Sazuna/UAT_keywords huggingface dataset:
Hot Stars in the GALEX Ultraviolet Sky Surveys (GUVcat_AISxSDSS_HS) and the Binary Fraction of Hot Evolved Stars. We present a catalog of 71,364 pointlike UV sources with Sloan Digital Sky Survey (SDSS) photometry and GALEX far-UV (FUV)‑near-UV (NUV) ≤0.1 mag. The limit corresponds to stellar T eff ≳15,000‑20,000 K, slightly depending on gravity but nearly reddening independent for Milky Way‑type dust. Most sources are hot white dwarfs (WDs) and subdwarfs. Comparing the spectral energy distribution (SED; GALEX FUV, NUV, SDSS u,g,r,i,z) of 35,294 sources having good photometry with colors of stellar models and known objects, we identify 12,404 ±   binary hot-compact stars with a cooler, less-evolved companion (with a possible 8%–15% contamination by low-redshift QSOs), and 22,848 ±   single-star candidates. Single-star counts are an upper limit because pairs of similar stars have single-star-like SEDs, and hot WDs with main-sequence companions of certain types (depending on the WD's radius) are missed or counted as single in the available wavelength range and selection. The catalog offers unique leverage for identifying hot WDs, elusive at longer wavelengths when a cooler, larger companion dominates optical–IR fluxes: 51% of the binary-star and 20% of the single-star candidates are previously unknown objects. Gaia DR3 provides a parallax with error ≤20% for 34% of the binary-star candidates and 45% of single-star candidates, allowing T eff, E  B‑V , radius, and L bol to be derived from SED analysis. The binary-candidate sample usefully expands the overall current binary-WD census to subpopulations elusive to Gaia and to other searches. The binary fraction among this specific sample of hot compact objects, albeit with the mentioned biases, b  f  ≳ 46%, compared with that of their progenitors (&gt;80%–50% for mass range 8‑1M ⊙, according to M. Moe), implies a lower merging rate than found for massive stars by H. Sana et al.

Keywords: Ultraviolet sources, Binary stars, Stellar properties, Evolved stars, Late stellar evolution, White dwarf stars, Stellar effective temperatures, Celestial objects catalogs, Stellar colors, Ultraviolet photometry
X-Ray Emission of Nearby Low-mass and Sunlike Stars with Directly Imageable Habitable Zones. Stellar X-ray and UV radiation can significantly affect the survival, composition, and long-term evolution of the atmospheres of planets in or near their host star's habitable zone (HZ). Especially interesting are planetary systems in the solar neighborhood that may host temperate and potentially habitable surface conditions, which may be analyzed by future ground- and space-based direct-imaging surveys for signatures of habitability and life. To advance our understanding of the radiation environment in these systems, we leverage ∼3 Ms of XMM-Newton and Chandra observations in order to measure three fundamental stellar properties at X-ray energies for 57 nearby FGKM stellar systems: the shape of the stellar X-ray spectrum, the luminosity, and the timescales over which the stars vary (e.g., due to flares). These systems possess HZs that will be directly imageable to next-generation telescopes such as the Habitable Worlds Observatory and ground-based Extremely Large Telescopes. We identify 29 stellar systems with L X/L bol ratios similar to (or less than) that of the Sun; any potential planets in the HZs of these stars therefore reside in present-day X-ray radiation environments similar to (or less hostile than) modern Earth, though a broader set of these targets could host habitable planets. An additional 19 stellar systems have been observed with the Swift X-ray Telescope; in total, only ∼30% of potential direct imaging target stars has been observed with XMM-Newton, Chandra, or Swift. The data products from this work (X-ray light curves and spectra) are available via a public Zenodo repository (doi:10.5281/zenodo.11490574).

Keywords: G dwarf stars, Stellar x-ray flares, Dwarf planets, Planet hosting stars, F dwarf stars, K dwarf stars, Habitable planets
A Study of Stochastic Low-frequency Variability for Galactic O-type Stars. In order to explore how the ubiquitous stochastic low-frequency (SLF) variability of O-type stars is related to various stellar characteristics, we compiled a sample of 150 O-type stars observed via ground-based spectroscopic surveys, alongside photometric data obtained from the Transiting Exoplanet Survey Satellite (TESS). We analyzed 298 light curves obtained from TESS Sectors 1–65 for the stars in our sample. Leveraging the spectroscopic parameters, we used Bonnsai to determine masses, radii, fractional main-sequence ages, and mass-loss rates for stars of our sample. Subsequently, we identified possible correlations between the fitted parameters of SLF variability and stellar properties. Our analysis unveiled four significant correlations between the amplitude and stellar parameters, including mass, radius, fractional main-sequence ages, and mass-loss rate. For stars with ≳30 M ⊙, we observed a decrease in characteristic frequency and steepness with increasing radius. Finally, we compared various physical processes that may account for the SLF variability with our results. The observed SLF variability may arise from the combined effects of the iron convection zone (FeCZ) and internal gravity waves (IGWs), with IGWs potentially more dominant in the early stages of stellar evolution, and the contribution of FeCZ becoming more significant as stars evolve. Meanwhile, our results indicate that the SLF variability of O-type stars bears certain signatures of the line-driven wind instability and granulation.

Keywords: Early-type stars, Massive stars, Early-type variable stars
Investigating the Behavior and Spatiotemporal Variations of Green-line Emission in the Solar Corona. Understanding coronal structure and dynamics can be facilitated by analyzing green-line emission, which enables the investigation of diverse coronal structures such as coronal loops, streamers, coronal holes, and various eruptions in the solar atmosphere. In this study, we investigated the spatiotemporal behaviors of green-line emissions in both low and high latitudes across nine solar cycles, ranging from Solar Cycle 17 to the current Solar Cycle 25, using the modified homogeneous data set. We employed methodologies such as cross correlation, power spectral density, and wavelet transform techniques for this analysis. We found distinct behaviors in green-line energy across various latitudinal distributions in the solar atmosphere. The trends observed at higher latitudes differ from those at lower latitudes. The emission behaviors show a close association with other solar phenomena like solar flares, sunspots, and coronal mass ejections throughout the solar cycles. The observed variations exhibit harmonic periods. The emission activity is significantly higher in the low latitudes, accounting for over 70% of the emissions, while the higher latitudes contribute less than 30%. The emissions exhibit asymmetric behavior between the northern and southern hemispheres, leading to a 44 yr cycle of solar hemispheric dominance shifts. Various factors, such as Alfvén waves, solar magnetic fields, sunspots, differential rotation, and reconnection events, influence the observed differences in behavior between lower and higher latitudes, suggesting the existence of potential underlying phenomena contributing to deviations in properties, intensity, temporal dynamics, and spatiotemporal lifetime.

Keywords: Solar cycle, Solar coronal radio emission, Solar atmosphere, Astronomy data analysis, Solar activity, Active solar corona
AM<SUP>3</SUP>: An Open-source Tool for Time-dependent Lepto-hadronic Modeling of Astrophysical Sources. We present the Astrophysical Multimessenger Modeling (AM  3 ) software. AM  3  is a documented open-source software (source code at https://gitlab.desy.de/am3/am3; user guide and documentation at https://am3.readthedocs.io/en/latest/) that efficiently solves the coupled integro-differential equations describing the temporal evolution of the spectral densities of particles interacting in astrophysical environments, including photons, electrons, positrons, protons, neutrons, pions, muons, and neutrinos. The software has been extensively used to simulate the multiwavelength and neutrino emission from active galactic nuclei (including blazars), gamma-ray bursts, and tidal disruption events. The simulations include all relevant nonthermal processes, namely synchrotron emission, inverse Compton scattering, photon─photon annihilation, proton─proton and proton─photon pion production, and photo-pair production. The software self-consistently calculates the full cascade of primary and secondary particles, including nonlinear feedback processes and predictions in the time domain. It also allows the user to track separately the particle densities produced by means of each distinct interaction process, including the different hadronic channels. With its efficient hybrid solver combining analytical and numerical techniques, AM  3  combines efficiency and accuracy at a user-adjustable level. We describe the technical details of the numerical framework and present three examples of applications to different astrophysical environments.

Keywords: Open source software, Gamma-ray bursts, Theoretical models, High energy astrophysics, Tidal disruption, Neutrino astronomy, Computational methods, Active galactic nuclei, Radiative processes, Gamma-ray astronomy, Particle astrophysics, Cosmic rays
Scylla. I. A Pure-parallel, Multiwavelength Imaging Survey of the ULLYSES Fields in the LMC and SMC. Scylla is a deep Hubble Space Telescope (HST) survey of the stellar populations, interstellar medium, and star formation in the LMC and SMC. As a pure-parallel complement to the Ultraviolet Legacy Library of Young Stars as Essential Standards (ULLYSES) survey, Scylla obtained 342 orbits of ultraviolet (UV) through near-IR imaging of the LMC and SMC with Wide Field Camera 3. In this paper, we describe the science objectives, observing strategy, data reduction procedure, and initial results from our photometric analysis of 96 observed fields. Although our observations were constrained by ULLYSES primary exposures, we imaged all fields in at least two filters (F475W and F814W) and 64% of fields in at least three and as many as seven WFC3 filters spanning the UV to IR. Overall, we reach average 50% completeness of m F225W = 26.0, m F275W = 26.2, m F336W = 26.9, m F475W = 27.8, m F814W = 25.5, m F110W = 24.7, and m F160W = 24.0 Vega mag in our photometric catalogs, which is faintward of the ancient main-sequence turnoff in all filters. The primary science goals of Scylla include characterizing the structure and properties of dust in the MCs, as well as their spatially resolved star formation and chemical enrichment histories. Our images and photometric catalogs, which represent the widest-area coverage of MCs with HST photometry to date, are available as a high-level science product at the Barbara A. Mikulski Archive for Space Telescopes.

Keywords: Surveys, Hubble Space Telescope, Star formation, Interstellar medium, Magellanic Clouds, Catalogs
Impacts of Data Preprocessing and Sampling Techniques on Solar Flare Prediction from Multivariate Time Series Data of Photospheric Magnetic Field Parameters. The accurate prediction of solar flares is crucial due to their risks to astronauts, space equipment, and satellite communication systems. Our research enhances solar flare prediction by employing sophisticated data preprocessing and sampling techniques for the Space Weather Analytics for Solar Flares (SWAN-SF) data set, a rich source of multivariate time series data of solar active regions. Our study adopts a multifaceted approach encompassing four key methodologies. Initially, we address over 10 million missing values in the SWAN-SF data set through our innovative imputation technique called fast Pearson correlation-based k-nearest neighbors imputation. Subsequently, we propose a precise normalization technique, called LSBZM normalization, tailored for time series data, merging various strategies (log, square root, Box–Cox, Z-score, and min–max) to uniformly scale the data set's 24 attributes (photospheric magnetic field parameters), addressing issues such as skewness. We also explore the "near decision boundary sample removal" technique to enhance the classification performance of the data set by effectively resolving the challenge of class overlap. Finally, a pivotal aspect of our research is a thorough evaluation of diverse oversampling and undersampling methods, including SMOTE, ADASYN, Gaussian noise injection, TimeGAN, Tomek links, and random undersampling, to counter the severe imbalance in the SWAN-SF data set, notably a 60:1 ratio of major (X and M) to minor (C, B, and FQ) flaring events in binary classification. To demonstrate the effectiveness of our methods, we use eight classification algorithms, including advanced deep-learning-based architectures. Our analysis shows significant true skill statistic scores, underscoring the importance of data preprocessing and sampling in time-series-based solar flare prediction.

Keywords: Solar active region magnetic fields, Solar flares, Classification, Neural networks, Space weather, Solar active regions, Time series analysis
Coevolution of Giant Molecular Clouds, Filaments, and Clumps as a Function of the Dense Gas Mass Fraction. We investigate the evolutionary dynamics with archival continuum and line data of 27 giant molecular clouds (GMCs) in the Milky Way, focusing on their influence on star formation. Examining the dense gas mass fraction (DGMF) among the GMCs, we categorize them into low-DGMF (DGMF &lt; 20%), medium-DGMF (20% &lt; DGMF &lt; 60%), and high-DGMF (60% &lt; DGMF) groups. The analysis uncovers systematic trends in the free-fall time, virial parameter, surface density, star formation rate (SFR), SFR per unit area (ΣSFR), and star formation efficiency for dense gas as the DGMF increases within GMCs. We identified 362 filaments and 3623 clumps within the GMCs. Increasing DGMF correlates with higher proportions of star-forming clumps and clumps capable of forming massive stars. Clump properties such as hydrogen number density and surface density increase with DGMF, while the mass and radius decrease. The dust temperature and virial parameters show no significant variation with DGMF. We also observe convergence in the hydrogen number density and dust temperature between star-forming and starless clumps with rising DGMF. Filaments are found to be spatially associated with clumps capable of forming high-mass stars, with those on filaments exhibiting greater mass, radius, hydrogen number density, surface density, and velocity dispersion. Moreover, filaments hosting clumps capable of forming high-mass stars demonstrate larger mass, length, and line mass. In summary, this comprehensive analysis of GMCs, filaments, and clumps supports the notion of a multiscale coevolution process. From GMCs to filaments and subsequently to clumps, the DGMF emerges as a valuable tracer for understanding the evolutionary trajectory of GMCs and the processes governing their development.

Keywords: Molecular clouds, Astrostatistics, Dense interstellar clouds, Star formation, Interstellar medium, Dynamical evolution
Estimating Stellar Atmospheric Parameters and [α/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 ([α/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 [α/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 via doi:10.12149/101402.

Keywords: Astronomy data analysis
The Thermal Emission in Short Gamma-Ray Bursts with Extended Emission Observed by Fermi/GBM. Short gamma-ray bursts (SGRBs) with extended emission (EE) are composed of initial main emission (ME) with a short hard spike, followed by a long-lasting EE. Whether the ME and EE originated from the same origin or not, as well as the jet composition, remains an open question. In this paper, we present a systematic analysis of 36 gamma-ray bursts (GRBs) in our sample, which are identified as the category of SGRBs with EE as observed by Fermi/Gamma-ray Burst Monitor. By extracting time-integrated spectra of ME and EE with cutoff power-law or Band models for our sample, we find that 20 out of 36 SGRBs have α values that exceed the death line (e.g., ‑2/3) of synchrotron emission within either ME or EE phases, and we suggest that the quasi-thermal component should exist in the prompt emission. Then, we extract the time-resolved spectra of our samples, but only four GRBs are bright enough to extract the time-resolved spectra. We find that both thermal and nonthermal emissions do exist in the prompt emission of those four bright GRBs, which suggests that a hybrid jet (e.g., matter and Poynting-flux outflow) in GRBs should exist. Moreover, strong positive correlations (e.g., F tot–Γ and F tot–kT) in the time-resolved spectra of ME and EE for those four GRBs have been discovered. This indicates that the spectral evolution of both ME and EE seem to share similar behavior, possibly from the same physical origin.

Keywords: Gamma-ray bursts
LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars. With the increasing amount of astronomical observation data, it is an inevitable trend to use artificial intelligence methods for automatic analysis and identification of light curves for full samples. However, data sets covering all known classes of variable stars that meet all research needs are not yet available. There is still a lack of standard training data sets specifically designed for any type of light-curve classification, but existing light-curve training sets or data sets cannot be directly merged into a large collection. Based on the open data sets of the All-Sky Automated Survey for SuperNovae, Gaia, and Zwicky Transient Facility, we construct a compatible light-curve data set named LEAVES for automated recognition of variable stars, which can be used for training and testing new classification algorithms. The data set contains a total of 977,953 variable and 134,592 nonvariable light curves, in which the supported variables are divided into six superclasses and nine subclasses. We validate the compatibility of the data set through experiments and employ it to train a hierarchical random forest classifier, which achieves a weighted average F1-score of 0.95 for seven-class classification and 0.93 for 10-class classification. Experimental results prove that the classifier is more compatible than the classifier established based on a single band and a single survey, and has wider applicability while ensuring classification accuracy, which means it can be directly applied to different data types with only a relatively small loss in performance compared to a dedicated model.

Keywords: Astronomy databases, Light curve classification , Variable stars
Chasing the Neutrino Blazar Candidates. In our study of the correlations between IceCube-detected neutrino events and γ-ray properties of blazars, we recognize the inherent challenges posed by the limited detection of neutrinos. In this paper, we explore few-shot learning to deal with the class imbalance and few-shot issues presented in the incremental version of the 12 yr Fermi-LAT γ-ray source catalog (4FGL_ DR3). Specifically, we train a triplet network to transform the blazars with neutrino emission (NBs) and nonblazar samples into an embedding space where their similarities can be measured. With two-way three-shot learning, 199 out of 3708 blazars without neutrino emission (non-NBs) are considered as the potential blazars emitting neutrinos (NB candidates, or NBCs for short), with a similarity score against NBs exceeding 98%. Moreover, the Kolmogorov–Smirnov test supports our identification of NBCs.

Keywords: Blazars
An Efficient Method for Batch Derivation of Detached Eclipsing Binary Parameters: Analysis of 34,907 OGLE Systems. Detached eclipsing binary (EB) systems are crucial for measuring the physical properties of stars that evolve independently. Large-scale time-domain surveys have released a substantial number of light curves for detached EBs. Utilizing the Physics of Eclipsing Binaries package in conjunction with Markov Chain Monte Carlo (MCMC) methods for batch parameter derivation poses significant computational challenges, primarily due to the high computational cost and time demands. Therefore, this paper develops an efficient method based on the neural network model and the stochastic variational inference method (denoted NNSVI) for the rapid derivation of parameters for detached EBs. For studies involving more than three systems, the NNSVI method significantly outperforms techniques that combine MCMC methods in terms of parameter inference speed, making it highly suitable for the batch derivation of large numbers of light curves. We efficiently derived parameters for 34,907 detached EBs, selected from the Optical Gravitational Lensing Experiment catalog and located in the Galactic bulge, using the NNSVI method. A catalog detailing the parameters of these systems is provided. Additionally, we compared the parameters of two double-lined detached EBs with those from previous studies and found the estimated parameters to be essentially identical.

Keywords: Eclipsing binary stars, Astronomy data analysis, Detached binary stars