Sazuna/UAT_keywords huggingface dataset:
Best Practices for Data Publication in the Astronomical Literature. We present an overview of best practices for publishing data in astronomy and astrophysics journals. These recommendations are intended as a reference for authors to help prepare and publish data in a way that will better represent and support science results, enable better data sharing, improve reproducibility, and enhance the reusability of data. Observance of these guidelines will also help to streamline the extraction, preservation, integration and cross-linking of valuable data from astrophysics literature into major astronomical databases, and consequently facilitate new modes of science discovery that will better exploit the vast quantities of panchromatic and multidimensional data associated with the literature. We encourage authors, journal editors, referees, and publishers to implement the best practices reviewed here, as well as related recommendations from international astronomical organizations such as the International Astronomical Union for publication of nomenclature, data, and metadata. A convenient Checklist of Recommendations for Publishing Data in the Literature (Appendix A) is included for authors to consult before the submission of the final version of their journal articles and associated data files. We recommend that publishers of journals in astronomy and astrophysics incorporate a link to this document in their Instructions to Authors.

Keywords: Astronomical coordinate systems, Astronomical object identification, Astronomy databases, Observatories, Astronomical reference materials, Astrometry, Photometry, Telescopes
Selection of the Main Control Parameters for the Dst Index Prediction Model Based on a Layer-wise Relevance Propagation Method. The prediction of the Dst index is an important subject in space weather. It has significant progress with the prevalent applications of neural networks. The selection of input parameters is critical for the prediction model of the Dst index or other space-weather models. In this study, we perform a layer-wise relevance propagation (LRP) method to select the main parameters for the prediction of the Dst index and understand the physical interpretability of neural networks for the first time. Taking an hourly Dst index and 10 types of solar wind parameters as the inputs, we utilize a long short-term memory network to predict the Dst index and present the LRP method to analyze the dependence of the Dst index on these parameters. LRP defines the relevance score for each input, and a higher relevance score indicates that the corresponding input parameter contributes more to the output. The results show that Dst, E  y , B  z , and V are the main control parameters for Dst index prediction. In order to verify the LRP method, we design two more supplementary experiments for further confirmation. These results confirm that the LRP method can reduce the initial dimension of neural network input at the cost of minimum information loss and contribute to the understanding of physical processes in space weather.

Keywords: Solar-terrestrial interactions, Space weather, Solar wind, Geomagnetic fields
"Winged" Radio Sources from the LOFAR Two-meter Sky Survey First Data Release (LoTSS DR1). A small number of extragalactic radio sources disclose a pair of low-surface-brightness radio lobes, known as "wings," aligned at a certain angle to the primary jets. Such exotic sources are known as "winged" radio sources. Here we report the new identification of a total of 26 "winged" radio sources from the LOFAR Two-meter Sky Survey First Data Release (LoTSS DR1). Out of the 26 "winged" sources, 14 are identified as X-shaped radio galaxies and the remaining 12 as Z-shaped radio galaxies. The available optical counterpart of each radio galaxy is cataloged along with its estimated redshift. Among the 26 sources, 15 candidates are classified as FR-II radio galaxies, and two are classified as FR-I type. For nine candidates, no conclusions are drawn due to their complex morphology. We also calculate the physical parameters such as spectral index, radio luminosity, and power of the sources. We have made a statistical study of the spectral index by combining our estimated value with the spectral index collected from previous works. A mean value of spectral index of 0.71 is obtained.

Keywords: Radio continuum emission, Surveys, Active galactic nuclei, Catalogs
New Open-cluster Candidates Found in the Galactic Disk Using Gaia DR2/EDR3 Data. We report 541 new open-cluster candidates in Gaia EDR3 through revisiting the cluster results from an earlier analysis of Gaia DR2, which revealed nearly 1000 open-cluster candidates in the solar neighborhood (mostly d &lt;3 kpc) residing at Galactic latitudes ∣b∣ &lt; 20°. A subsequent comparison with lists of known clusters shows a large increase of the cluster samples within 2 kpc from the Sun. We assign membership probabilities to the stars through the open-source pyUPMASK algorithm, and also estimate the physical parameters through isochrone fitting for each candidate. Most of the new candidates show small total-proper-motion dispersions and clear features in the color-magnitude diagrams. Besides, the metallicity gradient of the new candidates is consistent with those found in the literature. The cluster parameters and member stars are available at CDS via anonymous ftp to cdsarc.u-strasbg.fr(130.79.128.5) or via https://cdsarc.unistra.fr/viz-bin/cat/J/ApJS/260/8. The discovery of these new objects shows that the open-cluster samples in Gaia data is still not complete, and more discoveries are expected in future research.

Keywords: Open star clusters, Star clusters
Decreasing False-alarm Rates in CNN-based Solar Flare Prediction Using SDO/HMI Data. A hybrid two-stage machine-learning architecture that addresses the problem of excessive false positives (false alarms) in solar flare prediction systems is investigated. The first stage is a convolutional neural network (CNN) model based on the VGG-16 architecture that extracts features from a temporal stack of consecutive Solar Dynamics Observatory Helioseismic and Magnetic Imager magnetogram images to produce a flaring probability. The probability of flaring is added to a feature vector derived from the magnetograms to train an extremely randomized trees (ERT) model in the second stage to produce a binary deterministic prediction (flare/no-flare) in a 12 hr forecast window. To tune the hyperparameters of the architecture, a new evaluation metric is introduced: the "scaled True Skill Statistic." It specifically addresses the large discrepancy between the true positive rate and the false positive rate in the highly unbalanced solar flare event training data sets. Through hyperparameter tuning to maximize this new metric, our two-stage architecture drastically reduces false positives by ≍48% without significantly affecting the true positives (reduction by ≍12%), when compared with predictions from the first-stage CNN alone. This, in turn, improves various traditional binary classification metrics sensitive to false positives, such as the precision, F1, and the Heidke Skill Score. The end result is a more robust 12 hr flare prediction system that could be combined with current operational flare-forecasting methods. Additionally, using the ERT-based feature-ranking mechanism, we show that the CNN output probability is highly ranked in terms of flare prediction relevance.

Keywords: Solar flares
A Flexible Method for Estimating Luminosity Functions via Kernel Density Estimation. II. Generalization and Python Implementation. We propose a generalization of our previous kernel density estimation (KDE) method for estimating luminosity functions (LFs). This new upgrade further extends the application scope of our KDE method, making it a very flexible approach that is suitable to deal with most bivariate LF calculation problems. From the mathematical point of view, usually the LF calculation can be abstracted as a density estimation problem in the bounded domain of $\{{Z}_{1}\lt z\lt {Z}_{2},\,L\gt {f}_{\mathrm{lim}}(z)\}$ . We use the transformation-reflection KDE method ( $\hat{\phi }$ ) to solve the problem, and introduce an approximate method ( ${\hat{\phi }}_{1}$ ) based on one-dimensional KDE to deal with the small sample size case. In practical applications, the different versions of LF estimators can be flexibly chosen according to the Kolmogorov-Smirnov test criterion. Based on 200 simulated samples, we find that for both cases of dividing or not dividing redshift bins, especially for the latter, our method performs significantly better than the traditional binning method ${\hat{\phi }}_{\mathrm{bin}}$ . Moreover, with the increase of sample size n, our LF estimator converges to the true LF remarkably faster than ${\hat{\phi }}_{\mathrm{bin}}$ . To implement our method, we have developed a public, open-source Python toolkit, called kdeLF. With the support of kdeLF, our KDE method is expected to be a competitive alternative to existing nonparametric estimators, due to its high accuracy and excellent stability. kdeLF is available online at GitHub with further extensive documentation available.

Keywords: Luminosity function, Smoothing, Astrostatistics techniques, Markov chain Monte Carlo
Critically Evaluated Spectral Data for Singly Ionized Carbon (C II). All available experimental data on the spectrum of singly ionized carbon have been critically analyzed. Measurement uncertainties of all published studies have been reassessed. The scope of observational data includes laboratory emission spectra of arcs, sparks, electrodeless discharges, and hollow cathode lamps recorded with grating and Fourier transform spectrometers, laboratory photoabsorption spectra, and emission spectra of planetary nebulae. The total number of observed spectral lines included in this compilation is 597. These lines participate in 972 transitions. From this list of identified transitions, we have derived a set of 414 energy levels, which are optimized using a least-squares fitting procedure. The identifications are supported by parametric calculations with Cowan's codes. The existing tables of critically evaluated transition probabilities have been extended with our newly calculated data. The ionization energy has been derived from the newly optimized energy levels with improved precision. Data on the isotope shifts and hyperfine structure have also been compiled.

Keywords: Line positions, Atomic physics, Spectral line identification, Isotope shifts, Atomic spectroscopy, Transition probabilities, Line intensities
Kinematics of Parsec-scale Jets of Gamma-Ray Blazars at 43 GHz during 10 yr of the VLBA-BU-BLAZAR Program. We analyze the parsec-scale jet kinematics from 2007 June to 2018 December of a sample of γ-ray bright blazars monitored roughly monthly with the Very Long Baseline Array (VLBA) at 43 GHz under the VLBA-BU-BLAZAR program. We implement a novel piecewise linear fitting method to derive the kinematics of 521 distinct emission knots from a total of 3705 total intensity images in 22 quasars, 13 BL Lacertae objects, and 3 radio galaxies. Apparent speeds of these components range from 0.01c to 78c, and 18.6% of knots (other than the "core") are quasi-stationary. One-fifth of moving knots exhibit nonballistic motion, with acceleration along the jet within 5 pc of the core (projected) and deceleration farther out. These accelerations occur mainly at locations coincident with quasi-stationary features. We calculate the physical parameters of 273 knots with statistically significant motion, including their Doppler factors, Lorentz factors, and viewing angles. We determine the typical values of these parameters for each jet and the average for each subclass of active galactic nuclei. We investigate the variability of the position angle of each jet over the 10 yr of monitoring. The fluctuations in position of the quasi-stationary components in radio galaxies tend to be parallel to the jet, while no directional preference is seen in the components of quasars and BL Lacertae objects. We find a connection between γ-ray states of blazars and their parsec-scale jet properties, with blazars with brighter 43 GHz cores typically reaching higher γ-ray maxima during flares.

Keywords: BL Lacertae objects, Blazars, Active galaxies, Relativistic jets, Flat-spectrum radio quasars, Interferometry
Recovering the CMB Signal with Machine Learning. The cosmic microwave background (CMB), carrying the inhomogeneous information of the very early universe, is of great significance for understanding the origin and evolution of our universe. However, observational CMB maps contain serious foreground contaminations from several sources, such as Galactic synchrotron and thermal dust emissions. Here, we build a deep convolutional neural network (CNN) to recover the tiny CMB signal from various huge foreground contaminations. Focusing on CMB temperature fluctuations, we find that the CNN model can successfully recover the CMB temperature maps with high accuracy, and that the deviation of the recovered power spectrum C  ℓ  is smaller than the cosmic variance at ℓ &gt; 10. We then apply this method to the current Planck observations, and find that the recovered CMB is quite consistent with that disclosed by the Planck Collaboration, which indicates that the CNN method can provide a promising approach to the component separation of CMB observations. Furthermore, we test the CNN method with simulated CMB polarization maps based on the CMB-S4 experiment. The result shows that both the EE and BB power spectra can be recovered with high accuracy. Therefore, this method will be helpful for the detection of primordial gravitational waves in current and future CMB experiments. The CNN is designed to analyze two-dimensional images, thus this method is not only able to process full-sky maps, but also partial-sky maps. Therefore, it can also be used for other similar experiments, such as radio surveys like the Square Kilometer Array.

Keywords: Observational cosmology, Cosmic microwave background radiation, Convolutional neural networks
Morpho-kinematic Properties of Wolf-Rayet Planetary Nebulae. The majority of planetary nebulae (PNs) show axisymmetric morphologies, whose causes are not well understood. In this work, we present spatially resolved kinematic observations of 14 Galactic PNs surrounding Wolf-Rayet ([WR]) and weak emission-line stars (wels), based on the Hα and [N II] emission taken with the Wide Field Spectrograph on the Australian National University 2.3 m telescope. Velocity-resolved channel maps and position-velocity diagrams, together with archival Hubble Space Telescope (HST) and ground-based images, are employed to construct three-dimensional morpho-kinematic models of 12 objects using the program SHAPE. Our results indicate that these 12 PNs mostly have elliptical morphologies, with either open or closed outer ends. The kinematic maps show the on-sky orientations of the interior shells in NGC 6578 and NGC 6629, as well as the compact (≤6″) PNs Pe 1-1, M 3-15, M 1-25, Hen 2-142, and NGC 6567, in agreement with the elliptically symmetric morphologies seen in high-resolution HST images. Point-symmetric knots in Hb 4 exhibit deceleration with distance from the central star, which could be due to shock collisions with the ambient medium. The velocity dispersion maps of Pe 1-1 also disclose the shock interaction between its collimated outflows and the interstellar medium. Collimated bipolar outflows are also visible in the position-velocity diagrams of M 3-30, M 1-32, and M 3-15, which are reconstructed by tenuous prolate ellipsoids extending upward from dense equatorial regions in the kinematic models. The formation of aspherical morphologies and collimated outflows in these PNs could be related to the stellar evolution of hydrogen-deficient [WR] and wels nuclei, which require further investigation.

Keywords: Stellar kinematics, Stellar jets, Planetary nebulae, Interstellar medium, Wolf-Rayet stars
Time-domain Deep-learning Filtering of Structured Atmospheric Noise for Ground-based Millimeter Astronomy. The complex physics involved in atmospheric turbulence makes it very difficult for ground-based astronomy to build accurate scintillation models and develop efficient methodologies to remove this highly structured noise from valuable astronomical observations. We argue that a deep-learning approach can bring a significant advance to treat this problem because of deep neural networks' inherent ability to abstract nonlinear patterns over a broad scale range. We propose an architecture composed of long short-term memory cells and an incremental training strategy inspired by transfer and curriculum learning. We develop a scintillation model and employ an empirical method to generate a vast catalog of atmospheric-noise realizations and train the network with representative data. We face two complexity axes: the signal-to-noise ratio (S/N) and the degree of structure in the noise. Hence, we train our recurrent network to recognize simulated astrophysical pointlike sources embedded in three structured-noise levels, with a raw-data S/N ranging from 3 to 0.1. We find that a slow and repetitive increase in complexity is crucial during training to obtain a robust and stable learning rate that can transfer information through different data contexts. We probe our recurrent model with synthetic observational data, designing alongside a calibration methodology for flux measurements. Furthermore, we implement traditional matched filtering (MF) to compare its performance with our neural network, finding that our final trained network can successfully clean structured noise and significantly enhance the S/N compared to raw data and in a more robust way than traditional MF.

Keywords: Astronomy data modeling, Neural networks, Millimeter astronomy, Diffuse radiation, Astrostatistics techniques, Atmospheric scintillation, Humidity, Red noise, Time series analysis
Predicting Solar Energetic Particles Using SDO/HMI Vector Magnetic Data Products and a Bidirectional LSTM Network. Solar energetic particles (SEPs) are an essential source of space radiation, and are hazardous for humans in space, spacecraft, and technology in general. In this paper, we propose a deep-learning method, specifically a bidirectional long short-term memory (biLSTM) network, to predict if an active region (AR) would produce an SEP event given that (i) the AR will produce an M- or X-class flare and a coronal mass ejection (CME) associated with the flare, or (ii) the AR will produce an M- or X-class flare regardless of whether or not the flare is associated with a CME. The data samples used in this study are collected from the Geostationary Operational Environmental Satellite's X-ray flare catalogs provided by the National Centers for Environmental Information. We select M- and X-class flares with identified ARs in the catalogs for the period between 2010 and 2021, and find the associations of flares, CMEs, and SEPs in the Space Weather Database of Notifications, Knowledge, Information during the same period. Each data sample contains physical parameters collected from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Experimental results based on different performance metrics demonstrate that the proposed biLSTM network is better than related machine-learning algorithms for the two SEP prediction tasks studied here. We also discuss extensions of our approach for probabilistic forecasting and calibration with empirical evaluation.

Keywords: Solar energetic particles, Solar activity, Solar flares, Solar coronal mass ejections
Validations and Corrections of the SFD and Planck Reddening Maps Based on LAMOST and Gaia Data. Precise correction of dust reddening is fundamental to obtain the intrinsic parameters of celestial objects. The Schlegel et al. (SFD) and the Planck 2D extinction maps are widely used for reddening correction. In this work, using accurate reddening determinations of about 2 million stars from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Data Release 5 and Gaia DR2, we check and calibrate the SFD and Planck maps in the middle and high Galactic latitudes. The maps show similar precision in reddening correction. We find small yet significant spatially dependent biases for the four maps, which are similar between the SFD and Planck2014-R maps, and between the Planck2014-Tau and Planck2019-Tau maps. The biases show a clear dependence on the dust temperature and extinction for the SFD and Planck2014-R maps. While those of the Planck2014-Tau and Planck2019-Tau maps have a weak dependence on the dust temperature, they both strongly depend on the dust spectral index. Finally, we present corrections of the SFD and Planck extinction maps within the LAMOST footprint, along with empirical relations for corrections outside the LAMOST footprint. Our results provide important clues for the further improvement of the Galactic all-sky extinction maps and lay a significant foundation for accurate extinction correction in the era of precision astronomy.

Keywords: Galaxy stellar content, Interstellar dust extinction
Target-of-opportunity Observations of Gravitational-wave Events with Vera C. Rubin Observatory. The discovery of the electromagnetic counterpart to the binary neutron star (NS) merger GW170817 has opened the era of gravitational-wave multimessenger astronomy. Rapid identification of the optical/infrared kilonova enabled a precise localization of the source, which paved the way to deep multiwavelength follow-up and its myriad of related science results. Fully exploiting this new territory of exploration requires the acquisition of electromagnetic data from samples of NS mergers and other gravitational-wave sources. After GW170817, the frontier is now to map the diversity of kilonova properties and provide more stringent constraints on the Hubble constant, and enable new tests of fundamental physics. The Vera C. Rubin Observatory's Legacy Survey of Space and Time can play a key role in this field in the 2020s, when an improved network of gravitational-wave detectors is expected to reach a sensitivity that will enable the discovery of a high rate of merger events involving NSs (~tens per year) out to distances of several hundred megaparsecs. We design comprehensive target-of-opportunity observing strategies for follow-up of gravitational-wave triggers that will make the Rubin Observatory the premier instrument for discovery and early characterization of NS and other compact-object mergers, and yet unknown classes of gravitational-wave events.

Keywords: Neutron stars, Black holes, Gravitational wave sources, Transient detection, Astronomical methods