Sazuna/UAT_keywords huggingface dataset:
Evaluating Time-series Augmentation Techniques for Deep Learning–Based Solar Flare Prediction. Accurate forecasting of solar flares is crucial for mitigating their severe impacts on space-based and communication systems. Deep learning models have shown promise in predicting flare activity using magnetic field measurements of solar active regions. However, the scarcity and imbalance of flare occurrence data—particularly for major flares—pose significant challenges to model robustness and generalization. This study provides a comprehensive evaluation of time-series data augmentation techniques to improve deep learning-based solar flare prediction. Using the benchmark Space Weather Analytics for Solar Flares data set, which offers multivariate magnetic field parameter time series, we assess 12 augmentation methods across three architectures: fully convolutional network (FCN), multivariate LSTM-FCN, and residual network. Our primary experiments address the binary classification of major (M- and X-class) versus minor (C-, B-, and FQ-class) flares, using metrics tailored for imbalanced data, including recall, true skill statistic, Heidke skill score, and the Gini coefficient. A case study on X versus M classification further examines performance under a simpler, more balanced setting. Results show that select augmentation strategies yield measurable gains across different models and scenarios, offering a viable path forward for addressing data scarcity in space weather forecasting tasks.

Keywords: Solar flares, Magnetic fields, Neural networks
DarkAI: Reconstructing the Density, Velocity, and Tidal Fields of Dark Matter from a DESI-like Bright Galaxy Sample. Reconstructing the mass density, velocity, and tidal (MTV) fields of dark matter from galaxy surveys is essential for advancing our understanding of the large-scale structure of the Universe. In this work, we present a machine learning–based framework using a UNet convolutional neural network to reconstruct the MTV fields from mock samples of the DESI bright galaxy survey within the redshift range 0.1 &lt; z &lt; 0.4. Our approach accounts for realistic observational effects, including geometric selection, flux-limited data, and redshift-space distortion (RSD) effects, thereby improving the fidelity of the reconstructed fields. Testing on mock galaxy catalogs generated from the Jiutian N-body simulation, our method achieves a significant accuracy level. The reconstructed density field exhibits strong consistency with the true field, effectively eliminating most RSD effects and achieving a cross-correlation power spectrum coefficient greater than 0.985 on scales with k &lt; 0.1 h Mpc‑1. The velocity field reconstruction accurately captures large-scale coherent flows and small-scale turbulent features, exhibiting slopes of grid-to-grid relationships close to unity and scatter below ∼100 km s‑1. Additionally, the tidal field is reconstructed without bias, successfully recovering the features of the large-scale cosmic web, including clusters, filaments, sheets, and voids. Our results confirm that the proposed framework effectively captures the large-scale distribution and dynamics of dark matter while addressing key systematic challenges. These advancements provide a reliable and robust tool for analyzing current and future galaxy surveys, paving the way for new insights into cosmic structure formation and evolution.

Keywords: Dark matter distribution, Large-scale structure of the universe
Solar Flare Forecasting Using Machine Learning and SDO/HMI Data: A Multiple Machine Learning Model and Data Curation Technique Comparison Study. Forecasting solar flares is essential to mitigate their harmful effects on our technologies. This paper describes an in-depth study of solar flare forecasting using machine learning (ML) models. These ML models utilize active region patch parameters extracted from the Solar Dynamics Observatory Helioseismic and Magnetic Imager (HMI) data; specifically, Space-weather HMI Active Region Patch (SHARP) parameters. Six models are considered: k-nearest neighbors, linear discriminant analysis, logistic regression, quadratic discriminant analysis, random forest classifier, and support vector machine. A detailed analysis of hyperparameter selection for each of these models is included in the study. The importance of each SHARP parameter in model performance is also examined. A major component of this work examines the incorporation of active region flaring history into the forecasting process. This examination indicates that this incorporation substantively improves forecasting accuracy. Another major contribution of the work here is a consideration of the effects of multiple data curation methods on the predictive performance of the models. The results presented here indicate that using high-quality data can markedly improve predictive accuracy. Furthermore, this work demonstrates that using SHARPs that contain more than one active region limits the performance of these models. The performance of models with varying lead times is also explored, providing insights into the choice of the best prediction windows.

Keywords: Random Forests, Space weather, Support vector machine, Classification
MaNGA DynPop. VII. A Unified Bulge–Disk–Halo Model for Explaining Diversity in Circular Velocity Curves of 6000 Spiral and Early-type Galaxies. We derive circular velocity curves (CVCs) from stellar dynamical models for ∼6000 nearby galaxies in the final data release of the Sloan Digital Sky Survey-IV MaNGA survey with integral-field spectroscopy, exploring connections between the inner gravitational potential (traced by CVC amplitude/shape) and galaxy properties. The maximum circular velocity ( Vcircmax ) and circular velocity at the half-light radius ( Vcirc(Remaj) ) both scale linearly with the stellar second velocity moment  σe2≡&lt;V2+σ2&gt;  within the half-light isophote, following  Vcircmax≈1.72σe  (7% error) and  Vcirc(Remaj)≈1.62σe  (7% error). CVC shapes (rising, flat, declining) correlate strongly with structural and stellar population properties: declining curves dominate in massive, early-type, bulge-dominated galaxies with old, metal-rich stars and early quenching, while rising CVCs prevail in disk-dominated systems with younger stellar populations and ongoing star formation. Using a unified bulge–disk–halo model, we predict CVC shapes with minimal bias, identifying three governing parameters: bulge-to-total mass ratio (B/T), dark matter fraction within Re, and bulge Sérsic index. The distribution of CVC shapes across the mass–size plane reflects evolutionary pathways driven by (i) in situ star formation (spurring bulge growth) and (ii) dry mergers. This establishes CVC morphology as a diagnostic for galaxy evolution, linking dynamical signatures to structural and stellar population histories.

Keywords: Galaxy formation, Galaxy structure, Galaxy dynamics, Galaxy mass distribution, Galaxy evolution
An Improved Atlas of Full-scan Spectra from ISO/SWS. We present an atlas of full-scan spectra from the Short-Wavelength Spectrometer (SWS) on board the Infrared Space Observatory after reprocessing and improving an earlier version published 22 yr ago. The SWS spectra cover the wavelength range from 2.35 to 45.3 μm. They include scans in 12 separate bands, and we have updated the methods used to combine those bands into a single continuous spectrum. The main improvement comes from applying multiple constraints, including new photometry and spectra from the Infrared Spectrograph on the Spitzer Space Telescope that have become available since the release of the original products, and individualized attention to each spectrum, to renormalize the separate bands into a more consistent single spectrum. In particular, this removed unphysical negative fluxes that were common in the original data products. The new database, with 1035 reprocessed spectra, will be available to the community at NASA's Infrared Science Archive, which also hosts the original processing.

Keywords: Astronomy databases, Infrared spectroscopy
The CLASS Quasar Catalog: Coronal Line Activity in Type 1 SDSS Quasars. We conduct the first systematic survey of a total of 11 optical coronal lines (CLs) in the spectra of a large sample of low-redshift (z &lt; 0.8) Type 1 quasars observed by the Sloan Digital Sky Survey (SDSS). We find that strong CL emission is rare in SDSS even in Type 1 quasars; only 885 out of 19,508 (4.5%) galaxies show at least one CL, with higher ionization potential lines (&gt;100 eV) being even rarer. The [Ne V] λ3426 line, which constitutes the majority of detections, is strongly correlated with the bolometric luminosity. These findings suggest that the optical CLs are significantly suppressed in the majority of local active galactic nuclei (AGNs), possibly as a result of the presence of dust in the emitting regions. We find that the incidence of ionized outflows is significantly higher in CL emitters compared with non-CL emitters, possibly suggesting that dust destruction in outflows enhances CL emission in AGNs. Many CLs show line profiles that are broader than those of narrow lines, and are blueshifted relative to the lower ionization potential lines, suggesting outflows in the highly ionized gas. Given the limited number of detections, we do not find any statistically significant trends in detection statistics or line ratios with black hole mass, Eddington ratio, or AGN bolometric luminosity. The catalog is publicly available and can provide a useful database of the CL properties of low-redshift quasars that can be compared to the growing number of high-z AGNs discovered by JWST.

Keywords: Active galaxies, Solar coronal lines, Active galactic nuclei, Galaxy evolution, Interstellar medium
Parkes Transient Events. II. Pulsar Single-pulses Database Containing Raw Data Segments. We have reprocessed single-pulse candidates from the first four years (1997─2001) of the Parkes Multibeam receiver system observations, creating a new Parkes transient database (PTD II) that contains 165,592 single pulses from 363 known pulsars. Unlike previous databases, PTD II preserves the critical raw data segments of each detected pulse, enabling detailed analyses of emission physics while maintaining a compact size of only 1.5 GB. The database employs an SQLITE3 structure organising pulsar metadata, observation files, and pulse events with their raw data stored in binary format. We provide processing tools for extracting and analyzing single-pulse data, enabling fluence fitting and statistical analysis. Our pulsars exhibit diverse fluence distributions, such as log-normal, Gaussian, and unimodal. Temporal analyses reveal significant evolution in emission characteristics of several pulsars, including event-rate variations spanning 2 orders of magnitude in PSR J1602─5100 and PSR J0942─5552. Beyond supporting fundamental pulsar emission studies, the database serves as a valuable training resource for developing new single-pulse detection algorithms in the presence of radio-frequency interference.

Keywords: Astronomy databases, Radio bursts, Radio pulsars, Radio transient sources
Discrete Fourier Transform and Minimum Variance Distortionless Response Beamforming Algorithms for Searching for Transients in Radio Interferometers that Experience Radio-frequency Interference. Searching for transient wireless signals like fast radio bursts (FRBs) in radio interferometer data can be accomplished with beamforming algorithms. Radio-frequency interference (RFI) can significantly hamper the search since it causes a loss of bandwidth that can reach 40% for a telescope like the Canadian Hydrogen Intensity Mapping Experiment. In this work, we propose a new beamforming algorithm that combines the discrete Fourier transform and the high-resolution adaptive minimum variance distortionless response beamformers for RFI-free and RFI-inflicted channels, respectively. Our method operates on the baseband data after the transient has been detected. We deploy the beamformer first to produce cleaner dynamic spectra of the FRBs, and second to localize them spatially under high-power RFI. Our simulation results show that even when a fraction of 20% of the channels are contaminated with RFI, we have an increase in localization accuracy, especially in the low signal-to-noise ratio regime.

Keywords: Radio receivers, Fast Fourier transform, Radio transient sources
Quasiperiodic Variations of the GOES Soft X-Ray Flares during Solar Cycles 21–25. The long-term variations of quasiperiodic solar flares are of great importance for a better understanding and accurate prediction of solar flare activity. To explore the quasiperiodic characteristics of different classes of solar flares and the hemispheric difference, we comprehensively analyzed the quasiperiodic variations of different classes of solar flares, including the whole solar disk and hemispheres during solar cycles (SC) 21–25. The main results show that: (1) Certain periods are observed only in one hemisphere or some specific solar cycles. For example, over the entire time interval, the period of 3.49 yr was detected exclusively in C-class, M-class, and X-class flares in the northern hemisphere. (2) The quasi-biennial oscillation periodicities were observed for B-class, C-class, M-class, and X-class flares during SC22, SC24, and SC25. Moreover, the quasi-biennial oscillation periodicities were more pronounced in C-class and X-class flares in SC21 and in B-class and M-class flares in SC23. (3) The occurrence of different classes of solar flares displayed statistically significant short- and medium-range oscillations in both hemispheres, with distinct periodic variations and asymmetric evolutionary features. Identifying features such as the 3.49 yr period and quasi-biennial oscillation periodicities may help predict the activity patterns of different classes of solar flares across hemispheres and solar cycles, potentially improving forecasts of solar flare impacts on Earth's technological infrastructure and space weather systems.

Keywords: Solar activity, Solar flares, Solar cycle
Orbits and Masses for 156 Companions from Combined Astrometry and Radial Velocities, and a Validation of Gaia Non-single-star Solutions. We combine absolute astrometry from Hipparcos and Gaia with archival radial velocities from the Keck/HIRES and ESO/HARPS spectrographs, as well as relative astrometry (when available), to derive masses and orbits for 156 companions around main-sequence stars, including 111 stellar companions, 12 brown dwarfs, and 33 planets. Although this sample is not compiled for occurrence rate statistics due to systematic biases in nonuniform target selection and varied observing strategies, we nonetheless clearly detect the brown dwarf desert in the distribution of companion masses (as well as in mass ratio), out to separations of ≳10 au. This work also enables a validation of Gaia DR3 non-single-star solutions by predicting Gaia's measured R.A. and decl. acceleration terms. For stars with Gaia astrometric acceleration solutions, we find qualitative agreement with Gaia DR3 results. Our predicted accelerations agree with the Gaia DR3 values overall, showing a median offset of 1.85σ, with a tail extending to ≍10σ. These residuals suggest modestly underestimated uncertainties, broadly consistent with previous results for parallaxes and proper motions. Three of our systems have full Gaia orbital fits; however, their true orbital periods are long, and all three Gaia solutions are spurious. Gaia DR4 will provide individual astrometric measurements and enable more detailed and extensive investigations of accelerating and orbital fits.

Keywords: Brown dwarfs, Exoplanet detection methods, Astrometric exoplanet detection, Orbits, Radial velocity, Orbit determination
A 13 Billion Year View of Galaxy Growth: Metallicity Gradient Evolution from the Local Universe to z = 9 with JWST and Archival Surveys. Galaxy gas-phase metallicity gradients have been extensively studied over the past four decades, both in the local and in the high-redshift Universe, as they trace the baryon cycle and growth of galaxies. With the unprecedented spatial resolution and sensitivity of JWST, it is now possible to measure metallicity and its radial gradients out to redshifts as high as z = 9. Here, we present a sample of 455 spectroscopically confirmed galaxies from redshifts 1.7 ≲ z ≲ 9 that are spatially resolved on subkiloparsec scales by deep JWST NIRCam or NIRISS Wide Field Slitless Spectroscopy. Synthesizing these new JWST observations with legacy observations from the literature, we observe that at redshift z &gt; 5, galaxy centers are more metal rich, exhibiting negative metallicity gradients of ∼−0.4 dex kpc−1. These gradients flatten over time, reaching near zero around z ≍ 2, coinciding with the peak of the cosmic star formation rate. Beyond this point, the gradients become negative again at lower redshifts approaching z = 0. This evolution likely reflects transitions in galaxy formation modes: an inside-out growth phase dominated by intense central star formation with inefficient feedback and limited gas mixing during "cosmic dawn," enhanced gas mixing due to feedback-driven wind and gas accretion at "cosmic noon," and a later phase of slow evolution and reduced feedback toward the present day. These physical processes, including gas accretion and feedback, not only regulate star and galaxy formation on a cosmic scale but also shape the evolutionary pathways of individual galaxies over cosmic time.

Keywords: Metallicity, Galaxy formation, Galaxy chemical evolution, Chemical enrichment, High-redshift galaxies
Toward a Robust R2D2 Paradigm for Radio-interferometric Imaging: Revisiting Deep Neural Network Training and Architecture. The R2D2 deep neural network (DNN) series was recently introduced for image formation in radio interferometry. It can be understood as a learned version of CLEAN, whose minor cycles are substituted with DNNs. We revisit R2D2 on the grounds of series convergence, training methodology, and DNN architecture, improving its robustness in terms of generalizability beyond training conditions, capability to deliver high data fidelity, and epistemic uncertainty. First, while still focusing on telescope-specific training, we enhance the learning process by randomizing Fourier sampling integration times; incorporating multiscan, multinoise configurations; and varying imaging settings, including pixel resolution and a visibility-weighting scheme. Second, we introduce a convergence criterion whereby the reconstruction process stops when the data residual is compatible with noise, rather than simply using all available DNNs. This not only increases the reconstruction efficiency by reducing its computational cost but also refines training by pruning out the data/image pairs for which optimal data fidelity is reached before training the next DNN. Third, we substitute R2D2's early U-Net DNN with a novel architecture (U-WDSR) combining U-Net and WDSR, which leverages wide activation, dense skip connections, weight normalization, and low-rank convolution to improve feature reuse and reconstruction precision. As previously, R2D2 was trained for monochromatic intensity imaging with the Very Large Array at fixed 512 × 512 image size. Simulations on a wide range of inverse problems and a case study on real data reveal that the new R2D2 model consistently outperforms its earlier version in image reconstruction quality, data fidelity, and epistemic uncertainty.

Keywords: Computational methods, Aperture synthesis, Astronomy image processing, Neural networks
Prediction of Intensity Variations Associated with Emerging Active Regions using Helioseismic Power Maps and Machine Learning. We developed recurrent neural networks using the long short-term memory (LSTM) architecture to predict the decrease in continuum intensity linked to the emergence of active regions (ARs), before they become visible on the solar surface and start forming sunspots. The goal of this study is to develop a machine learning (ML)-driven interpretable model to predict the starting time and location of emergence that later will be formed into a large AR. The model was trained on observations that included the emergence of 40 ARs and tested on five AR emergence events. The model training was based on observations of continuum intensity, unsigned magnetic flux, and oscillation power maps computed from Solar Dynamics Observatory Helioseismic and Magnetic Imager (HMI) data. For testing the predictive capabilities, the LSTM model uses a time series of the mean oscillation power calculated from Dopplergrams in four frequency ranges and the mean unsigned magnetic flux to predict the time and location of the decrease in the continuum intensity associated with the emerging ARs in a 12 hr time window. The results demonstrate that the LSTM ML analysis of the oscillation power maps can predict the emergence of large ARs several hours before their initial HMI continuum intensity signal becomes visible and at the time when the HMI magnetic flux is at 4%─9.6% of its eventual maximum value, therefore opening perspectives for further development of ML methodology for AR forecasting.

Keywords: Neural networks, Space weather, Sunspots, Solar active regions, Solar activity
Theoretical Diagnostics for the Physical Conditions in Active Galactic Nuclei under the View of JWST. With excellent spectral and angular resolutions and, especially, sensitivity, the JWST allows us to observe infrared emission lines that were previously inaccessible or barely accessible. These emission lines are promising for evaluating the physical conditions in different galaxies. Based on MAPPINGS V photoionization models, we systematically analyze the dependence of over 20 mid-infrared (mid-IR) emission lines covered by MIRI on board JWST on the physical conditions of different galactic environments, in particular narrow-line regions in active galactic nuclei (AGN). We find that mid-IR emission lines of highly ionized argon (i.e., [Ar V] 7.90 and 13.10 μm) and neon (i.e., [Ne V] 14.32 and 24.32 μm, and [Ne VI] 7.65 μm) are effective in diagnosing the physical conditions in AGN. We accordingly propose new prescriptions to constrain the ionization parameter (U), peak energy of the AGN spectrum (Epeak), metallicity ( 12+log(O/H) ), and gas pressure (P/k) in AGN. These new calibrations are applied to the central regions of six Seyfert galaxies included in the Galaxy Activity, Torus, and Outflow Survey as a proof of concept. We also discuss the similarity and difference in the calibrations of these diagnostics in AGN of different luminosities, highlighting the impact of hard X-ray emission and particularly radiative shocks, as well as the different diagnostics in star-forming regions. Finally, we propose diagnostic diagrams involving [Ar V] 7.90 μm and [Ne VI] 7.65 μm to demonstrate the feasibility of using the results of this study to distinguish galactic regions governed by different excitation sources.

Keywords: Interstellar line emission, Photoionization, Infrared spectroscopy, James Webb Space Telescope, Active galactic nuclei, Shocks