Sazuna/UAT_keywords huggingface dataset:
Galaxy Morphological Classification with Zernike Moments and Machine Learning Approaches. Classifying galaxies is an essential step for studying their structures and dynamics. Using GalaxyZoo2 (GZ2) fractions thresholds, we collect 545 and 11,735 samples in nongalaxy and galaxy classes, respectively. We compute the Zernike moments (ZMs) for GZ2 images, extracting unique and independent characteristics of galaxies. The uniqueness due to the orthogonality and completeness of Zernike polynomials, reconstruction of the original images with minimum errors, invariances (rotation, translation, and scaling), different block structures, and discriminant decision boundaries of ZMs' probability density functions for different order numbers indicate the capability of ZMs in describing galaxy features. We classify the GZ2 samples, first into the galaxies and nongalaxies and second, galaxies into spiral, elliptical, and odd objects (e.g., ring, lens, disturbed, irregular, merger, and dust lane). The two models include the support vector machine (SVM) and 1D convolutional neural network (1D-CNN), which use ZMs, compared with the other three classification models of 2D-CNN, ResNet50, and VGG16 that apply the features from original images. We find the true skill statistic (TSS) greater than 0.86 for the SVM and 1D-CNN with ZMs for the oversampled galaxy–nongalaxy classifier. The SVM with ZMs model has a high-performance classification for galaxy and nongalaxy data sets. We show that the SVM with ZMs, 1D-CNN with ZMs, and VGG16 with vision transformer are high-performance (accuracy larger than 0.90 and TSS greater than 0.86) models for classifying the galaxies into spiral, elliptical, and odd objects. We conclude that these machine learning algorithms are helpful tools for classifying galaxy images. The Python notebooks are available on GitHub at https://github.com/hmddev1/machine_learning_for_morphological_galaxy_classification.

Keywords: Galaxy structure, Elliptical galaxies, Galaxy classification systems, Spiral galaxies
Fourier and Wavelet Analysis of Coronal Mass Ejections during Solar Cycles 23–25: Angular Widths and Latitudinal Distribution. Coronal mass ejections (CMEs), the large-scale eruptions of plasma and magnetic fields from the solar corona, play a critical role in space weather and solar–terrestrial interactions. Understanding the periodicities of CMEs is essential for predicting their occurrences and impacts, but their quasiperiodic variations in different cycles, at different latitudes, and for different angular widths, are still unclear. In the present work, we classify CMEs based on their angular widths and latitudinal distribution, using data from the version 2.0 of the Coordinated Data Analysis Workshop catalog (1996 January–2024 March). Employing Fourier and wavelet transforms, we systematically analyze the quasiperiodic variations in the occurrence rates of CMEs. The main results show the following: (1) A lesser number of periodicities is found in high-latitude CMEs while the low-latitude CMEs show a greater number of periodicities; (2) for the periodic variations, significant differences exist between different types of CMEs across latitudes. Narrow and normal CMEs exhibit similar periodicities, whereas halo CMEs show a different periodic pattern. (3) Additional periodicities were identified for all CMEs, which occur exclusively at low latitudes or during specific cycles, potentially as a result of the noticeable heliospheric dynamic pressure after the maximum time of cycle 23. (4) The identified dominant periodicities may be associated with magneto-Rossby waves at the tachocline. This mechanism can effectively explain the periodic behavior of the observed CMEs. Our analysis results might be useful for better understanding the long-term variations of CMEs during different cycles and at different latitudes, and could also have implications for the geoeffectiveness predictions of CMEs.

Keywords: Solar activity, Solar magnetic fields, Solar coronal mass ejections
Galaxy─Galaxy Strong Lensing with U-Net (GGSL-UNet). I. Extracting Two-dimensional Information from Multiband Images in Ground and Space Observations. We present a novel deep learning method to separately extract the two-dimensional flux information of the foreground galaxy (deflector) and background system (source) of galaxy─galaxy strong-lensing events using U-Net (GGSL-UNet for short). In particular, the segmentation of the source image is found to enhance the performance of the lens modeling, especially for ground-based images. By combining mock lens foreground+background components with real sky survey noise to train GGSL-UNet, we show it can correctly model the input image noise and extract the lens signal. However, the most important result of this work is that GGSL-UNet can accurately reconstruct real ground-based lensing systems from the Kilo-degree Survey in 1 s. We also test GGSL-UNet on space-based lenses from BELLS GALLERY, and obtain comparable accuracy to standard lens-modeling tools. Finally, we calculate the magnitudes from the reconstructed deflector and source images and use these to derive photometric redshifts (photo-z), with the photo-z of the deflector well consistent with the spectroscopic ones. This first work demonstrates the great potential of the generative network for lens finding, image denoising, source segmentation, and decomposing and modeling of strong-lensing systems. For upcoming ground- and space-based surveys, GGSL-UNet can provide high-quality images as well as geometry and redshift information for precise lens modeling, in combination with classical Markov Chain Monte Carlo modeling for the best accuracy in galaxy─galaxy strong-lensing analysis.

Keywords: Strong gravitational lensing, Astronomy image processing
PyEMILI: A New Generation Computer-aided Spectral Line Identifier. Deep high-dispersion spectroscopy of Galactic photoionized gaseous nebulae, mainly planetary nebulae and H II regions, has revealed numerous emission lines. As a key step of spectral analysis, identification of emission lines hitherto has mostly been done manually, which is a tedious task, given that each line needs to be carefully checked against huge volumes of atomic transition/spectroscopic data to reach a reliable assignment of identity. Using PYTHON, we have developed a line-identification code PyEMILI, which is a significant improvement over the FORTRAN-based package EMILI introduced ∼20 yr ago. In our new code PyEMILI, the major shortcomings in EMILI's line-identification technique have been amended. Moreover, the atomic transition database utilized by PyEMILI was adopted from Atomic Line List v3.00b4 but greatly supplemented with theoretical transition data from the literature. The effective recombination coefficients of the C II, O II, N II, and Ne II nebular lines are collected from the literature to form a subset of the atomic transition database to aid identification of faint optical recombination lines in the spectra of planetary nebulae (PNe) and H II regions. PyEMILI is tested using the deep, high-dispersion spectra of two Galactic PNe, Hf 2-2 and IC 418, and gives better results of line identification than EMILI does. We also ran PyEMILI on the optical spectrum of a late-type [WC11] star UVQS J060819.93-715737.4 recently discovered in the Large Magellanic Cloud, and our results agree well with the previous manual identifications. The new identifier PyEMILI is applicable to not only emission-line nebulae but also to emission stars, such as Wolf–Rayet stars.

Keywords: Spectral line lists, Astronomy software, Atomic physics, Spectral line identification, Gaseous nebulae, Planetary nebulae, Spectroscopy, H II regions
Chemistry of NH<SUB>2</SUB>CH<SUB>2</SUB>CH<SUB>2</SUB>OH and Its Related Species in the Interstellar Medium. Ethanolamine (NH2CH2CH2OH) can form not only glycine directly under the conditions of proposed Archean alkaline hydrothermal vents, a possible environment for the origin of life, but also the polar hydrophilic head of phosphatidylethanolamine, the second most abundant phospholipid in cell membranes, under possible conditions of the primitive Earth. Recently, NH2CH2CH2OH was detected toward the G+0.693-0.027 molecular cloud. We construct the chemical network for NH2CH2CH2OH and its related species (HOCH2CN, CH3NH2, NH2OH, CH3OH, C2H5OH, and C2H3OH) via quantum chemical calculations, simulate their abundance evolutions with the pnautilus code, and then acquire the best-fitting shock-wave model for G+0.693: the combination of isothermal model I5 (Tg = Td = 16 K, nH = 4 × 103 cm‑3, AV = 6 mag, and ζ = 1.3 × 10‑15 s‑1) and continuous shock model S5 (Vs = 20 km s‑1). We find NH2CH2CH2OH mainly comes from the thermal desorption produced by shock-induced heating and the photodesorption generated by cosmic-ray-induced UV photons; overall, its ice-phase species is mostly produced by four sequential ice-phase addition pathways, in which the final reactions are J-H + J-NH2CH2CHOH → J-NH2CH2CH2OH, J-H + J-NHCH2CH2OH → J-NH2CH2CH2OH, J-H + J-NH2CH2CH2O → J-NH2CH2CH2OH, and J-CH2OH + J-CH2NH2 → J-NH2CH2CH2OH. We also discuss the formation of HOCH2CN, CH3OH, C2H5OH, and C2H3OH in the best-fitting shock-wave model, as well as the formation of NH2CH2CH2OH in a typical hot-core model. Moreover, we predict NH2CH2CH2OH may be detected toward the newly formed hot core; the undiscovered species CH2CH2OH and HOCCNH are potentially detectable toward G+0.693.

Keywords: Interstellar molecules, Theoretical models, Interstellar abundances, Interstellar medium, Pre-biotic astrochemistry
Half a Million Binary Stars Identified from the Low-resolution Spectra of LAMOST. Binary stars are prevalent yet challenging to detect. We present a novel approach using convolutional neural networks (CNNs) to identify binary stars from low-resolution spectra obtained by the LAMOST survey. The CNN is trained on a data set that distinguishes binaries from single main-sequence stars based on their positions on the Hertzsprung─Russell diagram. Specifically, the training data labels stars with mass ratios between approximately 0.71 and 0.93 as intermediate-mass-ratio binaries, while excluding those beyond this range. The network achieves high accuracy with an area under the receiver operating characteristic curve of 0.949 on the test set. Its performance is further validated against known eclipsing binaries (97% detection rate) and binary stars identified by radial velocity variations (92% detection rate). Applying the trained CNN to a sample of one million main-sequence stars from LAMOST DR10 and Gaia DR3 yields a catalog of 468,634 binary stars, which are mainly intermediate-mass-ratio binaries given the training data. This catalog includes 115 binary stars located beyond 10 kpc from the Sun and 128 crossmatched with known exoplanet hosts from the NASA Exoplanet Archive. This new catalog provides a valuable resource for future research on the properties, formation, and evolution of binary systems, particularly for statistically characterizing large populations.

Keywords: Main sequence stars, Convolutional neural networks, Binary stars
A Survey of General Relativistic Magnetohydrodynamic Models for Black Hole Accretion Systems. General relativistic magnetohydrodynamics (GRMHD) simulations are an indispensable tool in studying accretion onto compact objects. The Event Horizon Telescope (EHT) frequently uses libraries of ideal GRMHD simulations to interpret polarimetric, event-horizon-scale observations of supermassive black holes at the centers of galaxies. In this work, we present a library of 10 nonradiative, ideal GRMHD simulations that were utilized by the EHT Collaboration in their analysis of Sagittarius A*. The parameter survey explores both low (SANE) and high (MAD) magnetization states across five black hole spins a* = ‑15/16, ‑1/2, 0, +1/2, +15/16 where each simulation was run out to 30,000 GM/c‑3. We find the angular momentum and energy flux in SANE simulations closely matches the thin-disk value, with minor deviations in prograde models due to fluid forces. This leads to spin equilibrium around a* ∼ 0.94, consistent with previous studies. We study the flow of conserved quantities in our simulations and find mass, angular momentum, and energy transport in SANE accretion flows to be primarily inward and fluid dominated. MAD models produce powerful jets with outflow efficiency &gt;1 for a* = + 0.94, leading to black hole spin-down in prograde cases. We observe outward directed energy and angular momentum fluxes on the horizon, as expected for the Blandford–Znajek mechanism. MAD accretion flows are sub-Keplerian and exhibit greater variability than their SANE counterpart. They are also hotter than SANE disks within r ≲ 10 GM/c‑2. This study is accompanied by a public release of simulation data at http://thz.astro.illinois.edu/.

Keywords: Accretion, Supermassive black holes, Low-luminosity active galactic nuclei, Relativistic fluid dynamics, Magnetohydrodynamical simulations
Production and Loss Processes of Hydrogen Energetic Neutral Atoms in the Heliosphere from 5 eV–500 keV. Energetic neutral atom (ENA) observations provide valuable insights into the plasma conditions in the heliosphere and the surrounding interstellar medium. Unlike plasma detectors, which measure charged particles tied to the magnetic fields at their location, ENA detectors capture former ions that were neutralized in distant regions and traverse the heliosphere in straight trajectories. ENA fluxes near the Sun represent line-of-sight integrals of parent ion fluxes multiplied by neutralization (production) rates and reduced by the probability of ENA reionization (loss) processes. So far, most ENA analyses have focused on charge exchange between hydrogen atoms and protons as the primary source of ENAs. Here, we examine various ENA production and loss processes throughout the heliosphere in the broad energy range (5 eV to 500 keV) encompassing the next-generation ENA instruments aboard the Interstellar Mapping and Acceleration Probe mission. Our study considers binary collisions involving the most abundant species: protons, electrons, α-particles, He+ ions, photons, as well as hydrogen and helium atoms. Our findings indicate that, in addition to ENAs produced by charge exchange of energetic protons with hydrogen atoms, a significant portion of high-energy ENAs originate from the charge exchange with helium atoms. Below 10 keV, the dominant ENA loss processes are charge exchange collisions with protons and photoionization. However, stripping ionization processes, e.g., from collisions with ambient interstellar neutral hydrogen, become the main loss mechanism for higher energies because the charge exchange rate rapidly decreases.

Keywords: Photoionization, Recombination, Heliosphere, Charge transfer, Electron impact ionization, Collision physics, Ionization, Charge exchange ionization, Heliosheath, Charge exchange recombination
Optical Monitoring and Long-term Optical Spectral Variability of BL Lacertae Object S5 0716+714. We present photometric observations of the BL Lacertae object S5 0716+714 with a temporal resolution of 120 s in the Sloan   and   bands. These observations were conducted using the Comet Search Program telescope at Xingming Observatory from 2018 December 22 to 2020 February 15, and more than 5600 effective images were obtained on each filter across 79 nights. Additionally, we compiled long-term variability data spanning 34 yr in the optical UBVRI bands. Using the power-enhanced F-test and nested ANOVA test, we found intraday variability (IDV) on 31 nights and possible IDV on 20 nights in the i' band. Similarly, IDV was detected on 35 nights in the r' band, while possible IDV was observed on 22 nights. The minimum variability timescale is 7.33 ± 0.74 minutes, and the estimated black hole masses are (0.68 ∼ 5.12) × 108M⊙. The spectral variability and long-term optical light curves reveal a bluer-when-brighter trend on intraday timescales. The long-term optical flux density and spectral index exhibit periodic variability with a timescale of about 1038 days. An anticorrelation between optical flux and spectral index was observed, with a time delay of −140 days. Variability across different optical bands exhibited a strong correlation, with no discernible time lag. From the IDV, spectral variability, correlation, and time delays between different bands, we conclude that these radiation characteristics may result from the shock-in-jet model scenario.

Keywords: Blazars
The Stellar Abundances and Galactic Evolution Survey (SAGES). II. Machine Learning–based Stellar Parameters for 21 Million Stars from the First Data Release. Stellar parameters for large samples of stars play a crucial role in constraining the nature of stars and stellar populations in the Galaxy. An increasing number of medium-band photometric surveys are presently used in estimating stellar parameters. In this study, we present a machine learning approach to derive estimates of stellar parameters, including [Fe/H], log g, and Teff, based on a combination of medium-band and broadband photometric observations. Our analysis employs data primarily sourced from the Stellar Abundances and Galactic Evolution Survey (SAGES), which aims to observe much of the Northern Hemisphere. We combine the uv-band data from SAGES DR1 with photometric and astrometric data from Gaia EDR3, and apply the random forest method to estimate stellar parameters for approximately 21 million stars. We are able to obtain precisions of 0.09 dex for [Fe/H], 0.12 dex for log g, and 70 K for Teff. Furthermore, by incorporating Two Micron All Sky Survey and Wide-field Infrared Survey Explorer infrared photometric and Galaxy Evolution Explorer ultraviolet data, we are able to achieve even higher precision estimates for over 2.2 million stars. These results are applicable to both giant and dwarf stars. Building upon this mapping, we construct a foundational data set for research on metal-poor stars, the structure of the Milky Way, and beyond. With the forthcoming release of additional bands from SAGES such DDO51 and Hα, this versatile machine learning approach is poised to play an important role in upcoming surveys featuring expanded filter sets.

Keywords: Fundamental parameters of stars, Stellar abundances, Astronomy data analysis, Photometry
AstroSat UVIT Survey of M31: New Compact Source Catalog. An ultraviolet survey of M31 was carried out during 2017─2023 with the UltraViolet Imaging Telescope (UVIT) instrument on board the AstroSat Observatory. Here we present far- and near-ultraviolet (FUV and NUV) observations from the M31 UVIT survey, which covers a sky area of ≃3 5 × 1 3 with a spatial resolution of ≃1″. The observations included six filter bands in the wavelength range of 120─280 nm. Including the six bands, ≃115,000 sources with signal-to-noise ratio (S/N) ≥ 3 (≃95,000 sources with S/N ≥ 5) were detected at FUV or NUV wavelengths, with the largest set of detections (≃54,000 sources) in the FUV 150 nm band (F148W filter). This is considerably more than for the first version of the M31 source catalog (published in 2020), in part due to additional observations of M31 by UVIT and in part due to improved data processing. The magnitude (mAB) at which incompleteness sets in is ≃23.0 in the F148W band, with the other bands somewhat less sensitive, with least sensitive band (in mAB units) N279N with incompleteness for sources fainter than ≃20.3). The faintest sources detectable in F148W have mAB ≃ 25.4, with other bands having higher minimum detectable brightness, and with N279N having a minimum detectable limit of mAB ≃ 22.1. The product of this work is the new M31 UVIT compact-source catalog, containing positions, fluxes, magnitudes, and S/N for the sources.

Keywords: Ultraviolet astronomy, Andromeda Galaxy, Ultraviolet photometry
Spectuner: A Framework for Automated Line Identification of Interstellar Molecules. Interstellar molecules, which play an important role in astrochemistry, are identified using observed spectral lines. Despite the advent of spectral analysis tools in the past decade, the identification of spectral lines remains a tedious task that requires extensive manual intervention, preventing us from fully exploiting the vast amounts of data generated by large facilities such as the Atacama Large Millimeter/submillimeter Array. This study aims to address the aforementioned issue by developing a framework for automated line identification. We introduce a robust spectral fitting technique applicable to spectral line identification with minimal human supervision. Our method is assessed using published data from five line surveys of hot cores, including W51, Orion-KL, Sgr B2(M), and Sgr B2(N). By comparing the identified lines, our algorithm achieves an overall recall of ∼74%–93%, and an average precision of ∼78%–92%. Our code, named SPECTUNER, is publicly available on GitHub.

Keywords: Spectral line identification, Interstellar medium
Cross-survey Image Transformation: Enhancing SDSS and DECaLS Images to Near-HSC Quality for Advanced Astronomical Analysis. This study focuses on transforming galaxy images between astronomical surveys, specifically enhancing images from the Sloan Digital Sky Survey (SDSS) and the Dark Energy Camera Legacy Survey (DECaLS) to achieve quality comparable to the Hyper Suprime-Cam (HSC) Survey. We proposed a hybrid model called Pix2WGAN, which integrates the pix2pix framework with the Wasserstein generative adversarial network with gradient penalty to convert low-quality observational images into high-quality counterparts. Our model successfully transformed DECaLS images into pseudo-HSC images, yielding impressive results and significantly enhancing the identification of complex structures, such as galaxy spiral arms and tidal tails, which may have been overlooked in the original DECaLS images. Moreover, Pix2WGAN effectively addresses issues like artifacts, noise, and blurriness in both source and target images. In addition to the basic Pix2WGAN model, we further developed an advanced architecture called Cascaded Pix2WGAN, which incorporates a multistage training mechanism designed to bridge the quality gap between SDSS and HSC images, demonstrating similarly promising outcomes. We systematically assessed the similarity between the model-generated pseudo-HSC images and actual HSC images using various metrics, including root mean squared error, peak signal-to-noise ratio, and structural similarity index, along with perceptual metrics such as learned perceptual image patch similarity and Fréchet inception distance. The results indicate that images transformed by our model outperform both the original SDSS and DECaLS images across nearly all evaluation metrics. Our research is expected to provide significant technical support for astronomical data analysis, cross-survey image integration, and high-precision astrometry.

Keywords: Computational astronomy, Astronomy data modeling, Convolutional neural networks, Astronomy data analysis, Ground-based astronomy, Astronomy data visualization, Galaxies