Studying active galactic nucleus (AGN) pairs and their host galaxies is essential for understanding the interplay between galaxy mergers and key internal processes such as supermassive black hole fueling and feedback. We crossmatch between the Big Multi-AGN Catalog and the public data archive of the Very Large Telescope (VLT)/MUSE, and obtain 12 AGN pair candidates in the local Universe ( z ≲ 0.1) with a projected distance of r _p ≤ 20 kpc. Using archival VLT/MUSE data, we present a spatially resolved study of the ionized gas kinematics and ionization properties of these 12 AGN pair candidates. By decomposing the optical emission lines into two Gaussian components, we try to separate gas associated with disk rotation from noncircular motions. We further identify dominant ionization mechanisms using spatially resolved Baldwin–Phillips–Terlevich diagnostics. We find that both nuclei in four of the 12 systems are classified as a Seyfert or LINER. In addition, three nuclei are classified as star-forming or composite in the optical diagnostics, but are identified as AGNs at other wavelengths. Kinematically, regularly rotating ionized gas disks are detected in 16 of 24 nuclei. Prominent tidal features traced by ionized gas are also detected in nine systems. Ionized gas outflows are widespread and are detected in 18 nuclei. Finally, for three nuclei (Mrk 739A, NGC 7592B, and J1544+0446A), we find evidence for fading AGN activity over the past several tens of thousands of years, based on optical emission-line ratios and an assumed AGN photoionization model.
We present an uncertainty-aware, fine-grained multiclass classification of unresolved Fermi Large Area Telescope (LAT) gamma-ray sources in the 4FGL-DR4 catalog. We define the unresolved-source population as blank-class sources, sources labeled “ UNK ,” and blazar candidates of uncertain type, and classify them within a supervised 22-class catalog taxonomy. The baseline analysis represents each source through the three catalog spectral forms—power law (PL), log parabola (LP), and PL with a superexponential cutoff (PLEC)—and compares TabNet, random forest, and a stacked TabNet + random forest ensemble. The ensemble reaches accuracies of 92.95%, 92.45%, and 94.13% for the PL, LP, and PLEC representations, respectively. To quantify the role of feature choice, we further perform controlled ablations using an all-spectral configuration, which combines PL, LP, and PLEC parameters simultaneously, together with LP-only, PL-only, PLEC-only, and photometry + variability representations. Each configuration is tested with and without uncertainty columns and with ${\lambda }_{\max }=0.1$ –0.5 for synthetic interpolation. The strongest ablation result is obtained by the uncertainty-aware PLEC configuration, with accuracy and weighted F _1 of 0.9614. We also provide probabilistic predictions for all 4186 unresolved sources, including top-3 class probabilities, full class-probability vectors, top 1 – top 2 probability margins, and predictive entropy.
In this work, we employ a multilayer perceptron to derive atmospheric parameters (effective temperature T _eff , surface gravity $\mathrm{log}g$ , and metallicity [M/H]) for a large sample of cool giants from LAMOST DR9 low-resolution spectra. For spectra with a median signal-to-noise ratio ∼ 105, the achieved internal precisions are 36 K, 0.22 dex, and 0.16 dex for T _eff , $\mathrm{log}g$ , and [M/H], respectively. By integrating these parameters with multiband photometry from the Two Micron All Sky Survey and the Wide-field Infrared Survey Explorer, we estimate stellar distances within a Bayesian framework incorporating 3D extinction maps. Our distance estimates exhibit a remarkably stable fractional internal uncertainty ( σ _D / D ) of ∼8.4% across the sampled parameter space. External validations against Gaia EDR3 Bayesian geometric distances show a median distance bias of only 0.1%, with a 16th–84th percentile dispersion of −21.8%–28.9%. Furthermore, comparisons with open cluster distances confirm that systematic deviations remain within 10% for stars with T _eff ∼ 3700–4100 K. Applying these high-precision distances and velocities, we characterize the spatial distribution and kinematics of the Galactic disk. Our results demonstrate that cool giants are powerful tracers for the outer disk, effectively revealing its complex asymmetric structures and kinematic substructures.
Precise orbit determination of natural satellites relies on the consistent combination of heterogeneous astrometric observations spanning long time intervals. However, the use of different reference star catalogs introduces spatially correlated systematic biases that degrade data consistency and limit orbit accuracy. In this work, we construct a unified correction framework based on the Gaia DR3 reference frame and derive position and proper-motion corrections for 17 star catalogs commonly used in natural satellite astrometry. The method employs rigorous epoch propagation, HEALPix-based spatial partitioning, and robust median statistics to estimate local systematics, and reconstructs a continuous correction field using radial basis function interpolation. The derived corrections reveal a clear hierarchy among the catalogs: modern catalogs exhibit systematics at the milliarcsecond level, whereas older photographic catalogs show position offsets reaching the 100 mas to arcsecond level. This framework provides a consistent basis for reprocessing historical observations and improves the astrometric accuracy of natural satellite observations, thereby enhancing the precision of orbit determination and dynamical studies of natural satellite systems.
Coronal mass ejections (CMEs) can trigger severe space weather disturbances, disrupting Earth’s magnetic field and impairing communication systems. Accurately predicting CME arrival time is therefore of critical importance. Existing methods typically rely on either CME physical parameters alone or a single CME image. However, CME propagation is a dynamic process, and neither a single image nor an isolated physical parameter provides sufficient information for high-accuracy prediction. To address this limitation, we propose a novel approach that simultaneously extracts and fuses features from CME image sequences and physical parameters. The method comprises three key modules. First, the image sequence feature extraction network leverages the sparsity characteristics of CMEs to extract dynamic information from sequential images, laying a foundation for efficient arrival time prediction. Second, the physical parameter feature extraction network extracts meaningful features from CME physical parameters, including speed, size, and density, which are directly relevant to propagation dynamics. Finally, the multimodal feature fusion network integrates features from both modalities using graph convolutional networks. It models each modality’s features as individual nodes, captures intermodal correlations, and performs adaptive feature fusion. By incorporating both static CME physical parameters and evolutionary visual information of sequential images, this approach yields a more informative representation of CME characteristics, thereby significantly enhancing the accuracy of CME arrival time prediction. The experimental results demonstrate that our method achieves a mean absolute error (MAE) of 10.765 hr for CME arrival time prediction, which is competitive with state-of-the-art methods.
Ultracool dwarfs are faint at optical wavelengths, and catalog-based color selection can therefore miss sources with low signal-to-noise ratios or incomplete photometry. We present an image-based search pipeline for identifying L and T dwarf candidates in five-band Pan-STARRS images. Multiband FITS cutouts are used to detect candidate sources directly in the image plane, and the resulting detections are then validated using multisurvey photometry. Images containing M dwarfs are included as nontarget stellar contaminants, and a unified percentile-clipped normalization is adopted to maintain a common input scale across the five Pan-STARRS bands and all image cutouts. The trained detector is applied to the Pan-STARRS–Dark Energy Survey overlap region. Candidate validation includes duplicate removal, removal of known and previously reported sources, multisurvey photometric matching, phototype classification, LePhare extragalactic-template checks, and proper-motion reassessment. For sources lacking reliable AllWISE counterparts, CatWISE2020 photometry is used as a supplementary recovery route. The combined validation retains 39 new L and T dwarf candidates, including eight recovered through the CatWISE2020-based extension. In addition, 371 image-detected sources lack sufficient multiband photometric information for reliable phototype classification and are kept as reserve targets for future supplementary photometry or follow-up observations. A comparison with the Pan-STARRS Data Release 2 (DR2) catalog shows that two final candidates have no catalog counterpart, while some sources with Pan-STARRS1 (PS1) DR2 counterparts lack valid measurements in one or more key PS1 bands. These results show that image-based object detection complements conventional color selection and provides an additional route to faint ultracool-dwarf candidates, including cases affected by incomplete catalog photometry.
We study the cosmic evolution of radio-loud active galactic nuclei (AGNs) using a beaming-minimized sample of 4555 steep-spectrum sources compiled from the XXL survey, VLA-COSMOS, and other wide-field datasets. We constrain the rest-frame 1.4 GHz radio luminosity function (RLF) over 0 < z ≲ 5, and model it with a luminosity-and-density evolution (LADE; LE+DE) framework. Among the tested parameterizations, Model C is statistically preferred and provides a globally consistent description of the binned RLFs while remaining compatible with local RLF measurements and Euclidean-normalized source counts. In the fiducial solution, the LE term rises toward cosmic noon ( z ∼ 2–3) and then flattens or mildly declines, whereas the DE term decreases monotonically with redshift. This combined evolution naturally reproduces the observed luminosity-dependent turnover redshift z _peak ( L ) (often termed “cosmic downsizing”) without imposing a priori distinct evolutionary laws for low- and high-power sources. We further show that the same LADE functional family calibrated for star-forming galaxies also describes radio-loud AGNs when fitted independently, enabling a unified two-component (star-forming galaxy + AGN) model consistent with both the local RLF and source-count statistics. Finally, converting the inferred 1.4 GHz RLF to a kinetic luminosity function yields a radio-mode black hole accretion rate density traced by the steep-spectrum radio-loud AGN population. Its redshift dependence broadly tracks, in shape, the radio-based cosmic star formation rate density (after a conventional rescaling), with both histories peaking near z ∼ 2. The similarity suggests that the growth of this radio-loud AGN subpopulation is broadly synchronized, at the population level, with the cosmic star formation history of galaxies.
This paper presents a comprehensive statistical analysis of minifilament eruptions (MFEs) using high-cadence full-disk Hα blue-wing observations from Big Bear Solar Observatory. Despite the recognized importance of MFEs for coronal dynamics and solar wind structuring, previous efforts were often limited by small sample sizes or restricted fields of view, leaving open questions about their global occurrence and characteristic properties. We developed an algorithm incorporating intensity thresholding and temporal tracking to detect sudden enhancements in Hα blue-wing images. A total of 1986 such events were identified during a 4 hr period on 2020 June 9. These detections were cross-validated using Hα line-center observations to confirm the presence of associated filament structures. The analysis yields an occurrence rate of ∼6.6× 10^4 per day, an average length of ∼17 Mm, and a typical lifetime of ∼21 minutes. A power-law distribution in eruption lengths (with slope ∼-4.8) and a sublinear scaling between duration and length suggest that larger eruptions tend to last longer. The length distribution of MFEs was further analyzed relative to active region proximity, revealing that eruptions occurring nearby a sunspot region were, on average, larger than the global mean eruption length. Additionally, an eruption density map was used to analyze the spatial distribution of MFEs relative to coronal hole boundaries. The results indicate a mild suppression of activity inside coronal holes and intermediate eruption frequency in the boundary regions. We briefly discuss their potential role in structuring the small-scale solar wind features such as magnetic switchbacks and small-scale magnetic flux ropes.
The latitudinal structure of the solar wind varies with the solar activity, and the knowledge of its history is needed to understand the interaction of the heliosphere with the local interstellar medium and construction of realistic models of the heliosphere, with observation-verifiable predictions. The solar wind structure at 1 au is researched from the ecliptic plane using remote-sensing methods, mostly interplanetary scintillations, but available data reach only to 1985. In this paper, we extend the history of the evolution of the latitudinal profiles of the solar wind to the beginning of in situ solar wind observations in 1964. To that end, we define a set of proxies subjected to principal component analysis suitable to reproduce the actually observed evolution and available back to 1964. To reconstruct the density, we need to reconstruct the alpha content during the interval 1964–1974. We develop the alpha reconstruction method and are now able to reconstruct the evolution of the solar wind for both speed and density for the interval 1964–2026. The WawHelioIon MP model thus obtained is the zeroth model for an extension further in time, to be performed based on helioglow lightcurves from a new experiment, GLOWS, on board a new NASA mission, IMAP.
Coherent and quasi-coherent variability in active galactic nuclei (AGN) can serve as a useful diagnostic of dynamical processes operating in the immediate environments of supermassive black holes. The time-domain capabilities of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) enable systematic searches for such signals across large AGN samples. We identify and classify coherent oscillatory processes in AGN cores using LSST Data Preview 1 (DP1), and compile the results in the QhX Hub, an interactive catalog providing statistical variability features and visualization tools. AGN are selected by cross-matching LSST DP1 sources with external catalogs, and light curves are analyzed with the multiband wavelet-based Quasar harmonic eXplorer ( QhX ), designed to detect coherent signals in irregularly sampled, red-noise-dominated data. The catalog contains 230 objects stratified into variability classes. A single strong coherent candidate is identified: 3D-HST 2351, z = 0.198, LSST DP1 object_id = 611255072642321324, with a period of $P=2.7{6}_{-0.06}^{+2.65}$ days, where the large upper bound reflects the limited observational baseline. Its coherent nuclear variability is the primary motivation for follow-up. In subsequent LSST data releases, all catalog sources will be recalculated on progressively longer baselines, enabling more robust population-level characterization of coherent AGN variability.
One of the key unresolved questions in modern cosmology is the Hubble tension, which arises from the inconsistency between determinations of the Hubble constant ( H _0 ) based on nearby observations and those predicted by early-Universe data under the standard ΛCDM paradigm. Recent advances have identified the new Dark Energy Spectroscopic Instrument (DESI) survey as a promising observational tool for probing late-time cosmology. In this paper, we use the latest DESI DR2 baryon acoustic oscillation (BAO) measurements, combined with nonparametrically reconstructed Type Ia supernovae and cosmic chronometer data, to constrain the Hubble constant using three complementary machine learning methodologies: Gaussian process regression (GPR), artificial neural networks (ANNs), and long short-term memory (LSTM) networks. We perform individual and joint constraints on H _0 at the effective redshifts of DESI DR2 tracers, yielding H _0 = 61.8 ± 5.3 km s ^−1 Mpc ^−1 (GPR), 66.3 ± 5.2 km s ^−1 Mpc ^−1 (ANN), and 68.7 ± 6.0 km s ^−1 Mpc ^−1 (LSTM). Our results exhibit excellent agreement with the recent Planck measurements within 1 σ , which highlight the potential of data-driven, model-independent methods to address the Hubble tension. Finally, we further test the sensitivity of our results to potential systematics in the DESI data, emphasizing the importance of understanding systematic uncertainties in BAO surveys.
A comprehensive study of novae in the Large Magellanic Cloud (LMC) is presented. A total of 66 nova eruptions have been reported in the LMC over the past century (1926-2025). Of these, a total of 48 eruptions from 39 unique progenitor systems (9 eruptions are recurrences of 4 known recurrent novae) have sufficient photometric data to permit the maximum magnitudes and rates of decline to be reliably estimated. These data confirm earlier studies showing that LMC novae are, on average, slightly more luminous and faster evolving compared with novae in the Andromeda galaxy (M31) or the Milky Way. For the first time, the nova models of Yaron et al. (2005) have been used in conjunction with the light curve data to estimate the fundamental properties of the LMC nova population. The models suggest that LMC novae are characterized by generally higher WD masses and higher expansion velocities compared with M31 novae. The higher average WD masses has resulted in a higher percentage of RN eruptions in the LMC ($\sim20.6$%) compared with that seen in M31 ($\sim6.40$%). Future observations made possible by the Vera Rubin Observatory promise to revolutionize our understanding of nova populations in the LMC, and in galaxies beyond the local group.
Dielectronic recombination (DR) rate coefficients of Fe ^6+ –Fe ^3+ ions are calculated using the multiconfiguration Breit–Pauli approach as implemented in the autostructure code. A large-scale electron correlation between n = 3 and n = 4 configurations is considered in the present calculations, and semiempirical corrections to the near-threshold resonance energies are also applied, to improve the results at low temperatures. The present DR rate coefficients are greater than those previously calculated and currently used by CLOUDY, ION, and XSTAR by up to an order of magnitude. These DR rate coefficients of Fe ^6+ –Fe ^3+ , along with our previous results for Fe ^8+ and Fe ^7+ , are crucial for accurately determining the ionization balance of M-shell iron ions. The present increased DR rate coefficients would shift the predicted ionization equilibrium of these ions towards lower charge states.
Observations reveal a pronounced deficit of white dwarfs (WDs) in open clusters (OCs) relative to theoretical expectations, suggesting that a significant fraction of WDs may have escaped from their parent clusters after formation. In this work, we perform a systematic search for escaped WD candidates from OCs by back-tracing the motions of WDs and star clusters from Gaia DR3 catalogs. We identify 476 candidate WDs with kinematics consistent with having escaped from one of 175 OCs. A control-field Monte Carlo (MC) test yields a contamination rate of 87.6
Neutron monitors serve as the premier instruments for continuous, precise, real-time count rates of secondary particles from cosmic-ray showers, and recently developed techniques provide measures of the spectral index as well. We investigate the rigidity dependence of two such measures, the leader fraction and the count-rate ratio between leaded and unleaded counters, from the transportable semileaded neutron monitor “Changvan” during 2018–2019 and 2019–2020 latitude surveys on board the Chinese icebreaker Xue Long . Changvan comprises two outer leaded counters and one central counter without a lead producer, allowing simultaneous measurements of leaded and unleaded responses within a common moderator–reflector structure. Because the unleaded counter is embedded in the same assembly, its response is very different from that of an isolated bare detector. We examine the behaviors of the count-rate ratio C _Leaded / C _Unleaded and leader fractions L _Leaded and L _Unleaded over a wide range of geomagnetic cutoff rigidity. The count-rate ratio and both leader fractions decrease systematically with increasing cutoff rigidity, and Monte Carlo calculations reproduce the observed trends in leader fraction. Comparison with an earlier 2006–2007 fully leaded latitude survey suggests a consistent physical interpretation in which neutrons escaping from the leaded counters contribute to both count-rate and leader-fraction behavior. The unleaded counter acquires enhanced high-rigidity response through neutrons originating from lead around neighboring counters. This study confirms that the count-rate ratio and leader fractions serve to measure spectral variations, and the variations in leader fraction are well modeled by Monte Carlo simulations.
Large-scale spectroscopic surveys (e.g., LAMOST and DESI) inevitably contain a substantial fraction of defective spectra due to various observational and instrumental limitations. These defects, typically manifesting in the spectra as missing or corrupted contiguous regions, compromise downstream analyses. Conventional methods, such as interpolation, struggle with large gaps and erode features associated with physical information. In this work, we present Progressive Restoration and Inpainting for Spectral Measurement (PRISM), a deep learning model designed to restore defective stellar spectra while preserving their scientific integrity. Specifically, PRISM employs a two-stage architecture: a transformer-based encoder captures long-range dependencies to reconstruct the global structure, followed by a convolutional module that enhances local details. Furthermore, a progressive strategy iteratively recovers large defects by propagating context from valid regions. In synthetic experiments, downstream analysis of PRISM-restored spectra yields a stellar classification accuracy of 92.33% and an effective temperature mean absolute error (MAE) of 42.39 K, closely matching the baselines of pristine spectra. On real observations, PRISM reduces the effective temperature MAE to 55.95 K, drastically improving upon the standard pipeline’s 110.86 K. Finally, we deployed PRISM on all defective LAMOST DR10 spectra, releasing a value-added catalog of rederived parameters and corresponding restored spectra.
Dust plays a crucial role in protoplanetary disks (PPDs) evolution and planet formation, influencing disk dynamics through gas-dust coupling, regulating disk temperature by dominating continuum opacity, and altering disk ionization fraction by capturing free electrons. In this work, we develop a high-order discontinuous Galerkin (DG) method-based open-source code GRACE-DG to solve the collision-induced coagulation-fragmentation equations. In particular, we have derived a new conservative formulation for the non-linear fragmentation term, which enables the DG method to capture the mass transfer process. The new solver exhibits good convergence in coupled aggregation and breakage simulations, making it highly suitable for future integration into hydrodynamic codes.
