Sazuna/UAT_keywords huggingface dataset:
A Multiple-detection-heads Machine Learning Algorithm for Detecting White Dwarfs. White dwarfs (WDs) are the ultimate stage for approximately 97% of stars in the Milky Way and are crucial for studying stellar evolution and galaxy structure. Due to their small size and low luminosity, WDs are not easily observable. Traditional search methods mostly rely on analyzing photometric parameters, which need high-quality data. In recent years, machine learning has played a significant role in astronomical data mining, due to its speed, real time, and precision. However, we have identified two common issues. On the one hand, many studies are based on high-quality spectral data, while a large amount of image data remain underutilized. On the other hand, existing astronomical algorithms are essentially classification algorithms, with sample incompleteness being a critical weakness. In our study, we propose the WD Network (WDNet) algorithm, which is a new object detection algorithm that integrates multiple advanced technologies and can directly locate WDs in images. WDNet overcomes the degradation issue of WDs and detected 31,065 candidates in 80,448 images. The candidates exhibit a wide range of types, including DA, DB, DC, DQ, and DZ, with surface gravity within 7.8 dex ∼ 8.4 dex, effective temperatures within 10,000 K ∼ 56,000 K, colors within −1 &lt; u − g &lt; 1 and −0.8 &lt; g − r &lt; 0.4, and reduced proper motion within 20∼35 mag. In the future, WDNet will conduct large-scale searches using the Chinese Space Station Telescope and Sloan Digital Sky Survey V.

Keywords: White dwarf stars, Astronomical object identification, Astronomy data analysis, Astrostatistics
A Wideband Chemical Survey of Massive Star-forming Regions at Subarcsecond Resolution with the Submillimeter Array. Massive star-forming regions exhibit a rich chemistry with complex gas distributions, especially on small scales. While surveys have yielded constraints on typical gas conditions, they often have coarse spatial resolution and limited bandwidths. Thus, to establish an interpretative framework for these efforts, detailed observations that simultaneously provide high sensitivity, spatial resolution, and large bandwidths for a subset of diverse sources are needed. Here, we present wide-band (≈32 GHz) Submillimeter Array (SMA) observations of four high-mass star-forming regions (G28.20-0.05, G20.08-0.14 N, G35.58-0.03, and W33 Main) at subarcsecond resolution, where we detect and spatially resolve hundreds of lines from over 60 molecules, including many complex organic molecules (COMs). The chemical richness of our sample is consistent with an evolutionary sequence from the line-rich hot cores and hypercompact H II regions of G28.20-0.05 and G20.08-0.14 N to the more chemically modest ultracompact H II regions in G35.58-0.03, followed by the molecule-poor H II region W33 Main. We detect lines across a range of excitation conditions (Eu ≈ 20 to ≳800 K) and from numerous isotopologues, which enables robust estimates of gas properties. We derive nearly constant COM column density ratios that agree with literature values in other low- and high-mass protostellar cores, supporting the idea that COM abundances are set during the pre-stellar phase. In all regions, we identify spatial offsets among different molecular families, due to a combination of source physical structure and chemistry. In particular, we find potential evidence of carbon grain sublimation in G28.20-0.05 and identify an elemental oxygen gradient and rich sulfur chemistry in G35.58-0.03. Overall, these results demonstrate that the SMA's wide bandwidth is a powerful tool to untangle the complex molecular gas structures associated with massive star formation.

Keywords: Interstellar molecules, Star forming regions, Young stellar objects, High angular resolution, Complex organic molecules
The Fate of the Interstellar Medium in Early-type Galaxies. V. Active Galactic Nucleus Feedback from Optical Spectral Classification. Quenching of star formation plays a fundamental role in galaxy evolution. This process occurs due to the removal of the cold interstellar medium (ISM) or stabilization against collapse, so that gas cannot be used in the formation of new stars. In this paper, we study the effect of different mechanisms of ISM removal. In particular, we revised the well-known Baldwin─Philips─Terlevich (BPT) and EWHα versus [N II]/Hα (WHAN) emission-line ratio diagnostics, so that we could classify all galaxies, even those not detected at some emission lines, introducing several new spectral classes. We use spectroscopic data and several physical parameters of 2409 dusty early-type galaxies in order to find out the dominant ionization source (active galactic nuclei, AGNs, young massive stars, hot low-mass evolved stars, HOLMES) and its effect on the ISM. We find that strong AGNs can play a significant role in the ISM removal process only for galaxies with ages lower than 109.4 yr, but we cannot rule out the influence of weak AGNs at any age. For older galaxies, HOLMES/planetary nebulae contribute significantly to the ISM removal process. Additionally, we provide the BPT and WHAN classifications not only for the selected sample but also for all 300,000 galaxies in the Galaxy And Mass Assembly survey fields.

Keywords: Dust destruction, Galaxy ages, Galaxy evolution, Interstellar medium, Galaxy quenching, Galaxy spectroscopy, Elliptical galaxies, Early-type galaxies
Cloud Removal in Full-disk Solar Images Using Deep Learning. Ground-based solar telescopes often encounter cloud interference during observations, leading to varying degrees of cloud cover in solar images. Existing cloud removal methods face several challenges, including incomplete cloud removal, insufficient restoration of solar features, and ineffectiveness against severe cloud cover. This paper proposes a novel deep learning-based approach for cloud removal and feature restoration in Hα full-disk solar images. First, to create a high-quality data set of cloud-covered images, we developed a method for detecting and classifying cloudiness in Hα full-disk solar images. This method correlates average intensity profiles in log-polar coordinates and utilizes the median pixel intensity in the images. Using this approach, we constructed a data set with three levels of cloudiness: mild, moderate, and severe, utilizing high-cadence Hα full-disk solar images from different sites within the Global Oscillation Network Group. Second, our cloud removal network employs an encoder–decoder structure, integrating the Res2Net module into the encoder to capture comprehensive solar image features. The decoder enhances the model's ability to extract semantic information about cloud-covered areas through a residual multiscale attention mechanism. Quantitative and qualitative experimental results demonstrate that the proposed method effectively removes complex cloud interference while preserving solar features, significantly improving the quality of observed data and outperforming existing solar image cloud removal methods. Additionally, the model serves as a valuable reference for removing clouds from other ground-based telescope data. The data and code are available on GitHub (https://github.com/dupeng24/full-disk-cloud-removal) under a CC0 1.0 License, and version 2.0 is archived in Zenodo (10.5281/zenodo.14184221).

Keywords: Astronomical techniques, The Sun, Astronomy data analysis
Classification of Periodic Variable Stars from TESS. The number of known periodic variable stars has increased rapidly in recent years. As an all-sky transit survey, the Transiting Exoplanet Survey Satellite (TESS) plays an important role in detecting low-amplitude variable stars. Using 2 minute cadence data from the first 67 sectors of TESS, we find 72,505 periodic variable stars. We used 19 parameters including period, physical parameters, and light-curve (LC) parameters to classify periodic variable stars into 12 subtypes using the random forest method. Pulsating variable stars and eclipsing binaries are distinguished mainly by period, LC parameters, and physical parameters. Classical Cepheids, Type-II Cepheids, rotational variable stars, eruptive variable stars of the UV Ceti type, and young stellar objects are distinguished mainly by period and physical parameters. Compared to previously published catalogs, 63,106 periodic variable stars (87.0%) are newly classified, including 13 Cepheids, 27 RR Lyrae stars, ~4600 δ Scuti variable stars, ~1600 eclipsing binaries, ~34,000 rotational variable stars, and about 23,000 other types of variable star. The purity of eclipsing binaries and pulsation variable stars ranges from 94.2% to 99.4% when compared to the variable star catalogs of Gaia Data Release 3 and Zwicky Transient Facility Data Release 2. The purity of rotational variable stars is relatively low at 83.3%. The increasing number of variables stars is helpful to investigate the structure of the Milky Way, stellar physics, and chromospheric activity.

Keywords: Periodic variable stars, Delta Scuti variable stars, Pulsating variable stars, Light curves, Cepheid variable stars, RR Lyrae variable stars, Eclipsing binary stars, Catalogs
Introducing the Condor Array Telescope. V. Deep Broad- and Narrowband Imaging Observations of the M81 Group. We used the Condor Array Telescope to obtain deep imaging observations through the luminance broadband and He II 468.6 nm, [O III] 500.7 nm, He I 587.5 nm, Hα, [N II] 658.4 nm, and [S II] 671.6 nm narrowband filters of an extended region comprising 13 "Condor fields" spanning ≈ 8 × 8 deg2 on the sky centered near M81 and M82. Here we describe the acquisition and processing of these observations, which together constitute unique very deep imaging observations of a large portion of the M81 Group through a complement of broad- and narrowband filters. The images are characterized by an intricate web of faint, diffuse, continuum produced by starlight scattered from Galactic cirrus, and all prominent cirrus features identified in the broadband image can also be identified in the narrowband images. We subtracted the luminance image from the narrowband images to leave, more or less, only line emission in the difference images, and we masked regions of the resulting images around stars at an isophotal limit. The difference images exhibit extensive extended structures of ionized gas in the direction of the M81 Group, from known galaxies of the M81 Group, clouds of gas, filamentary structures, and apparent or possible bubbles or shells. Specifically, the difference images show a remarkable filament known as the "Ursa Major Arc;" a remarkable network of criss-crossed filaments between M81 and NGC 2976, some of which intersect and overlap the Ursa Major Arc; and details of a "giant shell of ionized gas."

Keywords: Galaxy interactions, Galaxy tails, Galaxy photometry, Galaxy mergers, Galaxy groups, Interacting galaxies, Galaxies, Low surface brightness galaxies
The DiskMass Survey. XI. Disk Geometries and Star Formation Surface Densities from Ionized Gas Kinematics and Line Intensities for the Full Hα Sample. We present Hα-region integral-field spectroscopy for 137 low-inclination, intermediate to late-type galaxies. Spectroscopic data, obtained with SparsePak and the Bench Spectrograph on the WIYN 3.5 m telescope, span 6475─6880 Å with an instrumental resolution of 13 km s−1 (σ). The spectral range includes Hα and [N II]λλ6548, 6584 for every source, and in most cases includes [S II]λλ6717, 6731. We present and publicly release 18,288 calibrated spectra and visually inspected Gaussian line fits to the Hα emission. Most measurements yield a signal-to-noise ratio above 5 in integrated Hα line flux, adequate to derive reliable line centroids and widths. Second kinematic components are required to adequately describe the emission-line profile in 15% of reliable data. The Hα velocity dispersion distribution peaks at 18 km s−1, modestly increasing with Hα surface brightness, reaching 20 km s−1 at ΣHα = 1040 erg s−1 kpc−2. Lower-flux secondary components, when present, have widths of ~50 km s−1. These results agree well with previous echelle measurements of nearby galaxies. Velocity-field analysis yields kinematic inclinations, with a sample mean of 26°. Large kinematic asymmetries systematically affect kinematic inclination estimates in a small fraction of our sample. When deviations from circular motion are below 10% of the projected velocity, kinematic inclinations are consistent, within errors, to estimates from inverting the Tully─Fisher relation. This confirms previous disk-submaximality estimates for galaxies with regular kinematics based on inclinations derived from inverting the Tully─Fisher relation.

Keywords: Galaxy structure, Galaxy dynamics, Galaxy kinematics, Unbarred spiral galaxies
A New X-Ray and Radio Burst Activity from the Magnetar SGR 1935+2154. SGR 1935+2154 has emerged as the most active magnetar in recent years, exhibiting X-ray burst activity nearly annually and emitting a fast radio burst (FRB 200428 (FRB1)) in its 2020 April activity, for the first time from a Milky Way source, accompanied by an X-ray burst (XFRB1). The source was active again in 2021 September and 2022 January, but without emitting radio bursts. In 2022 October, the magnetar entered a new activity that included radio bursts, three of them reported to be associated with X-ray bursts. We present a comprehensive study of the activity, as observed by the Fermi Gamma-Ray Burst Monitor, and offer a comparison with the previous activities. The mean burst duration of the observed 113 X-ray bursts (∼122 ms) is ∼30% shorter than in 2020 April but longer than most other activities. The mean spectral parameters (ΓCPL ∼ 0.82, Ep CPL ∼ 26 keV and kTBBs ∼ 5.10 keV, kTBBh ∼ 8.96 keV) are generally comparable to the previous activities but with a softer ΓCPL and larger RBBh than in 2020 April. The  RBB2─kTBB  parameter space shows a marked contrast where two distinctive, nonoverlapping, and smaller soft and hard blackbody regions are manifested in 2020 April but not in 2022 October and the other episodes. The fluence─duration power-law correlation is ∼60% steeper in 2022 October and the other episodes than in 2020 April. The three new X-ray bursts associated with radio bursts stand out in being ∼2─10-fold longer than the mean duration, with X-ray-to-radio duration, flux, and fluence ratios of ∼102─3, 103─4, and 105─7, respectively, comparable to the XFRB1/FRB1 ratio for duration but larger for flux and fluence due to fainter radio bursts in 2022. We discuss the results in the context of the new emission models that propose physical mechanisms for the X-ray and radio burst emission from magnetars.

Keywords: Neutron stars, Gamma-ray bursters, Magnetars, X-ray bursts, Radio transient sources
A Coscaling Grid for Athena++. II. Magnetohydrodynamics. We extend the coscaling formalism of Habegger &amp; Heitsch implemented in Athena++ to magnetohydrodynamics. The formalism relies on flow symmetries in astrophysical problems involving expansion, contraction, and center-of-mass motion. The formalism is fully consistent with the upwind constrained transport method implemented in Athena++ and is accurate to second order in space. Applying our implementation to standard magnetohydrodynamic test cases leads to improved results and higher efficiency, compared to the fixed-grid solutions.

Keywords: Computational methods, Astronomical simulations, Magnetohydrodynamical simulations
Classification and Physical Characteristic Analysis of Fermi-GBM Gamma-Ray Bursts Based on Deep Learning. The classification of gamma-ray bursts (GRBs) has long been an unresolved problem. Early long- and short-burst classification based on duration is not convincing owing to the significant overlap in duration plot, which leads to different views on the classification results. We propose a new classification method based on convolutional neural networks and adopt a sample including 3774 GRBs observed by Fermi-GBM to address the T90 overlap problem. By using count maps that incorporate both temporal and spectral features as inputs, we successfully classify 593 overlapping events into two distinct categories, thereby refuting the existence of an intermediate GRB class. Additionally, we apply the optimal model to extract features from the count maps and visualize the extracted GRB features using the t-SNE algorithm, discovering two distinct clusters corresponding to S-type and L-type GRBs. To further investigate the physical properties of these two types of bursts, we conduct a time-integrated spectral analysis and discover significant differences in their spectral characteristics. The analysis also shows that most GRBs associated with kilonovae belong to the S type, while those associated with supernovae are predominantly L type, with few exceptions. Additionally, the duration characteristics of short bursts with extended emission suggest that they may manifest as either L-type or S-type GRBs. Compared to traditional classification methods (Amati and energy–hardness–duration methods), the new approach demonstrates significant advantages in classification accuracy and robustness without relying on redshift observations. The deep learning classification strategy proposed in this paper provides a more reliable tool for future GRB research.

Keywords: Convolutional neural networks, Gamma-ray bursts, Astronomy data analysis
Calibrating the Color–Magnitude Relation of M Giants by Using Open Clusters. M giants, with their distinctive properties such as high luminosity, serve as excellent indicators for mapping the structure of the Milky Way. The distance to distant M giants can be determined by using the color–magnitude relation (CMR), which is derived from color–magnitude diagrams of specific systems in previous studies. In this work, we aimed to achieve more accurate distance determination for M giants by focusing on open clusters (OCs) with a large number of member stars and thus improve the CMR. For the first time, we compiled a census of OCs harboring M giants using Gaia Data Release 3 (DR3) and Large Sky Area Multi-Object Fiber Spectroscopic Telescope Data Release 9. We identified 58 M giants associated with 43 OCs and obtained their astrometric and photometric parameters from Gaia DR3. Using the distances of these OCs, we derived the CMR for M giants as a linear correlation, expressed as  . This linear relation proved superior to the empirical distance relation in characterizing the CMR of M giants. The photometric distances of M giants derived from the CMR are consistent with the parallax distances from Gaia and known spectroscopic distances, with median deviations of 1.5% and 2.3%, respectively. Using the distances of M giants derived from the CMR, we computed their radial velocity (VR), azimuthal velocity (Vϕ), and vertical velocity (VZ), respectively. The distributions of these velocities revealed key features of the Galactic disk, including oscillation, north–south rotational asymmetry, and warp. These findings are consistent with previous studies and further validate the reliability of the derived CMR.

Keywords: Open star clusters, M giant stars
The Galaxy Activity, Torus, and Outflow Survey (GATOS). VII. The 20─214 μm Imaging Atlas of Active Galactic Nuclei Using SOFIA. We present a 19.7─214 μm imaging atlas of local (4─181 Mpc; median 43 Mpc) active galactic nuclei (AGN) observed with FORCAST and HAWC+ on board the SOFIA telescope with angular resolutions ~3″─20″. This atlas comprises 22 Seyferts (17 Type 2 and five Type 1) with a total of 69 images, 41 of which have not been previously published. The AGN span a range of luminosities of   with a median of  . We provide the total fluxes of our sample using aperture photometry for point-source objects and a 2D Gaussian fitting for objects with extended host galaxy emission, which was used to estimate the unresolved nuclear component. Most galaxies in our sample are pointlike sources; however, four sources (Centaurus A, Circinus, NGC 1068, and NGC 4388) show extended emission in all wavelengths. The 30─40 μm extended emission in NGC 4388 is coincident with the narrow-line region at PA ~ 50°, while the dusty extension at longer wavelengths arises from the host galaxy at PA ~ 90°. Our new observations allow us to construct the best-sampled parsec-scales (spectral energy distributions, SEDs) available between 30 and 500 μm for a sample of nearby AGN. We estimate that the average peak wavelength of the nuclear SEDs is ~40 μm in νFν, which we associate with an unresolved extended dusty region heated by the AGN.

Keywords: Supermassive black holes, Active galaxies, Active galactic nuclei
Sequencing Silicates in the Spitzer Infrared Spectrograph Debris Disk Catalog. I. Methodology for Unsupervised Clustering. Debris disks, which consist of dust, planetesimals, planets, and gas, offer a unique window into the mineralogical composition of their parent bodies, especially during the critical phase of terrestrial planet formation spanning 10 yr to a few hundred million years. Observations from the Spitzer Space Telescope have unveiled thousands of debris disks, yet systematic studies remain scarce, let alone those with unsupervised clustering techniques. This study introduces CLustering UnsupErvised with Sequencer (CLUES), a novel, nonparametric, fully interpretable machine learning spectral analysis tool designed to analyze and classify the spectral data of debris disks. CLUES combines multiple unsupervised clustering methods with multiscale distance measures to discern new groupings and trends, offering insights into compositional diversity and geophysical processes within these disks. Our analysis allows us to explore a vast parameter space in debris disk mineralogy and also offers broader applications in fields such as protoplanetary disks and solar system objects. This paper details the methodology, implementation, and initial results of CLUES, setting the stage for more detailed follow-up studies focusing on debris disk mineralogy and demographics.

Keywords: Silicate grains, Meteorite composition, Infrared spectroscopy, Minimum spanning tree, Exozodiacal dust, Planetary system formation, Clustering, Spectroscopy, Planetesimals, Meteorites, Debris disks
MARVEL Analysis of the Measured High-resolution Spectra of <SUP>12</SUP>C<SUP>16</SUP>O. The Measured Active Rotational–Vibrational Energy Levels (MARVEL) algorithm is used to determine accurate empirical rotational–vibrational energy levels for the ground electronic state of the diatomic 12C16O molecule. 2293 energy levels have been obtained through a careful analysis of lines measured and assigned in high-resolution experimental spectra reported previously in 68 publications. Out of the 19,399 (7955) measured (unique) transitions in the limited wavenumber range of 0–14,470 cm‑1, an analysis of the resulting experimental spectroscopic network (SN) validates 19,219 (7795), and only 11 transitions had to be deleted from the SN assembled (note that transitions within floating components of the SN cannot be validated). The measured transitions span states with vibrational and rotational quantum numbers less than or equal to 41 and 123, respectively, with the highest validated energy level lying at 67,148.1 cm‑1. The validation procedure covers all transitions with a one-photon absorption intensity larger than 10‑30 cm molecule‑1 at 296 K. The validated line centers and the empirical rovibrational energy levels of 12C16O, with appropriate uncertainties and assignments, are provided in the appendix to this paper. Detailed comparisons are made with several existing data sets, such as the Kurucz and the HITRAN databases, NIST-certified wavelengths, and the list of lines protected by the International Astronomical Union, revealing occasional discrepancies.

Keywords: Molecular physics, Molecular spectroscopy, Laboratory astrophysics