Sazuna/UAT_keywords huggingface dataset:
A λ 3 mm Line Survey toward the Circumstellar Envelope of the Carbon-rich AGB Star IRC+10216 (CW Leo). We present an unbiased λ 3 mm spectral line survey (between 84.5 and 115.8 GHz), conducted by the Purple Mountain Observatory 13.7 m radio telescope, together with updated modeling results, toward the carbon-rich asymptotic giant branch star IRC+10216 (CW Leo). A total of 75 spectral lines (96 transitions) are detected, and identified to arise from 19 molecules: C2H, l-C3H, C4H, CN, C3N, HC3N, HC5N, HCN, HNC, CH3CN, MgNC, CO, c-C3H2, SiC2, SiO, SiS, CS, C2S, C3S, and their isotopologues. Among them, one molecular emission line (H13CCCN J = 13–12) is discovered in IRC+10216 for the first time. The excitation temperature, column density, and fractional abundance of the detected species are deduced by assuming they are in local thermodynamic equilibrium. In addition, the isotopic ratios of [12C]/[13C], [32S]/[34S], [28Si]/[29Si], and [12C34S]/[13C32S] are obtained and found to be consistent with previous studies. Finally, we summarize all of the 106 species detected in IRC+10216 to date with their observed and modeled column densities for the convenience of future studies.

Keywords: Astrochemistry
Physically Motivated Deep Learning to Superresolve and Cross Calibrate Solar Magnetograms. Superresolution (SR) aims to increase the resolution of images by recovering detail. Compared to standard interpolation, deep learning-based approaches learn features and their relationships to leverage prior knowledge of what low-resolution patterns look like in higher resolution. Deep neural networks can also perform image cross-calibration by learning the systematic properties of the target images. While SR for natural images aims to create perceptually convincing results, SR of scientific data requires careful quantitative evaluation. In this work, we demonstrate that deep learning can increase the resolution and calibrate solar imagers belonging to different instrumental generations. We convert solar magnetic field images taken by the Michelson Doppler Imager (resolution ∼2″ pixel‑1; space based) and the Global Oscillation Network Group (resolution ∼2.″5 pixel‑1; ground based) to the characteristics of the Helioseismic and Magnetic Imager (resolution ∼0.″5 pixel‑1; space based). We also establish a set of performance measurements to benchmark deep-learning-based SR and calibration for scientific applications.

Keywords: The Sun, Solar active regions, Solar magnetic fields, Solar physics
The JWST Resolved Stellar Populations Early Release Science Program. V. DOLPHOT Stellar Photometry for NIRCam and NIRISS. We present NIRCam and NIRISS modules for DOLPHOT, a widely used crowded-field stellar photometry package. We describe details of the modules including pixel masking, astrometric alignment, star finding, photometry, catalog creation, and artificial star tests. We tested these modules using NIRCam and NIRISS images of M92 (a Milky Way globular cluster), Draco II (an ultrafaint dwarf galaxy), and Wolf–Lundmark–Mellote (a star-forming dwarf galaxy). DOLPHOT's photometry is highly precise, and the color–magnitude diagrams are deeper and have better definition than anticipated during original program design in 2017. The primary systematic uncertainties in DOLPHOT's photometry arise from mismatches in the model and observed point-spread functions (PSFs) and aperture corrections, each contributing ≲0.01 mag to the photometric error budget. Version 1.2 of WebbPSF models, which include charge diffusion and interpixel capacitance effects, significantly reduced PSF-related uncertainties. We also observed minor (≲0.05 mag) chip-to-chip variations in NIRCam's zero-points, which will be addressed by the JWST flux calibration program. Globular cluster observations are crucial for photometric calibration. Temporal variations in the photometry are generally ≲0.01 mag, although rare large misalignment events can introduce errors up to 0.08 mag. We provide recommended DOLPHOT parameters, guidelines for photometric reduction, and advice for improved observing strategies. Our Early Release Science DOLPHOT data products are available on MAST, complemented by comprehensive online documentation and tutorials for using DOLPHOT with JWST imaging data.

Keywords: Stellar photometry, Hertzsprung Russell diagram, James Webb Space Telescope, Local Group
Metallicities and Refined Stellar Parameters for 52 Cool Dwarfs with Transiting Planets and Planet Candidates. We collected near-infrared spectra of 65 cool stars with the NASA Infrared Telescope Facility and analyzed them to calculate accurate metallicities and stellar parameters. The sample of 55 M dwarfs and 10 K dwarfs includes 25 systems with confirmed planets and 27 systems with planet candidates identified by the K2 and TESS missions. Three of the 25 confirmed planetary systems host multiple confirmed planets and two of the 27 planet candidate systems host multiple planet candidates. Using the new stellar parameters, we refit the K2 and TESS light curves to calculate updated planet properties. In general, our updated stellar properties are more precise than those previously reported and our updated planet properties agree well with those in the literature. Lastly, we briefly examine the relationship between stellar mass, stellar metallicity, and planetary system properties for targets in our sample and for previously characterized planet-hosting low-mass stars. We provide our spectra, stellar parameters, and new planetary fits to the community, expanding the sample available with which to investigate correlations between stellar and planetary properties for low-mass stars.

Keywords: M dwarf stars, Stellar properties, Exoplanets, Exoplanet systems, Low mass stars, Spectroscopy
Modeling the Morphology of Fast Radio Bursts and Radio Pulsars with fitburst. We present a framework for modeling astrophysical pulses from radio pulsars and fast radio bursts (FRBs). This framework, called fitburst, generates synthetic representations of dynamic spectra that are functions of several physical and heuristic parameters; the heuristic parameters can nonetheless accommodate a vast range of distributions in spectral energy. fitburst is designed to optimize the modeling of features induced by effects that are intrinsic and extrinsic to the emission mechanism, including the magnitude and frequency dependence of pulse dispersion and scatter broadening. fitburst removes intrachannel smearing through two-dimensional upsampling, and can account for phase-wrapping of "folded" signals that are typically acquired during pulsar-timing observations. We demonstrate the effectiveness of fitburst in modeling data containing pulsars and FRBs observed with the Canadian Hydrogen Intensity Mapping Experiment telescope.

Keywords: Astronomy software, Pulsars, Interstellar medium, Interstellar scintillation, Radio transient sources
Modeling and Calibration of Gaia, Hipparcos, and Tycho-2 Astrometric Data for the Detection of Dark Companions. Hidden within the Gaia satellite's multiple data releases lies a valuable cache of dark companions. To facilitate the efficient and reliable detection of these companions via combined analyses involving the Gaia, Hipparcos, and Tycho-2 catalogs, we introduce an astrometric modeling framework. This method incorporates analytical least-square minimization and nonlinear parameter optimization techniques to a set of common calibration sources across the different space-based astrometric catalogs. This enables us to discern the error inflation, astrometric jitter, differential parallax zero-points, and frame rotation of various catalogs relative to Gaia Data Release 3 (DR3). Our findings yield the most precise Gaia DR2 calibration parameters to date, revealing notable dependencies on magnitude and color. Intriguingly, we identify submilliarcsecond frame rotation between Gaia DR1 and DR3, along with an estimated astrometric jitter of 2.16 mas for the revised Hipparcos catalog. In a thorough comparative analysis with previous studies, we offer recommendations on calibrating and utilizing different catalogs for companion detection. Furthermore, we provide a user-friendly pipeline (https://github.com/ruiyicheng/Download_HIP_Gaia_GOST) for catalog download and bias correction, enhancing accessibility and usability within the scientific community.

Keywords: Astrometric exoplanet detection, Astrometric binary stars, Astrometry, Bayes factor
Experimental Branching Fractions, Transition Probabilities, and Oscillator Strengths in Sm II. Branching fractions (BFs) of Sm II for 71 lines from 12 excited levels ranging from 31,638.79 to 35,463.91 cm‑1 were determined for the first time based on the Fourier transform spectra available from the National Solar Observatory database. New transition probabilities and oscillator strengths for these lines were derived by combining the determined BFs with reliable lifetimes measured using a time-resolved laser-induced fluorescence technique. Furthermore, BFs for 38 lines from five levels included in earlier studies were also determined for comparisons. The new results reported in this work will be useful in many fields, especially for astrophysics.

Keywords: Transition probabilities, Atomic spectroscopy, Radiative processes, Atomic physics
Ultradiffuse Galaxies—A Distinct Population? Dwarf Galaxies in the Coma Cluster and A262 from Deep u'–g'–r' Wendelstein Imaging Data. In this study, we compare the structural parameters of ultradiffuse galaxies (UDGs) to those of other dwarf galaxies and investigate whether UDGs form a distinct population. We observed deep $u^{\prime} $ -, $g^{\prime} $ -, and $r^{\prime} $ -band images (maximum limiting surface brightness [3σ, 10″ × 10″] $u^{\prime} $ and $g^{\prime} $ : $\approx \ 30\,\mathrm{mag}\,{\mathrm{arcsec}}^{-2}$ ; $r^{\prime} $ : $\approx \,29\,\mathrm{mag}\,{\mathrm{arcsec}}^{-2}$ ) of A1656 (Coma cluster) and A262 with the Wendelstein Wide Field Imager at the 2.1 m Fraunhofer Wendelstein Telescope at the Wendelstein Observatory. We measure $u^{\prime} -g^{\prime} $ and $g^{\prime} -r^{\prime} $ colors and structural parameters using parametric fitting of tens of thousands of potential UDGs and other dwarf galaxies. Cluster members are identified and separated from diffuse background galaxies based on red sequence membership and location in the $u^{\prime} -g^{\prime} $ versus $g^{\prime} -r^{\prime} $ color–color diagram. We find 11 UDGs in A262 and 48 UDGs in A1656. The latter is 6 times more than van Dokkum et al. found in the overlapping region. By comparing the structural parameters of UDGs to non-UDGs in our sample and to spheroidals from the literature, we do not find any separation in all tested parameter spaces. Instead, UDGs form the diffuse end of the already well-known spheroidal population and slightly extend it. Furthermore, we find that the UDG definition used by Koda et al. and Yagi et al. mainly extends the definition by van Dokkum et al. toward ordinary spheroidals.

Keywords: Galaxy clusters, Dwarf galaxies, Galaxy structure, Coma Cluster, Galaxy photometry, Galaxy evolution, Dwarf spheroidal galaxies, Extragalactic astronomy, Abell clusters, Galaxies, Low surface brightness galaxies
StellarGAN: Classifying Stellar Spectra with Generative Adversarial Networks in SDSS and APOGEE Sky Surveys. Extracting precise stellar labels is crucial for large spectroscopic surveys like the Sloan Digital Sky Survey (SDSS) and APOGEE. In this paper, we report the newest implementation of StellarGAN, a data-driven method based on generative adversarial networks (GANs). Using 1D operators like convolution, the 2D GAN is modified into StellarGAN. This allows it to learn the relevant features of 1D stellar spectra without needing labels for specific stellar types. We test the performance of StellarGAN on different stellar spectra trained on SDSS and APOGEE data sets. Our result reveals that StellarGAN attains the highest overall F1-score on SDSS data sets (F1-score = 0.82, 0.77, 0.74, 0.53, 0.51, 0.61, and 0.55, for O-type, B-type, A-type, F-type, G-type, K-type, and M-type stars) when the signal-to-noise ratio (S/N) is low (90% of the spectra have an S/N &lt; 50), with 1% of labeled spectra used for training. Using 50% of the labeled spectral data for training, StellarGAN consistently demonstrates performance that surpasses or is comparable to that of other data-driven models, as evidenced by the F1-scores of 0.92, 0.77, 0.77, 0.84, 0.84, 0.80, and 0.67. In the case of APOGEE (90% of the spectra have an S/N &lt; 500), our method is also superior regarding its comprehensive performance (F1-score = 0.53, 0.60, 0.56, 0.56, and 0.78 for A-type, F-type, G-type, K-type, and M-type stars) with 1% of labeled spectra for training, manifesting its learning ability out of a limited number of labeled spectra. Our proposed method is also applicable to other types of data that need to be classified (such as gravitational-wave signals, light curves, etc.).

Keywords: Stellar classification, Stellar spectral types
CatNorth: An Improved Gaia DR3 Quasar Candidate Catalog with Pan-STARRS1 and CatWISE. A complete and pure sample of quasars with accurate redshifts is crucial for quasar studies and cosmology. In this paper, we present CatNorth, an improved Gaia Data Release 3 (Gaia DR3) quasar candidate catalog with more than 1.5 million sources in the 3π sky built with data from Gaia, Pan-STARRS1, and CatWISE2020. The XGBoost algorithm is used to reclassify the original Gaia DR3 quasar candidates as stars, galaxies, and quasars. To construct training/validation data sets for the classification, we carefully built two different master stellar samples in addition to the spectroscopic galaxy and quasar samples. An ensemble classification model is obtained by averaging two XGBoost classifiers trained with different master stellar samples. Using a probability threshold of p QSO_mean &gt; 0.95 in our ensemble classification model and an additional cut on the logarithmic probability density of zero proper motion, we retrieved 1,545,514 reliable quasar candidates from the parent Gaia DR3 quasar candidate catalog. We provide photometric redshifts for all candidates with an ensemble regression model. For a subset of 89,100 candidates, accurate spectroscopic redshifts are estimated with the convolutional neural network from the Gaia BP/RP spectra. The CatNorth catalog has a high purity of ∼90%, while maintaining high completeness, which is an ideal sample to understand the quasar population and its statistical properties. The CatNorth catalog is used as the main source of input catalog for the Large Sky Area Multi-Object Fiber Spectroscopic Telescope phase III quasar survey, which is expected to build a highly complete sample of bright quasars with i &lt; 19.5.

Keywords: Quasars, Astrostatistics techniques, Active galactic nuclei, Redshift surveys, Catalogs
The Initial Mass Function Based on the Full-sky 20 pc Census of ∼3600 Stars and Brown Dwarfs. A complete accounting of nearby objects—from the highest-mass white dwarf progenitors down to low-mass brown dwarfs—is now possible, thanks to an almost complete set of trigonometric parallax determinations from Gaia, ground-based surveys, and Spitzer follow-up. We create a census of objects within a Sun-centered sphere of 20 pc radius and check published literature to decompose each binary or higher-order system into its separate components. The result is a volume-limited census of ∼3600 individual star formation products useful in measuring the initial mass function across the stellar (&lt;8M ☉) and substellar (≳5M Jup) regimes. Comparing our resulting initial mass function to previous measurements shows good agreement above 0.8M ☉ and a divergence at lower masses. Our 20 pc space densities are best fit with a quadripartite power law, $\xi (M)={dN}/{dM}\propto {M}^{-\alpha }$ , with long-established values of α = 2.3 at high masses (0.55 &lt; M &lt; 8.00M ☉), and α = 1.3 at intermediate masses (0.22 &lt; M &lt; 0.55M ☉), but at lower masses, we find α = 0.25 for 0.05 &lt; M &lt; 0.22M ☉, and α = 0.6 for 0.01 &lt; M &lt; 0.05M ☉. This implies that the rate of production as a function of decreasing mass diminishes in the low-mass star/high-mass brown dwarf regime before increasing again in the low-mass brown dwarf regime. Correcting for completeness, we find a star to brown dwarf number ratio of, currently, 4:1, and an average mass per object of 0.41 M ☉.

Keywords: Trigonometric parallax, Brown dwarfs, Initial mass function, Binary stars, Solar neighborhood, Stellar mass functions
Is It Possible to Know Cosmological Fine-tuning?. Fine-tuning studies whether some physical parameters, or relevant ratios between them, are located within so-called life-permitting intervals of small probability outside of which carbon-based life would not be possible. Recent developments have found estimates of these probabilities that circumvent previous concerns of measurability and selection bias. However, the question remains whether fine-tuning can indeed be known. Using a mathematization of the concepts of learning and knowledge acquisition, we argue that most examples that have been touted as fine-tuned cannot be formally assessed as such. Nevertheless, fine-tuning can be known when the physical parameter is seen as a random variable and it is supported in the nonnegative real line, provided the size of the life-permitting interval is small in relation to the observed value of the parameter.

Keywords: Bayesian statistics, Anthropic principle, Cosmological parameters, Analytical mathematics, Cosmological constant
Flare Hunting in Hot Subdwarf and White Dwarf Stars from Cycles 1–5 of TESS Photometry. Stellar flares are critical phenomena on stellar surfaces, which are closely tied to stellar magnetism. While extensively studied in main-sequence (MS) stars, their occurrence in evolved compact stars, specifically hot subdwarfs and white dwarfs (WDs), remains scarcely explored. Based on Cycles 1–5 of TESS photometry, we conducted a pioneering survey of flare events in ∼12,000 compact stars, corresponding to ∼38,000 light curves with a 2 minute cadence. Through dedicated techniques for detrending light curves, identifying preliminary flare candidates, and validating them via machine learning, we established a catalog of 1016 flares from 193 compact stars, including 182 from 58 sdB/sdO stars and 834 from 135 WDs, respectively. However, all flaring compact stars showed signs of contamination from nearby objects or companion stars, preventing sole attribution of the detected flares. For WDs, it is highly probable that the flares originated from their cool MS companions. In contrast, the higher luminosities of sdB/sdO stars diminish companion contributions, suggesting that detected flares originated from sdB/sdO stars themselves or through close magnetic interactions with companions. Focusing on a refined sample of 23 flares from 13 sdB/sdO stars, we found their flare frequency distributions were slightly divergent from those of cool MS stars; instead, they resemble those of hot B/A-type MS stars having radiative envelopes. This similarity implies that the flares on sdB/sdO stars, if these flares did originate from them, may share underlying mechanisms with hot MS stars, which warrants further investigation.

Keywords: White dwarf stars, B subdwarf stars, Stellar flares, Photometry, Random Forests
Lithium Abundances from the LAMOST Medium-resolution Survey Data Release 9. Lithium is a fragile but crucial chemical element in the Universe, and exhibits interesting and complex behaviors. Thanks to the mass of spectroscopic data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) medium-resolution survey (MRS), we can investigate the lithium abundances in a large and diverse sample of stars, which could bring vital help in studying the origin and evolution of lithium. In this work, we use the Li I 6707.8 Å line to derive the lithium abundance through a template-matching method. A catalog of precise lithium abundance is presented for 795,384 spectra corresponding to 455,752 stars from the LAMOST MRS Data Release 9. Comparing our results with those of external high-resolution references, we find good consistency with a typical deviation of σ A(Li) ∼ 0.2 dex. We also analyze the internal errors using stars that have multiple LAMOST MRS observations, which will reach as low as 0.1 dex when the signal-to-noise ratio of the spectra is &gt;20. Besides, our result indicates that a small fraction of giant stars still exhibit a surprisingly high lithium content, and 967 stars are identified as Li-rich giants with A(Li) &gt; 1.5 dex, accounting for ∼2.6% of our samples. If one takes into account the fact that nearly all stars deplete lithium during the main sequence, then the fraction of Li-rich stars may far exceed 2.6%. This new catalog covers a wide range of stellar evolutionary stages from pre-main sequence to giants, and will provide help to the further study of the chemical evolution of lithium.

Keywords: Sky surveys, Late-type stars, Nucleosynthesis, Fundamental parameters of stars, Chemical abundances, Low mass stars, Lithium stars, Chemically peculiar stars, Catalogs, Stellar evolution
An Algorithm for the Determination of Coronal Mass Ejection Kinematic Parameters Based on Machine Learning. Coronal mass ejections (CMEs) constitute the major source of severe space weather events, with the potential to cause enormous damage to humans and spacecraft in space. It is becoming increasingly important to detect and track CMEs, since there are more and more space activities and facilities. We have developed a new algorithm to automatically derive a CME's kinematic parameters based on machine learning. Our method consists of three steps: recognition, tracking, and the determination of parameters. First, we train a convolutional neural network to classify images from Solar and Heliospheric Observatory Large Angle Spectrometric Coronagraph observations into two categories, containing CME(s) or not. Next, we apply the principal component analysis algorithm and Otsu's method to acquire binary-labeled CME regions. Then, we employ the track-match algorithm to track a CME's motion in time-series images and finally determine the CME's kinematic parameters, e.g., velocity, angular width, and central position angle. The results of four typical CME events with different morphological characteristics are presented and compared with a manual CME catalog and several automatic CME catalogs. Our algorithm shows some advantages in the recognition of CME structure and the accuracy of the kinematic parameters. This algorithm can be helpful for real-time CME warnings and predictions. In the future, this algorithm is capable of being applied to CME initialization in magnetohydrodynamic simulations to study the propagation characteristics of real CME events and to provide more efficient predictions of CMEs' geoeffectiveness.

Keywords: Space weather, Convolutional neural networks, Solar coronal mass ejections