicity. These results suggest that the framework delivers reliable accuracy, efficiency, and transferability, offering a practical approach to parameter inference in large spectroscopic surveys. The code and DESI-based catalog are available via \dataset[DOI: 10.12149/101679]{https://doi.org/10.12149/101679} and \dataset[DOI: 10.12149/101675]{https://doi.org/10.12149/101675}, respectively.

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arXiv2025

Estimating stellar atmospheric parameters and elemental abundances using fully connected residual network

Stellar atmospheric parameters and elemental abundances are traditionally determined using template matching techniques based on high-resolution spectra. However, these methods are sensitive to noise and unsuitable for ultra-low-resolution data. Given that the Chinese Space Station Telescope (CSST) will acquire large volumes of ultra-low-resolution spectra, developing effective methods for ultra-low-resolution spectral analysis is crucial. In this work, we investigated the Fully Connected Residual Network (FCResNet) for simultaneously estimating atmospheric parameters ($T_\text{eff}$, $\log g$, [Fe/H]) and elemental abundances ([C/Fe], [N/Fe], [Mg/Fe]). We trained and evaluated FCResNet using CSST-like spectra (\textit{R} $\sim$ 200) generated by degrading LAMOST spectra (\textit{R} $\sim$ 1,800), with reference labels from APOGEE. FCResNet significantly outperforms traditional machine learning methods (KNN, XGBoost, SVR) and CNN in prediction precision. For spectra with g-band signal-to-noise ratio greater than 20, FCResNet achieves precisions of 78 K, 0.15 dex, 0.08 dex, 0.05 dex, 0.10 dex, and 0.05 dex for $T_\text{eff}$, $\log g$, [Fe/H], [C/Fe], [N/Fe] and [Mg/Fe], respectively, on the test set. FCResNet processes one million spectra in only 42 seconds while maintaining a simple architecture with just 348 KB model size. These results suggest that FCResNet is a practical and promising tool for processing the large volume of ultra-low-resolution spectra that will be obtained by CSST in the future.

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arXiv2025

Refined M-type Star Catalog from LAMOST DR10: Measurements of Radial Velocities, $T_\text{eff}$, log $g$, [M/H] and [$\alpha$/M]

Precise stellar parameters for M-type stars, the Galaxy's most common stellar type, are crucial for numerous studies. In this work, we refined the LAMOST DR10 M-type star catalog through a two-stage process. First, we purified the catalog using techniques including deep learning and color-magnitude diagrams to remove 22,496 non-M spectra, correct 2,078 dwarf/giant classifications, and update 12,900 radial velocities. This resulted in a cleaner catalog containing 870,518 M-type spectra (820,493 dwarfs, 50,025 giants). Second, applying a label transfer strategy using values from APOGEE DR16 for parameter prediction with a ten-fold cross-validated CNN ensemble architecture, we predicted $T_\text{eff}$, $\log g$, [M/H], and [$\alpha$/M] separately for M dwarfs and giants. The average internal errors for M dwarfs/giants are respectively: $T_\text{eff}$ 30/17 K, log $g$ 0.07/0.07 dex, [M/H] 0.07/0.05 dex, and [$\alpha$/M] 0.02/0.02 dex. Comparison with APOGEE demonstrates external precisions of 34/14 K, 0.12/0.07 dex, 0.09/0.04 dex, and 0.03/0.02 dex for M dwarfs/giants, which represents precision improvements of over 20\% for M dwarfs and over 50\% for M giants compared to previous literature results. The catalog is available at https://nadc.china-vo.org/res/r101668/.

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arXiv2025

Planets Across Space and Time (PAST). VIII : Kinematic Characterization and Identification of Radial Velocity Variables for the LAMOST-Gaia-TESS Stars

The Transiting Exoplanet Survey Satellite (TESS) has discovered over 6700 nearby exoplanets candidates using the transit method through its all-sky survey. Characterizing the kinematic properties and identifying variable stars for the TESS stellar sample is crucial for revealing the correlations between the properties of planetary systems and the properties of stars (e.g., Galactic components, age, chemistry, dynamics, radiation). Based on data from TESS, Gaia DR3, and LAMOST DR10, we present a catalog of kinematic properties (i.e., Galactic positions, velocities, orbits, Galactic components, and kinematic age) as well as other basic stellar parameters for $\sim 660,000$ TESS stars. Our analysis of the kinematic catalog reveals that stars belonging to different Galactic components (i.e., thin disk, thick disk, halo and 12 streams in the disk) display distinctive kinematic and chemical properties. We also find that hot planets with period less then 10 days in the TESS sample favor thin disk stars compared to thick disk stars, consistent with previous studies. Furthermore, using the LAMOST multiple-epoch observations, we identify 41,445 stars exhibiting significant radial velocity variations, among which 7,846 are classified as binary stars. By fitting the radial velocity curves, we further derive orbital parameters (e.g., mass ratio, orbital period and eccentricity) for 297 binaries. The observed decreasing orbital eccentricity with shorting period reveals evidence of tidal circularization. The catalogs constructed in this work have laid a solid foundation for future work on the formation and evolution of stellar and planetary systems in different Galactic environments.

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arXiv2025

SpecCLIP: Aligning and Translating Spectroscopic Measurements for Stars

In recent years, large language models (LLMs) have transformed natural language understanding through vast datasets and large-scale parameterization. Inspired by this success, we present SpecCLIP, a foundation model framework that extends LLM-inspired methodologies to stellar spectral analysis. Stellar spectra, akin to structured language, encode rich physical and chemical information about stars. By training foundation models on large-scale spectral datasets, our goal is to learn robust and informative embeddings that support diverse downstream applications. As a proof of concept, SpecCLIP involves pre-training on two spectral types--LAMOST low-resolution and Gaia XP--followed by contrastive alignment using the CLIP (Contrastive Language-Image Pre-training) framework, adapted to associate spectra from different instruments. This alignment is complemented by auxiliary decoders that preserve spectrum-specific information and enable translation (prediction) between spectral types, with the former achieved by maximizing mutual information between embeddings and input spectra. The result is a cross-spectrum framework enabling intrinsic calibration and flexible applications across instruments. We demonstrate that fine-tuning these models on moderate-sized labeled datasets improves adaptability to tasks such as stellar-parameter estimation and chemical-abundance determination. SpecCLIP also enhances the accuracy and precision of parameter estimates benchmarked against external survey data. Additionally, its similarity search and cross-spectrum prediction capabilities offer potential for anomaly detection. Our results suggest that contrastively trained foundation models enriched with spectrum-aware decoders can advance precision stellar spectroscopy. Our code SpecCLIP is publicly available at https://github.com/Xiaosheng-Zhao/SpecCLIP

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arXiv2025

Measurements of the Diffuse Interstellar Bands at 5780, 5797, and 6614 \r{A} in the Hot Stellar Spectra of the LAMOST LRS DR10

Diffuse Interstellar Bands (DIBs) are crucial tracers of the interstellar medium (ISM), yet their carriers remain poorly understood. While large-scale surveys have advanced DIB studies in cool stellar spectra, measurements in hot stellar spectra are still limited. Using 287 277 high signal-to-noise (S/N $>$ 50) hot stellar spectra from the tenth data release of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope low-resolution spectroscopic survey (LAMOST LRS DR10), we systematically measured the three prominent optical DIBs at 5780, 5797, and 6614 \r{A}. We published three catalogs containing 285 103, 279 195, and 281 146 valid measurements for the DIBs at 5780, 5797, and 6614 \r{A}, respectively. Among them, 112 479, 25 232, and 71 048 are high-quality samples after rigorous quality control. To our knowledge, these are the largest hot-star DIB datasets in the northern sky. The catalogs provide spectral metadata, added astrometeric information, DIB profiles, and quality metrics. Our methodology and open-source pipeline ensure reproducibility, while the scale and precision of the data support future statistical studies. We anticipate that these catalogs will highlight the LAMOST's role in advancing DIB research and deepening our understanding of the ISM.

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arXiv2025

New Symbiotic Stars from LAMOST DR10 Spectra and Multi-band Photometry

Symbiotic star (SySt) is long-period interacting binary system, typically consisting of a white dwarf and a red giant surrounded by a nebula. These systems are natural astrophysical laboratories for investigating binary star evolution. In this paper, we identified nine SySts from the LAMOST DR10 low-resolution spectra survey, seven of which were previously known, while two are newly identified. Initially, we selected LAMOST spectra exhibiting typical SySt emission lines (e.g., $\rm H_{\alpha}, ~H_{\beta}, ~H_{\gamma}, ~and ~He II$). Subsequently, we utilized the distribution of known SySts on the HR diagram to select SySt candidates, and visually inspected their spectra. Ultimately, we classified all nine as S-type SySts using the $J - H$ vs. $H - K$ diagram. Additionally, based on multi-band photometric data from GALEX, Gaia, 2MASS, ALLWISE, and several X-ray catalogs, we found 12 accreting-only SySt (acc-SySt) candidates, characterized by concurrent ultraviolet and infrared excess and accretion process. Furthermore, we estimated the white dwarf temperatures by fitting their observed SEDs using a combination of Kurucz stellar atmosphere model and Koester white dwarf model. We compared the accretion rates of acc-SySt candidates and confirmed SySts, and found they have similar accretion rate distribution, providing evidence that these acc-SySt candidates constitute bona fide SySts.

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arXiv2025

Half a Million M Dwarf Stars Characterized Using Domain-Adapted Spectral Analysis

We present fundamental atmospheric parameters (Teff and log g) and metallicities ([M/H]) for 507,513 M dwarf stars using low-resolution spectra (R~1800) from LAMOST DR10. By employing Cycle-StarNet, an innovative domain adaptation approach, we successfully bridge the gap between theoretical PHOENIX synthetic spectra and observed LAMOST spectra, enabling parameter measurements even for lower signal-to-noise data (S/N>5). The fitting residual analysis shows a reduction from 2.0 times to 1.68 times the flux uncertainty. Comparing with available literature values, we find systematic offsets and precisions of 12$\pm$70 K in Teff, -0.04$\pm$0.17 dex in log g, and -0.06$\pm$0.20 dex in [M/H]. The precision improves for higher quality spectra (S/N>50) to 47 K, 0.12 dex, and 0.14 dex respectively. The metallicity consistency between wide binaries shows a scatter of 0.24 dex, improving to 0.15 dex at S/N>50. We provide a comprehensive catalog including stellar parameters, spectral classifications, activity indicators, and binary/variability flags, establishing a resource for studies of the most numerous stellar population. The complete catalog is available at https://doi.org/10.5281/zenodo.14030249.

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arXiv2024

Estimating Stellar Atmospheric Parameters and [{\alpha}/Fe] for LAMOST O-M type Stars Using a Spectral Emulator

In this paper, we developed a spectral emulator based on the Mapping Nearby Galaxies at Apache Point Observatory Stellar Library (MaStar) and a grouping optimization strategy to estimate effective temperature (T_eff), surface gravity (log g), metallicity ([Fe/H]) and the abundance of alpha elements with respect to iron ([alpha/Fe]) for O-M-type stars within the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) low-resolution spectra. The primary aim is to use a rapid spectral-fitting method, specifically the spectral emulator with the grouping optimization strategy, to create a comprehensive catalog for stars of all types within LAMOST, addressing the shortcomings in parameter estimations for both cold and hot stars present in the official LAMOST AFGKM-type catalog. This effort is part of our series of studies dedicated to establishing an empirical spectral library for LAMOST. Experimental results demonstrate that our method is effectively applicable to parameter prediction for LAMOST, with the single-machine processing time within $70$ hr. We observed that the internal error dispersions for T_eff, log g, [Fe/H], and [alpha/Fe] across different spectral types lie within the ranges of $15-594$ K, $0.03-0.27$ dex, $0.02-0.10$ dex, and $0.01-0.04$ dex, respectively, indicating a good consistency. A comparative analysis with external data highlighted deficiencies in the official LAMOST catalog and issues with MaStar parameters, as well as potential limitations of our method in processing spectra with strong emission lines and bad pixels. The derived atmospheric parameters as a part of this work are available at https://nadc.china-vo.org/res/r101402/ .

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arXiv2024

An Empirical Sample of Spectra of M-type Stars with Homogeneous Atmospheric-Parameter Labels

The discrepancies between theoretical and observed spectra, and the systematic differences between various spectroscopic parameter estimates, complicate the determination of atmospheric parameters of M-type stars. In this work, we present an empirical sample of 5105 M-type star spectra with homogeneous atmospheric parameter labels through stellar-label transfer and sample cleaning. We addressed systematic discrepancies in spectroscopic parameter estimates by adopting recent results for Gaia EDR3 stars as a reference standard. Then, we used a density-based spatial clustering of applications with noise to remove unreliable samples in each subgrid of parameters. To confirm the reliability of the stellar labels, a 5-layer neural network was utilized, randomly partitioning the samples into training and testing sets. The standard deviations between the predicted and actual values in the testing set are 14 K for Teff , 0.06 dex for log g, and 0.05 dex for [M/H], respectively. In addition, we conducted an internal cross-validation to enhance validation and obtained precisions of 11 K, 0.05 dex, and 0.05 dex for Teff , log g, and [M/H], respectively. A grid of 1365 high Signal-to-Noise ratio (S/N) spectra and their labels, selected from the empirical sample, was utilized in the stellar parameter pipeline for M-Type stars (LASPM) of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), producing an almost seamless Kiel distribution diagram for LAMOST DR10 and DR11 data. The atmospheric parameters for M-type stars from LAMOST DR11 show improved precision compared to the data from DR9, with improvements (for spectra with S/N higher than 10) from 118 to 67 K in Teff , 0.2 to 0.07 dex in log g, and 0.29 to 0.14 dex in [M/H].

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arXiv2024

Distance and stellar parameter estimations of solar-like stars from the LAMOST spectroscopic survey

Context. The Gaia mission has opened up a new era for the precise astrometry of stars, thus revolutionizing our understanding of the Milky Way. However, beyond a few kiloparseconds from the Sun, parallax measurements become less reliable, and even within 2 kpc, there still exist stars with large uncertainties. Aims. Our aim was to determine the distance and stellar parameters of 521,424 solar-like stars from LAMOST DR9; these stars lacked precise distance measurements (uncertainties higher than 20\% or even without any distance estimations) when checked with Gaia. Methods. We proposed a convolutional neural network (CNN) model to predict the absolute magnitudes, colors, and stellar parameters (T_eff, logg, and [FeH]) directly from low-resolution spectra. For spectra with signal-to-noise ratios at g band (S/N_g) greater than 10, the model achieves a precision of 85 K for T_eff, 0.07 dex for logg, 0.06 dex for [Fe/H], 0.25 mag for M_g, and 0.03 mag for bp-rp. The estimated distances have a median fractional error of 4% with a standard deviation of 8%. Results: We applied the trained CNN model to 521,424 solar-like stars to derive the distance and stellar parameters. Compared with other distance estimation studies and spectroscopic surveys, the results show good consistency. Additionally, we investigated the metallicity gradients of the Milky Way from a subsample, and find a radial gradient ranging from -0.05 < Delta{[Fe/H]}/Delta{R} < 0.0 dex/kpc and a vertical gradient ranging from -0.26 < Delta{[Fe/H]}/Delta{Z} < -0.07dex/kpc Conclusions. We conclude that our method is effective in estimating distances and stellar parameters for solar-like stars with limited astrometric data. Our measurements are reliable for Galactic structure studies and hopefully will be useful for exoplanet researches.

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arXiv2024

Measuring the Diffuse Interstellar Bands at 5780, 5797, and 6614 {\AA} in Low-Resolution Spectra of Cool Stars from LAMOST

We attempt to measure the DIBs $\lambda$5780, $\lambda$5797 and $\lambda$6614 in over two million low-resolution spectra of cool stars from LAMOST. Based on the DIB measurements, the correlation between DIBs and extinction, the kinematics of DIBs, and the Galactic distribution of DIBs are reviewed and investigated from the perspective of statistics. A pipeline is developed to measure the DIBs $\lambda$5780, $\lambda$5797 and $\lambda$6614 in the LAMOST low-resolution spectra. We obtain the DIB measurements of spectra of late-type stars from LAMOST, and screen out 176,831, 13,473 and 110,152 high-quality (HQ) measurements of the DIBs $\lambda$5780, $\lambda$5797 and $\lambda$6614, respectively, corresponding to 142,074, 11,480 and 85,301 unique sources. Utilizing these HQ measurements, we present the Galactic maps of the DIBs $\lambda$5780 and $\lambda$6614 in the northern sky for the first time. The central wavelengths of the DIBs $\lambda$5780, $\lambda$5797 and $\lambda$6614 in air are determined to be 5780.48 $\pm$ 0.01, 5796.94 $\pm$ 0.02 and 6613.64 $\pm$ 0.01 {\AA}, respectively, based on their kinematics. The equivalent widths of these three DIBs per unit extinction are statistically fitted to be 0.565, 0.176 and 0.256 {\AA}/mag. As a part of our work, three catalogs of the HQ measurements for the DIBs $\lambda$5780, $\lambda$5797 and $\lambda$6614 are provided online. To the best of our knowledge, this is the largest number of measurements of these three DIBs to date. It is also the first time that the Galactic maps of the DIBs $\lambda$5780 and $\lambda$6614 in the northern hemisphere are presented, and the central wavelengths of the DIBs $\lambda$5780, $\lambda$5797 and $\lambda$6614 are estimated from the kinematics.

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arXiv2024

FCN4Flare: Fully Convolution Neural Networks for Flare Detection

Stellar flares offer invaluable insights into stellar magnetic activity and exoplanetary environments. Automated flare detection enables exploiting vast photometric datasets from missions like Kepler. This paper presents FCN4Flare, a deep learning approach using fully convolutional networks (FCN) for precise point-to-point flare prediction regardless of light curve length. Key innovations include the NaN Mask to handle missing data automatedly, and the Mask Dice loss to mitigate severe class imbalance. Experimental results show that FCN4Flare significantly outperforms previous methods, achieving a Dice coefficient of 0.64 compared to the state-of-the-art of 0.12. Applying FCN4Flare to Kepler-LAMOST data, we compile a catalog of 30,285 high-confidence flares across 1426 stars. Flare energies are estimated and stellar/exoplanet properties analyzed, identifying pronounced activity for an M-dwarf hosting a habitable zone planet. This work overcomes limitations of prior flare detection methods via deep learning, enabling new scientific discoveries through analysis of photometric time-series data. Code is available at https://github.com/NAOC-LAMOST/fcn4flare .

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