Sazuna/UAT_keywords huggingface dataset:
Time-averaging Polarimetric and Spectral Properties of Gamma-Ray Bursts. The composition and radiation mechanism of gamma-ray bursts (GRBs) within jets continue to be hotly debated. Investigating the joint polarimetric and spectral properties is crucial for understanding the composition and radiation mechanism of GRBs. Various jet properties, such as "kinetic-energy-dominated" (KED), "Poynting-flux-dominated" (PFD), and "hybrid-dominated" (HD) relativistic outflows, have been inferred from observed GRB spectra, with expectations of differing polarization levels among them. In this study, we analyzed a sample of 27 GRBs detected by the Gamma-ray Burst Monitor on board the NASA Fermi Gamma-ray Space Telescope, focusing on 26 bursts with significant polarization measurements. Our analysis revealed that 16 bursts (62%) were predominantly associated with the "PFD" jet type, while 10 bursts (38%) were classified as HD, implying that photosphere emission may also be a possible mechanism powering the high levels of polarization. Notably, no bursts were identified as KED type. We found distinct polarization properties, with HD-type bursts exhibiting consistently higher polarization levels than PFD-type bursts. We proposed models incorporating ordered and random magnetic field configurations specific to hybrid jets.

Keywords: Gamma-ray bursts, Gamma-ray transient sources, Astronomy data analysis
Microlensing Discovery and Characterization Efficiency in the Vera C. Rubin Legacy Survey of Space and Time. The Vera C. Rubin Legacy Survey of Space and Time will discover thousands of microlensing events across the Milky Way, allowing for the study of populations of exoplanets, stars, and compact objects. We evaluate numerous survey strategies simulated in the Rubin Operation Simulations to assess the discovery and characterization efficiencies of microlensing events. We have implemented three metrics in the Rubin Metric Analysis Framework: a discovery metric and two characterization metrics, where one estimates how well the light curve is covered and the other quantifies how precisely event parameters can be determined. We also assess the characterizability of microlensing parallax, critical for detection of free-floating black hole lenses. We find that, given Rubin's baseline cadence, the discovery and characterization efficiency will be higher for longer-duration and larger-parallax events. Microlensing discovery efficiency is dominated by the observing footprint, where more time spent looking at regions of high stellar density, including the Galactic bulge, Galactic plane, and Magellanic Clouds, leads to higher discovery and characterization rates. However, if the observations are stretched over too wide an area, including low-priority areas of the Galactic plane with fewer stars and higher extinction, event characterization suffers by &gt;10%. This could impact exoplanet, binary star, and compact object events alike. We find that some rolling strategies (where Rubin focuses on a fraction of the sky in alternating years) in the Galactic bulge can lead to a 15%–20% decrease in microlensing parallax characterization, so rolling strategies should be chosen carefully to minimize losses.

Keywords: Optical observation, Sky surveys, Optical astronomy, Galactic bulge, Time domain astronomy, Gravitational microlensing, Milky Way Galaxy
Mining Double-line Spectroscopic Candidates in the LAMOST Medium-resolution Spectroscopic Survey Using a Human─AI Hybrid Method. We utilize a hybrid approach that integrates the traditional cross-correlation function (CCF) and machine learning to detect spectroscopic multiple star systems, specifically focusing on double-line spectroscopic binaries (SB2s). Based on the ninth data release (DR9) of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), which includes a medium-resolution survey (MRS) containing 29,920,588 spectra, we identify 27,164 double-line and 3124 triple-line spectra, corresponding to 7096 SB2 candidates and 1903 triple-line spectroscopic binary (SB3) candidates, respectively, representing about 1% of the selected data set from LAMOST-MRS DR9. Notably, 70.1% of the SB2 candidates and 89.6% of the SB3 candidates are newly identified. Compared to using only the traditional CCF technique, our method significantly improves the efficiency of detecting SB2s, saving time on visual inspections by a factor of 4.

Keywords: Spectroscopic binary stars, Binary stars
The Blue Horizontal-branch Stars from the LAMOST Survey: Atmospheric Parameters. Blue horizontal-branch (BHB) stars are crucial for studying the structure of the Galactic halo. Accurate atmospheric parameters of BHB stars are essential for investigating the formation and evolution of the Galaxy. In this work, a data-driven technique named Stellar Label Machine (SLAM) is used to estimate the atmospheric parameters of Large Sky Area Multi-Object Fiber Spectroscopic Telescope low-resolution spectra for BHB stars with a set of A-type theoretical spectra as the training data set. We add color indexes ((BP − G), (G − RP), (BP − RP), (J − H)) during the training process to constrain the stellar temperature further. Finally, we derive the atmospheric parameters (Teff, log g, [Fe/H]) for 5355 BHB stars. Compared to existing literature results, our results are more robust, and after taking the color index into account the resulting precision of Teff and log g is significantly improved, especially for the spectrum with a low signal-to-noise ratio (S/N). Based on the duplicate observations with an S/N difference &lt;20%, the random errors are around 30 K, 0.1 dex, and 0.12 dex for Teff, log g, and [Fe/H], respectively. The stellar labels provided by SLAM are also compared to those from the high-resolution spectra in the literature. The standard deviation between the predicted star labels and the published values from the high-resolution spectra is adopted as the statistical uncertainty of our results. They are σ(Teff) = 76 K, σ(log g) = 0.04 dex, and σ([Fe/H]) = 0.09 dex, respectively.

Keywords: Fundamental parameters of stars, Horizontal branch stars, Surveys
Simulating a Steady-state Heliosphere. Ever since the discovery of the solar wind, it has been understood that our solar system resides within a bubble of solar wind plasma that originates from the Sun. Due to its vast size, the geometry of this bubble, known as the heliosphere, remains a topic of conjecture and debate. Three-dimensional simulations, employing magnetohydrodynamics coupled to neutral particles, provide a key way of understanding the global flow dynamics on macroscopic scales. While many of these models suggest a comet-like shape with a single heliotail for the heliosphere, others have suggested geometries with two distinct outflows of solar wind plasma. In this paper we show how numerical simulations with the same boundary conditions may evolve differently, depending on factors such as initial conditions and run time. We find that the eventual steady state is the same comet-like shape, even for less than optimal choices of the initial condition.

Keywords: Heliopause, Astrosphere interstellar medium interactions, Heliosphere
Reduction and Archiving of Multiwavelength, Polarized-intensity Debris-disk Observations with the Gemini Planet Imager. The Gemini Planet Imager (GPI), an extreme adaptive optics instrument on Gemini South, has been pivotal in the advancement of the debris-disk field. Over the past decade, GPI has observed tens of debris disks at near-infrared wavelengths in both polarized and total intensity as a part of several direct-imaging surveys. Here we discuss the uniform reductions of the J-, H- and K1-band GPI observations, specifically in polarized intensity. This includes 24 debris-disk observations in the H band, 10 debris-disks observations in the J band and 11 disk observations in the K1 band. Additionally, all three reduced data sets have been archived on the digital platform, CANFAR, so that they are available for public use. The purpose of this work is to provide the necessary steps for one to carry out their own data reductions if desired, as well as to create a space where these uniformly reduced data are easily accessible for future analysis and research.

Keywords: Astronomy data acquisition, Debris disks, Astronomy data reduction, Near infrared astronomy
A Compilation of Optical Starlight Polarization Catalogs. Polarimetry of stars at optical and near-infrared wavelengths is an invaluable tool for tracing interstellar dust and magnetic fields. Recent studies have demonstrated the power of combining stellar polarimetry with distances from the Gaia mission, in order to gain accurate, 3D information on the properties of the interstellar magnetic field and the dust distribution. However, access to optical polarization data is limited, as observations are conducted by different investigators, with different instruments, and are made available in many separate publications. To enable a more widespread accessibility of optical polarimetry for studies of the interstellar medium, we compile a new catalog of stellar polarization measurements. The data are gathered from 81 separate publications spanning two decades since the previous, widely used agglomeration of catalogs by C. Heiles. The compilation contains a total of 55,742 measurements of stellar polarization. We combine this database with stellar distances based on the Gaia Early Data Release 3, thereby providing polarization and distance data for 42,482 unique stars. We provide two separate data products: an extended catalog (containing all polarization measurements) and a unique source catalog (containing a subset of sources excluding duplicate measurements). We propose the use of a common tabular format for the publication of stellar polarization catalogs to facilitate accessibility and increase discoverability in the future.

Keywords: Surveys, Interstellar magnetic fields, Starlight polarization, Interstellar medium, Catalogs
cogsworth: A Gala of COSMIC Proportions Combining Binary Stellar Evolution and Galactic Dynamics. We present cogsworth, an open-source Python tool for producing self-consistent population synthesis and galactic dynamics simulations. cogsworth allows users to (1) sample a population of binaries and star formation history, (2) perform rapid (binary) stellar evolution, (3) integrate orbits through the galaxy, and (4) inspect the full evolutionary history of each star or compact object, along with their positions and kinematics. It supports postprocessing hydrodynamical zoom-in simulations for more realistic galactic potentials and star formation histories, accounting for initial spatial stellar clustering and complex potentials. Alternatively, several analytic models are available for galactic potentials and star formation histories. cogsworth can also transform the intrinsic simulated population into an observed population using dust maps, bolometric correction functions, and survey selection functions. We provide a detailed explanation of the functionality of cogsworth and demonstrate its capabilities through a series of use cases: (1) we predict the spatial distribution of compact objects and runaways in both dwarf and Milky Way–like galaxies; (2) using a star cluster from a hydrodynamical simulation, we show how supernovae can change the orbits of stars in several ways; and (3) we predict the separation of disrupted binary stellar companions on the sky and create a synthetic Gaia color–magnitude diagram. We use cogsworth to demonstrate that both binarity and the galactic potential have a significant impact of the present-day positions of massive stars. We designed cogsworth and its online documentation to provide a powerful tool for constraining binary evolution, but also a flexible and accessible resource for the entire community.✎ ✎

Keywords: Open source software, Galaxy dynamics, Binary stars, Stellar evolution
Short-period Heartbeat Binaries from TESS Full-frame Images. We identify 240 short-period (P ≲ 10 days) binary systems in Transiting Exoplanet Survey Satellite data, 180 of which are heartbeat binaries. The sample is mostly a mix of A- and B-type stars and primarily includes eclipsing systems, where over 30% of the sources with primary and secondary eclipses show a secular change in their intereclipse timings and relative eclipse depths over a multiyear timescale, likely due to orbital precession. The orbital parameters of the population are estimated by fitting a heartbeat model to their phase curves and Gaia magnitudes, where the model accounts for ellipsoidal variability, Doppler beaming, reflection effects, and eclipses. We construct the sample's period─eccentricity distribution and find an eccentricity cutoff (where e → 0) at a period of 1.7 days. Additionally, we measure the periastron advance rate for 12 of the precessing sources and find that they all exhibit prograde apsidal precession, which is as high as 9° yr−1 for one of the systems. Using the inferred stellar parameters, we estimate the general relativistic precession rate of the argument of periastron for the population and expect over 30 systems to show a precession in excess of 0.3° yr−1.

Keywords: Eclipsing binary stars, Close binary stars
A Study of Real-time Detection Methods for Solar Radio Burst Identification. Solar flares, coronal mass ejections, and other solar radio burst phenomena release substantial amounts of solar radiation energy, resulting in adverse space weather conditions and posing significant hazards in space. Spectrum analysis conducted manually or with traditional image processing algorithms is limited by low efficiency and accuracy. This paper investigates solar radio burst detection methods and their applications. Five solar radio burst detection methods—Continuous-3σ, Sum Flux-3σ, Continuous Slope, Sum Flux Slope, and Sum Flux Continuous-3σ—are developed and validated using data from the Japanese NoRP and the Australian Learmonth Solar Radio Observatory. The results show that all five methods can detect solar radio bursts to some degree. Considering the combined metrics of success rate, false detection rate, and real-time performance, the Sum Flux Continuous-3σ method is deemed the optimal method among the five. Additionally, the Sum Flux Slope method, which is not reliant on historical data, demonstrates superior universality. Finally, we implement the Sum Flux Slope method on a 39.5–40 GHz two-element interferometer, achieving real-time solar radio burst detection in the upper computer software. The method also includes functionalities for email alerts, burst information recording, and control parameter adjustment, confirming its effectiveness and practicality. Test results demonstrate the method's effectiveness in real-time solar radio burst detection.

Keywords: Solar radio flares, Radio bursts, Experimental techniques, Radio astrometry
Mass, Luminosity, and Stellar Age of Early-type Stars from the LAMOST Survey. Mass (M) and luminosity (L) are fundamental parameters of stars but can only be measured indirectly. Typically, effective temperature (T eff), surface gravity ( ), and metallicity ([M/H]) are derived from stellar spectra, then M, L, and stellar age (t) can be obtained by interpolating in the grid of stellar evolutionary models. In this paper, we use random forest in combination with the evolutionary grid from PARSEC 1.2S to determine M, L, t, and initial mass (M i) for early-type main-sequence stars (T eff ≥ 7000 K) as identified from the LAMOST survey. The convex hull algorithm is employed to select the main-sequence stars. The results demonstrate that the prediction precision is 32% and 9% for L and M, respectively, which is comparable to that achieved by fitting evolutionary tracks. Furthermore, the predicted L also aligns with Gaia's observations, with a relative difference of 36%. The prediction for t is relatively less accurate, indicating a difference of 0.44 Gyr for two components in wide binaries. This discrepancy is due to the inconsistent metallicity measurements. For the two sets of atmospheric parameters we used, the relative differences in L, M, t, and M i are 29%, 7%, 36%, and 7%, respectively. The influence of metallicity on these parameters is analyzed, with the conclusion that metallicity has the greatest impact on t. Consequently, two catalogs are presented, which could be useful for studying stellar populations such as the luminosity function and initial mass function of early-type stars.

Keywords: Early-type stars, Random Forests, Catalogs
DRAFTS: A Deep-learning-based Radio Fast Transient Search Pipeline. The detection of fast radio bursts (FRBs) in radio astronomy is a complex task due to the challenges posed by radio-frequency interference and signal dispersion in the interstellar medium. Traditional search algorithms are often inefficient, time-consuming, and generate a high number of false positives. In this paper, we present DRAFTS, a deep-learning-based radio fast transient search pipeline. DRAFTS integrates object detection and binary classification techniques to accurately identify FRBs in radio data. We developed a large, real-world data set of FRBs for training deep-learning models. The search test on Five-hundred-meter Aperture Spherical radio Telescope real observation data demonstrates that DRAFTS performs exceptionally in terms of accuracy, completeness, and search speed. In the re-search of FRB 20190520B observation data, DRAFTS detected more than 3 times the number of bursts compared to HEIMDALL, highlighting the potential for future FRB detection and analysis.

Keywords: Convolutional neural networks, Astronomy databases, Astronomy software, Radio transient sources
COSMIC: A Galaxy Cluster─Finding Algorithm Using Machine Learning. Building a comprehensive catalog of galaxy clusters is a fundamental task for studies on structure formation and galaxy evolution. In this paper, we present Cluster Optical Search using Machine Intelligence in Catalogs (COSMIC), an algorithm utilizing machine learning techniques to efficiently detect galaxy clusters. COSMIC involves two steps, the identification of the brightest cluster galaxies and the estimation of cluster richness. We train our models on galaxy data from the Sloan Digital Sky Survey and the WHL galaxy cluster catalog. Validated against test data in the region of the northern Galactic cap, the COSMIC algorithm demonstrates high completeness when crossmatching with previous cluster catalogs. Richness comparison with previous optical and X-ray measurements also demonstrates a tight correlation. Our methodology showcases robust performance in galaxy cluster detection and holds promising prospects for applications in upcoming large-scale surveys. The COSMIC codes are published on https://github.com/tdccccc/COSMIC.

Keywords: Galaxy clusters, Brightest cluster galaxies, Convolutional neural networks, Classification
Kinematics of Young Stellar Objects under Various Stellar Feedback. Based on the Gaia Data Release 3 and APOGEE data sets, we investigate the kinematic differences between young stellar objects (YSOs) and their parent clouds in five nearby star-forming regions. Overall, the one-dimensional velocity differences between Class II YSOs and their parent molecular cloud range from [0, 1.4] km s−1. In feedback environments dominated by outflows, massive stars, and supernova feedback, the corresponding velocity differences range from [0, 1.4], [0.1, 0.4], and [0.1, 1] km s−1, respectively. These results indicate that YSO kinematics are not significantly affected by these different types of feedback environments. Additionally, compared to the Class II YSOs, Class III YSOs have slightly larger velocities and dispersions.

Keywords: Stellar kinematics, Young stellar objects, Molecular clouds
Semiclassical Estimates of Pressure-induced Line Widths for Infrared Absorption in Hot (Exo)planetary Atmospheres. Because of elevated temperatures and high fluxes of stellar radiation irreproducible in laboratory conditions, molecules and molecular ions found or expected in exoplanetary atmospheres are generally poorly characterized from the viewpoint of their spectroscopic line-shape parameters; in many cases, there are no data at all. Advanced theoretical approaches (classical, semiclassical, and quantum mechanical), without mentioning their high computational cost, are also impracticable due to the lack of potential energy surfaces. To fill this gap of crucially missing line-broadening parameters, we provide estimated values issued from a simple rotationally independent semiclassical expression. Only the index related to the leading long-range interaction term, molecular masses and kinetic diameters, as well as temperature are used as input parameters. A wide range of absorbers and perturbation by He, Ar, H2, N2, O2, CO, NO, CO2, H2O, CH4, and NH3 as well as self-perturbation are considered. The explicit temperature dependence T‑0.5 allows calculations to be limited to the single reference temperature of 296 K; for other temperatures a simple scaling can be used. The full set of line-broadening coefficients obtained with various possible values of kinetic diameters is provided by the new Collisional Line-broadening Parameters database, which is specifically designed for this purpose. "Midvalue" (or more recent) kinetic diameters are retained to create one-value line-broadening data required to populate the ExoMol database. A way to generate rotationally dependent line widths is proposed.

Keywords: Molecular data, Molecular spectroscopy, Stellar atmospheric opacity, Collisional broadening, Exoplanet atmospheres
Application of Machine Learning to Background Rejection in Very-high-energy Gamma-Ray Observation. Identifying gamma rays and rejecting the background of cosmic-ray hadrons are crucial for very-high-energy gamma-ray observations and relevant scientific research. Based on the simulated data from the square kilometer array (KM2A) of LHAASO, eight high-level features were extracted for the gamma/hadron classification. Machine learning (ML) models, including logistic regression, support vector machines, decision trees, random forests, XGBoost, CatBoost, and deep neural networks (DNN) were constructed and trained using data sets of four energy bands ranging from 1012 to 1016 eV, and finally fused using the stacking ensemble algorithm. To comprehensively assess the classification ability of each model, the accuracy, F1 score, precision, recall, and area under the curve value of the receiver operating characteristic curve were used. The results show that the ML methods have a significant improvement on particle classification in LHAASO-KM2A, particularly in the low-energy range. Among these methods, XGBoost, CatBoost, and DNN demonstrate stronger classification capabilities than decision trees and random forests, while the fusion model exhibits the best discriminatory ability. The ML methods provide a useful and alternative method for gamma/hadron identification. The codes used in this paper are available at Zenodo at doi:10.5281/zenodo.13623261.

Keywords: Computational astronomy, Classification, High-energy cosmic radiation, Astronomy software, Astronomy data analysis, Computational methods, Interdisciplinary astronomy, Cosmic rays