Sazuna/UAT_keywords huggingface dataset:
Classification of Wolf─Rayet Stars from LAMOST DR10. Wolf─Rayet (WR) stars are highly evolved descendants of the most massive initial stellar populations, serving as essential tracers in stellar evolution. Despite the progress made in their study, a more comprehensive population census is still required to advance our understanding of these celestial objects. The LAMOST DR10 provides 11,817,431 low-resolution spectra, including numerous spectra of rare and peculiar celestial objects, making it a proven resource for stellar classification. However, the spectral data for WR stars are extremely limited, which often poses challenges to the application of deep learning techniques. This paper proposes a data-augmented hybrid WDCNN+LSTM model that effectively balances spectra and achieves accurate classification. The WR spectra obtained from observation data sets were first augmented using an autoencoder generative model to construct new training and test data sets. These data sets were subsequently used to train a five-class WDCNN+LSTM model, designed to classify nitrogen WR (WN)-type, carbon WR- and oxygen WR-type, high-mass, small-to-medium-mass, and other emission-line stars based on their spectral features. Compared with four other models, the WDCNN+LSTM model demonstrated superior performance in both accuracy and recall. Utilizing this model, we conducted a search for candidate WR stars in LAMOST DR10. Through emission-line identification and crossmatching with existing catalogs, we ultimately identified three recorded WR stars and a newly discovered Galactic WN-type WR star.

Keywords: Wolf-Rayet stars, Stellar classification, Convolutional neural networks, Astronomy data analysis
Detecting Galactic Rings in the DESI Legacy Imaging Surveys with Semisupervised Deep Learning. The ring structures of disk galaxies are vital for understanding galaxy evolution and dynamics. However, due to the scarcity of ringed galaxies and challenges in their identification, traditional methods often struggle to efficiently obtain statistically significant samples. To address this, this study employs a novel semisupervised deep learning model, GC-SWGAN, aimed at identifying galaxy rings from high-resolution images of the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys. We selected over 5000 confirmed ringed galaxies from the Catalog of Southern Ringed Galaxies and the Northern Ringed Galaxies from the GZ2 catalog (GZ2-CNRG), both verified by morphology expert R. J. Buta, to create an annotated training set. Additionally, we incorporated strictly selected nonringed galaxy samples from the Galaxy Zoo 2 data set and utilized unlabeled data from the DESI Legacy Surveys to train our model. Through semisupervised learning, the model significantly reduced reliance on extensive annotated data while enhancing robustness and generalization. On the test set, it demonstrated exceptional performance in identifying ringed galaxies. With a probability threshold of 0.5, the classification accuracy reached 97%, with precision and recall for ringed galaxies at 94% and 93%, respectively. Building on these results, we predicted 750,000 galaxy images from the DESI Legacy Imaging Surveys with r-band apparent magnitudes less than 17.0 and redshifts in the range 0.0005 &lt; z &lt; 0.25, compiling the largest catalog of ringed galaxies to date, containing 62,962 galaxies with ring structures. This catalog provides essential data for subsequent research on the formation mechanisms and evolutionary history of galaxy rings.

Keywords: Galaxies, Computational astronomy, Convolutional neural networks, Astronomy data analysis, Ground-based astronomy, Astronomy image processing
BASSET: Bandpass-adaptive Single-pulse Search Toolkit—Optimized Subband Pulse Search Strategies for Faint Narrowband Fast Radio Bursts. The existing single-pulse search algorithms for fast radio bursts (FRBs) do not adequately consider the frequency bandpass pattern of the pulse, rendering them incomplete for the relatively narrow-spectrum detection of pulses. We present a new search algorithm for narrowband pulses to update the existing standard pipeline, Bandpass-Adaptive Single-pulse SEarch Toolkit (BASSET). The BASSET algorithm employs a time–frequency correlation analysis to identify and remove the noise involved by the nondetection frequency band, thereby enhancing the signal-to-noise ratio (SNR) of the pulses. The BASSET algorithm was applied to the FAST real data set of FRB 20190520B, resulting in the discovery of additional 79 pulses through reprocessing. The new detection doubles the number of pulses compared to the previously known 75 pulses, bringing the total number of pulses to 154. In conjunction with the pulse calibration and the Markov Chain Monte Carlo simulated injection experiments, this work updates the quantified parameter space of the detection rate. Moreover, a parallel-accelerated version of the BASSET code was provided and evaluated through simulation. BASSET has the capacity of enhancing the detection sensitivity and the SNR of the narrowband pulses from the existing pipeline, offering high performance and flexible applicability. BASSET not only enhances the completeness of the low-energy narrowband pulse detection in a more robust mode, but also has the potential to further elucidate the FRB luminosity function at a wider energy scale.

Keywords: Astronomy software, Astronomy data reduction, Radio transient sources
Spectroscopic Ages for 4 Million Main-sequence Dwarf Stars from LAMOST DR10 Estimated with a Data-driven Approach. Stellar age determination for large samples of stars opens new avenues for a broad range of astronomical sciences. While precise stellar ages for evolved stars have been derived from large ground- and space-based stellar surveys, reliable age determination for cool main-sequence dwarf stars remains a challenge. In this work, we set out to estimate the age of dwarf stars from the LAMOST spectra with a data-driven approach. We build a training set by using wide binaries for which the primary component has a reliable isochrone age estimate and thus gives the age of the secondary. This training set is further supplemented with field stars and cluster stars whose ages are known. We then train a data-driven model to infer age from their spectra with the XGBoost algorithm. Given a spectral signal-to-noise ratio greater than 50, the age estimation is precise to 10%–25% for K-type stars, as younger stars have larger relative errors. Validations suggest that the underlying information used for our age estimation is largely attributed to the LAMOST spectral features of chemical abundances. This means our result is a manifestation of stellar chemical clock effectively acting on LAMOST spectra (R ≃ 1800). Applying our model to the LAMOST DR10 yields a massive age catalog for ∼4 million dwarf stars. Statistical properties, such as the age distribution, age–abundance, and age–stellar activity relations of the sample stars are discussed. The catalog is publicly accessible and can be helpful for extensive sciences from detection and characterization of Earthlike planets to Galactic archaeology.

Keywords: Stellar ages, Spectroscopy
The Period–Luminosity Relation of Long-period Variables in the Large Magellanic Cloud Observed with ATLAS. Period–luminosity (PL) relations of long-period variables (LPVs) are a powerful tool to map the distances of stars in our Galaxy, and are typically calibrated using stars in the Large Magellanic Cloud (LMC). Recent results demonstrated that these relations show a strong dependence on the amplitude of the variability, which can be used to greatly improve distance estimates. However, one of the only highly sampled catalogs of such variables in the LMC is based on Optical Gravitational Lensing Experiment photometry, which does not provide all-sky coverage. Here, we provide the first measurement of the PL relation of LPVs in the LMC using photometry from the Asteroid Terrestrial-impact Last Alert System (ATLAS). We derive conversions between ugriz, Gaia, and ATLAS c and o passbands with a precision of ∼0.02 mag, which enable the measurement of reliable amplitudes with ATLAS for crowded fields. We successfully reproduce the known PL sequences A through E, and show evidence for sequence F using the ratios of amplitudes observed in both ATLAS passbands. Our work demonstrates that the ATLAS survey can recover variability in evolved red giants and lays the foundation for an all-sky distance map of the Milky Way using LPVs.

Keywords: Asteroseismology, Photometry, Large Magellanic Cloud
An All-sky 3D Dust Map Based on Gaia and LAMOST. We present a comprehensive 3D dust-reddening map covering the entire Milky Way, constructed by combining reddening estimates based on Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST) low-resolution spectra (E(B ‑ V)LAMOST) with those derived from Gaia XP spectra (E(B ‑ V)XP), along with revised Gaia distances. E(B ‑ V)LAMOST values of ∼4.6 million unique sources were obtained with the standard-pair analysis using LAMOST DR11 stellar parameters and synthesized B-/V-band photometry from Gaia XP spectra, showing a typical precision of ∼0.01 mag. The E(B ‑ V)XP from the catalog of X. Zhang et al., which was derived using forward modeling of Gaia XP spectra, were cross-validated with E(B ‑ V)LAMOST, leading to the selection of ∼150 million high-reliability measurements. The combined data set achieves a median precision of ∼0.03 mag for E(B ‑ V). To model the reddening–distance relationship along various lines of sight, we implemented a parametric approach that accounts for contributions from the Local Bubble, diffuse interstellar medium, and multiple potential molecular clouds. The sky was adaptively partitioned based on stellar density, resulting in angular resolutions ranging from 3 .' 4 to 58', with about half of the sky having a resolution better than 6 .' 9. The reddening precision of our 3D map for individual stars reaches ∼0.01 mag in most regions at ∣b∣ &gt; 20°, but degrades to 0.01–0.05 mag at ∣b∣ &lt; 20°. The map reaches a maximum distance of 3–5 kpc in high-extinction regions with ∣b∣ &lt; 5°, and extends to 10–15 kpc elsewhere. An interactive platform and Python package have been developed for utilization of the 3D dust map.

Keywords: Interstellar dust, Interstellar dust extinction, Interstellar reddening, Interstellar medium, Interstellar extinction, Milky Way Galaxy
A Comprehensive All-sky Catalog of 3345 Molecular Clouds from Three-dimensional Dust Extinction. Understanding the distribution and properties of molecular clouds is crucial for tracing the structure and evolution of the interstellar medium (ISM) and the large-scale morphology of the Milky Way. Here we present an all-sky catalog of 3345 molecular clouds identified from our previous three-dimensional dust-reddening map using a dendrogram-based clustering method with distance-adaptive parameters. The catalog spans heliocentric distances from 90 pc to 4.3 kpc and includes key physical properties for each cloud, including position, size, mass, surface density, and dust density. Approximately 650 clouds in our catalog are associated with the boundary of the Local Bubble, while around 740 clouds (excluding those associated with the Local Bubble) are located at high Galactic latitudes (∣b∣ &gt; 20∘). The spatial distribution of the cataloged clouds reveals prominent large-scale features in the Galactic disk, including coherent spur-like structures, large-scale cavities, and a more detailed view of the Local Bubble shell. These findings refine our understanding of how molecular clouds trace the Galactic spiral arm network and provide new insight into the spatial structure of the Local Bubble. The catalog serves as a valuable resource for future studies of star formation, Galactic structure, and the interaction between molecular clouds and large-scale ISM features.

Keywords: Interstellar dust, Molecular clouds, Interstellar medium, Milky Way Galaxy, Catalogs
Asteroid Types, Albedos, and Diameters Catalog from Gaia DR3: Intelligent Inversion Results via Multisource Information Fusion. The Gaia mission's third data release (Gaia DR3) offers extensive observations of small solar system objects, presenting a key opportunity to expand the asteroid property database. Taxonomy, diameter, and albedo are fundamental physical parameters for characterizing asteroids. In the absence of thermal infrared observations, diameter estimates primarily rely on the Bowell relationship, with reliability dominated by geometric albedo and absolute magnitude. To improve inversion accuracy using optical data and to obtain parameters for a larger number of asteroids, we propose an asteroid albedo-diameter joint inversion method (AadRF) that integrates low-resolution spectra, orbital dynamics, and physical parameters through artificial intelligence (AI). Cross validation and independent testing show that AadRF reduces the root mean square error for albedo and diameter predictions by 64.0 percentage points and 70.2 percentage points, respectively, compared to the traditional method, with corresponding mean absolute percentage errors of 28.9% and 17.5%. The model's advantage lies in its fusion of multisource information, embedded domain knowledge, and a hybrid deep learning–random forest architecture, which together enhance generalization. Applied to Gaia DR3, the proposed methods yield a catalog of taxonomic types, albedos, and diameters for 58,168 asteroids. Compared to existing databases, it expands the number of spectrally classified samples by nearly tenfold and adds approximately 18,100 new entries for both albedo and diameter. Further statistics reveal distinct patterns in size distribution, albedo clustering, and taxonomic composition across orbital populations. This study demonstrates the potential of AI-driven, multisource fusion for estimating asteroid parameters from large data sets and broader applications in small body property inversion.

Keywords: Astronomy databases, Gaia, Asteroids
Digitization of Astronomical Photographic Plates from China: Photometric Calibration of Single-exposure Plates and Release of Photometric Catalogs. The Chinese Plate-Digitizing Project has digitized a total number of about 30,000 astronomical plates from 11 telescopes of five observatories (Shanghai Astronomical Observatory (SHAO), Astronomical Observatory of the Chinese Academy of Sciences (NAOC), Purple Mountain Observatory (PMO), Yunnan Astronomical Observatory (YNAO), and Qingdao Observatory (QDO)) in China, spanning nearly 100 yr of observations. In this work, we present a photometric calibration method to calibrate about 15,000 single-exposure plates to the JKC photometric system. Using standard stars constructed from the BEST database, we have identified and corrected various systematic effects (including the magnitude term, color term, and flat-field term) to a high precision. The final photometric precision is typically 0.15, 0.23, 0.17, 0.11, 0.19 mag for plates collected in SHAO, NAOC, PMO, YNAO, and QDO, respectively, with best cases reaching 0.07, 0.08, 0.06, 0.05, and 0.11 mag, respectively. Candidates of variable sources are also identified. The catalogs are publicly available at the China National Astronomical Data Center. Such a data set will provide a valuable resource for conducting long-term, temporal-scale astronomical research. Our calibration method can also be applied to other digitized astronomical plates in general.

Keywords: Stellar photometry, Calibration, Astronomy data analysis, Variable stars
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 (TD) 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 5 yr, including the effective temperature, the surface gravity, the metallicity, the α-element abundance, the radial velocity (RV), and  vsini  of the target stars, is released, together with the weighted averages and uncertainties. At a signal-to-noise ratio = 10, the measurement uncertainties are 120 K, 0.18 dex, 0.13 dex, 0.08 dex, 1.9 km s‑1, and 4.0 km s‑1 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 α-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 2333 stars whose RV seems to be variable. Using Kepler/K2 or TESS photometric data, we confirmed 371 periodic variable stars among the RV variable candidates and classified their variability types. LK-MRS-I provides spectroscopic data that is useful for studies of the Kepler and K2 fields. The LK-MRS project will continue collecting TD medium-resolution spectra for target stars during the third phase of LAMOST surveys, providing data to support further scientific research.

Keywords: Astronomy databases, Surveys, Fundamental parameters of stars, Spectroscopy, Catalogs
Hemispheric Asymmetry of Relative Sunspot Numbers during Solar Cycles 17–25. The hemispheric asymmetry of solar activity provides important diagnostics of solar dynamo processes. In this study, we present a phase-resolved statistical analysis of hemispheric sunspot number asymmetry over solar cycles 17–25 (1939–2024), using monthly mean data from the National Astronomical Observatory of Japan. By combining normalized asymmetry indices, cumulative deviation tracking, and year-by-year significance testing, we identify four key results: (1) the northern hemisphere consistently dominates during the ascending phases of most cycles, suggesting a preferential emergence of magnetic flux at the northern hemisphere; (2) the hemispheric asymmetry significantly diminishes during polarity reversal periods, indicating a balancing effect of global magnetic reconfiguration; (3) solar cycles 23 and 25 exhibit remarkably similar asymmetry patterns, implying solar cycle 25 is likely to be similar to solar cycle 23; and (4) the strength of hemispheric dominance correlates with the overall solar activity level, with stronger asymmetries observed during high-activity phases and weaker signals near solar minima. These findings offer new observational constraints for modeling hemispheric solar dynamics and establish a reproducible, scalable framework for future investigations of long-term solar magnetic asymmetries.

Keywords: Solar cycle, Solar magnetic fields, Sunspot number
Imaging the Solar Corona from "Within": A Cutting-edge Approach to Enhance PSP/WISPR Imagery. The unprecedented remote-sensing observations obtained by the Wide Field Imager for Solar Probe (WISPR) on board the Parker Solar Probe (PSP) from distances inside Mercury's orbit are revolutionizing our perception of the fine-scale morphology and evolution of the solar corona. For the first time, the inherent limitations of 1 au observations arising from the superposition of optically thin features along the long lines of sight are being overcome by observing from "within." The richness of information makes the interpretation of the coronal scene highly subject to the processing performed on the images to remove the background, i.e., the bright, constant emission from interplanetary dust (F-corona). Several customized techniques to reveal the faint, solar-outflowing plasma and the large-scale K-corona structures have already been developed for WISPR. They differ on how the background is estimated. None, however, excels at revealing the faint and diffuse K-corona brightness component that permeates the scene. In this paper, we introduce a new, heuristic approach to model the evolution of the F-corona background as a function of the observer's location. The methodology exploits WISPR data sets from PSP encounters that share the same orbital parameters. The resulting background-removed data products unveil the morphology and dynamics of the large-scale K-corona with superb clarity and demonstrate that the scientific interpretation of the coronal scene in WISPR images relies on the complementary vision provided by the existing methodologies.

Keywords: Solar coronal transients, Solar K corona, Solar F corona, Solar coronal streamers, Astronomy image processing
A Deep Learning–Based Multimodal F10.7 Prediction with Mamba. F10.7, the solar radiation flux at a wavelength of 10.7 cm, serves as a crucial parameter in various space weather models and plays a significant role in measuring the intensity of solar activity. The study and prediction of F10.7 are of great significance for many applications. The motivation for this work stems from the close correlation between the F10.7 and various types and levels of solar activity, which can be well informed by solar images: By extracting relevant features from multimodal data sources and integrating them into the F10.7 prediction, we expect to improve prediction accuracy. To this end, we propose a multimodal F10.7 prediction model with Mamba, leveraging both the F10.7 data and several types of solar image data, such as ADAPT-GONG images, Helioseismic and Magnetic Imager magnetograms, and EUV images (AIA 131, 211, and 304 Å). We construct Mamba-based modules for the F10.7 index (MaFI) and sequential image (MaSI) representation learning. The temporal embeddings learned from these two modules are then fused by cross attention to capture relationships between the F10.7 and solar image data. Extensive experimental results demonstrate the superior performance of the proposed multimodal model compared to single-modal models in predicting F10.7 within approximately one solar activity cycle (from 2010 to 2024). We also studied the selection methodology for different types of images and found that, in general, the robustness of multimodal models increases with the number of image types used. However, as long as we select the appropriate data types, we can still achieve excellent prediction results with fewer image types.

Keywords: Solar cycle, Neural networks, Space weather, Convolutional neural networks, Solar physics