Sazuna/UAT_keywords huggingface dataset:
Stellar CoRGI in MaNGA: Stellar Counterrotating Galaxies Identified in the MaNGA Survey. Stellar counterrotating (CR) galaxies are systems hosting two large-scale stellar components rotating in opposite directions—a main, preexisting galaxy body with an older stellar population and a younger CR stellar disk likely formed from externally accreted gas. Such systems offer a unique opportunity to study disk assembly by analyzing the stellar populations of each component. Using integral field spectroscopic data from the SDSS-IV Mapping Nearby Galaxies at Apache Point Observatory survey, we identified a sample of 120 CR disk galaxies (65 reliable and 55 probable systems) by inspecting their kinematic maps and analyzing the shape of the stellar line-of-sight velocity distribution, which was recovered nonparametrically. Of these, 74 CR galaxies have not been reported in previous studies. For one-third of our sample, we further derived the ages and metallicities of stars for both disks via a spectral decomposition technique. We show that the observed spatial bimodality—where the CR disk either is concentrated in the central region (inner counterrotation) or dominates the outer part of the galaxy (outer counterrotation)—is driven by differences in the stellar mass and angular momentum of the CR disk. The wide range of stellar metallicities observed in CR disks suggests that no single source of external material is solely responsible for the formation of counterrotation in all galaxies; instead, proposed mechanisms such as merger with gas-rich satellites, accretion from cosmic filaments, and exchange of gas between neighboring galaxies can dominate in individual cases.

Keywords: Galaxy accretion, Galaxy kinematics, Galaxy stellar content, Disk galaxies
Search for the Optical Counterpart of Einstein Probe─discovered Fast X-Ray Transients from the Lulin Observatory. The launch of the Einstein probe (EP) mission has revolutionized the detection and follow-up observations of fast X-ray transients (FXTs) by providing prompt and timely access to their precise localizations. In the first year of its operation, the EP mission reported the discovery of 72 high signal-to-noise FXTs. Subjected to the visibility in the sky and weather conditions, we search for the optical counterparts of 42 EP-discovered FXTs from the Lulin Observatory. We successfully detected the optical counterparts of 12 FXTs, and five of those were first discovered by us from the Lulin Observatory. We find that the optical counterparts are generally faint (r &gt; 20 mag) and decline rapidly (&gt;0.5 mag day−1). We also find that 12 out of 42 FXTs show direct evidence of their association with gamma-ray bursts (GRBs) through significant temporal and spatial overlapping. Furthermore, the luminosities and redshifts of FXTs with confirmed optical counterparts in our observations are fully consistent with the faintest end of the GRB population. However, the nondetection of any associated optical counterpart with a significant fraction of FXTs suggests that EP FXTs are likely a subset of the so-called "dark FXTs," similar to "dark GRBs." Additionally, the luminosities of two FXTs with confirmed redshifts are also consistent with jetted tidal disruption events (TDEs). However, we find that the optical luminosities of FXTs differ significantly from typical supernova shock breakout or kilonova emissions. Thus, we conclude that a significant fraction of EP-discovered FXTs are associated with events having relativistic jets; either a GRB or a jetted TDE.

Keywords: Gamma-ray bursts, High energy astrophysics, Optical identification, Transient sources, X-ray transient sources
Species-dependent Variability in the Energy Spectra of Intense Solar Energetic Particle Events Observed by PSP/ISʘIS/EPI-Hi/LET. Solar energetic particle (SEP) events, comprising electrons, protons, and heavy ions with energies ranging from tens of keV nuc−1 to several GeV nuc−1, offer critical insights into solar particle acceleration and transport mechanisms. The Parker Solar Probe Integrated Science Investigation of the Sun High Energy Energetic Particle Instrument (EPI-Hi) has enabled in situ measurements of 16 ion species in the near-Sun environment, including both commonly studied ions (H, He, O, and Fe) and less frequently analyzed species (C, N, Ne, Na, Mg, Al, Si, S, Ar, Ca, Cr, and Ni) at multi─MeV nuc−1 energies. This study presents a comprehensive spectral and compositional analysis of intense SEP events observed by EPI-Hi's Low Energy Telescope (LET), incorporating newly derived species-specific geometric factors and instrument response variations. Energy spectra are characterized using Band function fits across different intensity phases of each event, accounting for changes in instrument mode, energy coverage, and detection efficiency. Relative contributions of each ion species during the SEP events are also examined. The results reveal a trend of increasing spectral indices with intensity during the 11 studied events, indicating that low-energy particle enhancements dominate during the high-intensity phase. By extending the analysis beyond the commonly studied ion species, this work provides a more complete picture of SEP composition and contributes to a deeper understanding of their source conditions and transport dynamics in the inner heliosphere. Finally, this paper and its appendices document new instrument response functions used for the EPI-Hi/LET public data release going forward.

Keywords: Solar energetic particles, Monte Carlo methods, Heliosphere
UNIONS Optical Identifications for VLASS Radio Sources in the Euclid Sky (UNVEIL). I. A Catalog of ∼146,000 Radio Galaxies up to z ∼ 5. We present the results of optical identifications for Very Large Array Sky Survey (VLASS) radio sources employing the Ultraviolet Near Infrared Optical Northern Survey (UNIONS). A crossmatch between UNIONS and VLASS Epoch 2 catalogs yields 146,212 radio galaxies over a wide area of ∼4200 deg2 in the Northern Hemisphere. We perform g-dropout selections and find ≳200 sources at z ∼ 4 optimistically. Of 63,019 sources with valid photometric redshifts, 8692 are at zphoto ≥ 1 and 1171 are at zphoto ≥ 2. Based on spectral luminosities at 1.4 and 3 GHz using the valid UNIONS photo-z, we identify ∼49,000 radio-loud active galactic nuclei (RLAGNs) with L1.4 GHz &gt; 1024 W Hz−1, and all radio galaxies at z ≥ 1 are radio-loud (RL). Adopting radio-loudness instead, 138,266 out of 146,212 UNIONS-VLASS radio galaxies are RL. By virtue of the wide area and medium depth provided by UNIONS, our catalog greatly increases the number counts of RLAGNs at z &gt; 1. We further crossmatch the UNIONS-VLASS catalog with the LOFAR Two-metre Sky Survey (LoTSS) at 144 MHz and VLA Faint Images of the Radio Sky at Twenty-cm (FIRST) at 1.4 GHz, yielding 101,671 UNIONS-VLASS-LoTSS, 79,638 UNIONS-VLASS-FIRST, and 64,672 UNIONS-VLASS-LoTSS-FIRST sources, respectively. This multifrequency radio data set reveals sources of various spectral shapes, including the steep spectrum of aged populations, the peaked spectrum of young populations, and the upturned spectrum that might be associated with transient sources. The UNIONS-VLASS radio galaxy candidates will be covered by the Euclid wide survey, bringing about legacy values to benefit multifaceted studies related to radio galaxies and beyond.

Keywords: Radio source catalogs, Radio galaxies, Radio active galactic nuclei, Spectral index, High-redshift galaxies
Fast Reconstruction of Solar Wind Magnetohydrodynamic Parameters at 0.1 au with Machine Learning. Accurately determining solar wind parameters is crucial for Sun─Earth space research, as they significantly affect spacecraft safety and ground-based power systems. Traditionally, solar wind conditions are derived using coupled coronal and heliospheric models, with the latter initialized by the former's output at 0.1 au, a computationally intensive and time-consuming process that limits real-time space weather forecasting. In this work, we propose a machine-learning-based method for generating solar wind parameters at 0.1 au. Specifically, we employ a U-Net neural network, trained using the output of the COolfluid COroNal UnsTructured (COCONUT) model as the learning target and Global Oscillation Network Group─Air Force Data Assimilative Photospheric Flux Transport magnetograms as input. The model achieves correlation coefficients of 0.992 for radial velocity, 0.987 for number density, and 0.991 for radial magnetic field on the test set, with derived Alfvén speed and dynamic pressure reaching 0.996 and 0.769, respectively, demonstrating strong capability in reconstructing key solar wind parameters. Moreover, the model effectively captures the temporal evolution of these parameters within a single Carrington rotation. Once trained, the model generates full-surface solar wind predictions at 0.1 au in 7.8 s on a CPU-only device and 0.065 s on a cluster with one GPU and 10 CPU cores, achieving 15× and 1800× speed-ups, respectively, over the COCONUT magnetohydrodynamic simulation, which requires at least 1 hr to obtain a converged steady-state solution and over 2 minutes on 288 CPU cores per prediction.

Keywords: Solar E corona, Space weather, Magnetohydrodynamics, Solar wind
Assessment of Daytime Sky Quality and Atmospheric Refractive Index (<inline-formula> <mml:math><mml:msubsup><mml:mrow><mml:mi>C</mml:mi></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math> </inline-formula>) Analysis at Pico dos Dias Observatory. Ground-based astronomical and solar observations are strongly influenced by atmospheric turbulence, a dynamic phenomenon that degrades optical resolution through refractive index fluctuations. This study evaluates daytime sky quality and the refractive index structure parameter ( Cn2 ) at the Pico dos Dias Observatory (OPD) in Brazil, integrating in situ meteorological data with the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 product. Variables such as air temperature, wind speed, relative humidity, and cloud cover are analyzed across vertical and temporal scales, providing insights into diurnal, seasonal, and altitudinal atmospheric behaviors. The study highlights the distinct challenges posed by daytime turbulence, driven by solar heating and convection, on adaptive optics (AO) systems and high-precision solar spectropolarimetric instruments, such as the prototype of the Galileo Solar Space Telescope. To provide context for the atmospheric conditions at the OPD, this study characterizes several key meteorological variables. Typical daytime conditions feature median air temperatures of 15°C─20°C and surface winds of 1.0─4.0 m s−1, predominantly from the north, while nighttime conditions are cooler (10°C─15°C) with weaker winds shifting south─southeast. Relative humidity generally remains below 70%, especially in the June─July─August months, making it favorable for astronomical observations. Cloud cover shows seasonality, with clear-sky fractions of about 32% at night and 28% by day, peaking in austral winter and minimizing in summer. Our quantitative analysis of refractive index variability underscores the necessity of advanced AO technologies. Seasonal and vertical profiles of  Cn2  present pronounced variability, influenced by solar heating, terrain-induced airflow, and jet stream dynamics. These findings stress the importance of strategic scheduling and turbulence modeling to optimize performance. This comprehensive characterization advances understanding of atmospheric conditions for solar and nighttime astronomy, refining AO systems, and enabling progress in high-precision astrophysics.

Keywords: Atmospheric effects
Super-size Me: The Big Multi-AGN Catalog (The Big MAC) Data Release 1: The Source Catalog. Galaxy mergers represent the most transformative and dramatic avenue for galaxy and supermassive black hole (SMBH) evolution. Multiple active galactic nuclei (multi-AGNs) are expected to ignite, grow, and evolve alongside the host galaxies, and these represent different evolutionary stages of the SMBHs over the merger sequence. However, no comprehensive census exists of observed multi-AGN systems. Here we present the Big Multi-AGN Catalog (the Big MAC), the first literature-complete catalog of all known (confirmed and candidate) multi-AGN systems, which includes dual AGNs (separations ∼0.03─110 kpc), binary AGNs (gravitationally bound, ≲30 pc), and recoiling AGNs, gleaned from hundreds of literature articles spanning the years 1970─2020. The Big MAC is the first archive to assemble all multi-AGN systems and candidates across all selection methods, redshifts, and galaxy mass ratios, and this catalog offers a solid foundation for archival and targeted multiwavelength follow-up investigations. In this work, we provide an overview of the creation of the multi-AGN literature library and the catalog itself, present definitions for different multi-AGN classes (including new definitions for dual AGNs derived from galaxy pairs in Illustris-TNG100), describe the general properties of the catalog as a function of redshift space and separation, and we provide a thorough examination of selection and confirmation method usage within the literature. We also discuss best practices for the multi-AGN literature, and we emphasize that a diverse, multiwavelength array of selection approaches is crucial for a complete understanding of multi-AGNs and—by extension—answering long-standing, open questions regarding the importance of AGNs and galaxy mergers.

Keywords: Interacting galaxies, Active galactic nuclei, Galaxy mergers
Smart Source Detection Framework for Lobster Eye Telescopes. Lobster eye telescopes are designed to capture X-ray emissions from celestial objects across a broad field of view. However, the unique optical system of these telescopes leads to a cruciform pattern on the detector plane, complicating the detection of celestial objects. While machine learning has driven the development of algorithms to address this, real applications still face challenges such as observation condition variations, often requiring manual adjustments of parameters in algorithms. Relying solely on 2D morphological features further restricts detection efficiency, as energy distribution and temporal variations are not considered. This paper introduces a novel source detection framework enhanced by deep neural networks, presenting three advancements: first, the use of a Swin Transformer to efficiently detect bright sources; second, a neural network to identify ordinary sources from a 3D data cube that integrates spatial and energy information; and third, an adaptive neural network branch within the 3D framework that automatically adjusts thresholds to classify true sources, accommodating observation condition variations. Applied to simulated data, our framework demonstrated a precision of over 95% and a recall rate exceeding 60% for targets brighter than 0.5 mCrab. Testing on 13,469 real observation images from the Wide-field X-ray Telescope on board the Einstein Probe resulted in an improvement in accuracy from 79% to 82% and a 27% increase in the number of detected sources compared to the current data-processing pipeline. The source detection framework and catalog obtained by the framework in a selected sky area are made accessible to the community for further applications.

Keywords: X-ray astronomy, Convolutional neural networks, X-ray surveys
Metallicities from High-resolution TRES Spectra with uberMS: Performance Benchmarks and Literature Comparison. As the field of exoplanetary astronomy has matured, demand has grown for precise stellar abundances to probe subtle correlations between stellar compositions and planetary demographics. However, drawing population-level conclusions from the disparate measurements in the literature is challenging, with various groups measuring metallicities using bespoke codes with differing line lists, radiative transfer models, and other assumptions. Homogeneous analyses are thus critical. Here we use the neural-net framework uberMS to measure iron abundances and alpha enrichments from high-resolution optical spectra observed by the Tillinghast Reflector Echelle Spectrograph (TRES), a key resource used for the follow-up of candidate exoplanet hosts. To contextualize these measurements and benchmark our method's performance, we compare to external constraints on metallicity using the Hyades cluster, wide binaries, and asteroids, to external constraints on Teff and  logg  using stars with interferometric radii, and to the results of other abundance measurement methods using overlap samples with the APOGEE and SPOCS catalogs, as well as by applying the Stellar Parameter Classification method directly to TRES spectra. We find that TRES-uberMS provides reliable parameter estimates with errors of roughly 100 K in Teff, 0.09 dex in  logg , and 0.04 dex in [Fe/H] for many nearby dwarf stars, although [Fe/H] performance is poorer for mid-to-late K dwarfs, with the bias worsening with decreasing Teff. Performance is also worse for evolved stars. For [α/Fe], our error may be as good as 0.03 dex for dwarfs based on external benchmarks, despite sizable and variable systematic differences when comparing with specific alpha-element abundances from other catalogs.

Keywords: High resolution spectroscopy, Stellar properties, Exoplanets, Abundance ratios, Stellar abundances, Chemical abundances, Chemical enrichment, Computational methods, Planet hosting stars, Metallicity
The Hawaii Infrared Supernova Study (HISS): Spectroscopic Data Release 1. We present the first data release of the Hawaii Infrared Supernova Study (HISS), consisting of a large sample of near-infrared (NIR) spectra, 0.7─2.5 μm, obtained with the Keck-II/NIRES and IRTF/SpeX spectrographs. This sample is comprised of 90 NIR spectra of 48 transients, spanning from hours after explosion to ≥+350 days. Acquired over 3 yr (2021─2024), this data release includes 17 Type Ia supernovae (SNe), 15 Type II SNe, eight stripped envelope SNe, six interacting SNe, one tidal disruption event, and one SLSN-I. These spectra were all systematically reduced using either the Python-based reduction code PYPEIT or the IDL-based SPEXTOOL and constitute one of the largest NIR samples of transients available to the astrophysical community. We show the utility of NIR spectra and identify the key spectral features across multiple types of SNe. We demonstrate how both early-time and nebular-phase NIR spectra of SNe can be used to investigate the physics of the explosion, and to reveal the properties of the progenitor. With the addition of this data set, the number of publicly available NIR spectra spanning multiple transient types has been substantially increased. In its next phase, HISS will leverage target-of-opportunity spectral observations and NIR imaging from telescopes on Maunakea. Expanding the NIR data set of SNe is vital to the transient community, particularly in light of the increasing emphasis on the infrared regime following the recent launch of the James Webb Space Telescope and the forthcoming launch of the Nancy Grace Roman Space Telescope.

Keywords: Type Ia supernovae, Supernovae, Core-collapse supernovae, Near infrared astronomy
Coronal Mass Ejection Arrival Forecasting with the Drag-based Assimilation of Satellite Observations. Forecasting the arrival of coronal mass ejections (CMEs) is vital for protecting satellites, power systems, and human spaceflight. We present the Heliospheric Observer for Predicting CME Arrival via Nonlinear Drag Assimilation (HELIOPANDA), a framework that integrates the drag-based model (DBM) with spacecraft observations using iterative parameter estimation and Kalman filter assimilation. We introduce a method for estimating the solar wind speed w and drag parameter γ, two key but usually unknown quantities controlling CME propagation, through direct solutions of the DBM equations. We tested the method on 4480 synthetic CME profiles spanning CME speeds of 200─3500 km s−1, solar wind speeds of 250─800 km s−1, and drag parameters of 0.1─1.0 × 10−7 km−1. The results demonstrate that the framework provides accurate reconstructions of the DBM input parameters, providing a solid basis for in situ and remote-sensing applications. By testing a single virtual spacecraft positioned at nine distances along the Sun─Earth line, HELIOPANDA achieved arrival-time errors as low as 0.6 hr for a 600 km s−1 CME and 1 hr for a 2500 km s−1 CME when the spacecraft was located 30 million km from the Sun. We developed a Kalman filter framework to assimilate noisy heliospheric data into the DBM, enabling recursive updates of CME kinematics and robust estimates of w and γ, and yielding Earth and Mars arrival-time predictions within 1─2 hr using 160 simulated hourly measurements. By combining DBM, parameter recovery, and data assimilation, HELIOPANDA provides a pathway to real-time, multipoint CME forecasts, suited to observations from Solar Orbiter, Parker Solar Probe, PUNCH, and planned L4/L5 missions.

Keywords: Heliosphere, The Sun, Solar wind, Solar-planetary interactions, Astronomy data analysis, Solar coronal mass ejection shocks, Solar physics
Modeling Cosmic-Ray Acceleration at Nonplanar Astrophysical Shocks Using the SPECTRUM Code. Diffusive shock acceleration is an established mechanism for accelerating charged particles to high energies, both inside the heliosphere and beyond its boundaries. In this paper, we rigorously test and benchmark the Space Plasma and Energetic Charged particle TRansport on Unstructured Meshes (SPECTRUM) open-source software on steady-state and time-dependent problems for planar and spherical shock waves based on Parker's cosmic-ray transport equation. In particular, we investigate the accuracy of the SPECTRUM algorithm as a function of the shock width, the time step, and other simulation parameters. We compare the forward-in-time and backward-in-time methods for simulating stochastic trajectories, proving that both approaches are valid and yield correct results by benchmarking them against analytic solutions. We also compare source-term and boundary-condition methods to normalize the distribution function for problems with and without distributed energy losses. In each verified case, the simulation results match the analytic prediction, demonstrating the suitability of SPECTRUM to study cosmic-ray shock acceleration in diverse space plasma environments.

Keywords: Monte Carlo methods, Shocks, Interplanetary particle acceleration, Cosmic rays
Dual-stage Flare Prediction Using a Full-disk Flare Index: Trend-disturbance Decomposition and Hybrid Forecasting Model. Accurate prediction of solar flares is essential for space weather warnings and safeguarding technological infrastructure. This study proposes a dual-stage flare prediction framework leveraging the full-disk flare index (FI). In the first stage, FI is decomposed into long-term trend and short-term disturbance components via a "trend-disturbance" decomposition strategy. The iTransformer model independently forecasts each component, which is then fused to generate high-fidelity FI predictions. The second stage develops a regression-classification architecture that maps predicted FI values to flare intensity levels, enabling comprehensive full-disk flare forecasting. Experimental results indicate that the decomposition strategy improves performance across all flare classes, reducing the mean absolute error of FI prediction to 19.066 and achieving a TSS of 0.639 and an F1 score of 0.649 for M-class flares. Tested from the solar minimum in 2019 through the 2024 solar maximum, the framework surpasses the operational forecasting capabilities of leading space weather prediction centers such as SWPC and SEPC, successfully delivering reliable 72 hr flare warnings during 2024 May events. The dual-stage framework introduces an innovative approach to improving the accuracy and reliability of flare prediction, offering a valuable reference for forecasting extreme solar activity events.

Keywords: Solar activity, Solar physics, Solar flares, Neural networks