SGR 1935+2154 has emerged as the most active magnetar in recent years, exhibiting X-ray burst activity nearly annually and emitting a fast radio burst (FRB 200428 (FRB1)) in its 2020 April activity, for the first time from a Milky Way source, accompanied by an X-ray burst (X_FRB1). The source was active again in 2021 September and 2022 January, but without emitting radio bursts. In 2022 October, the magnetar entered a new activity that included radio bursts, three of them reported to be associated with X-ray bursts. We present a comprehensive study of the activity, as observed by the Fermi Gamma-Ray Burst Monitor, and offer a comparison with the previous activities. The mean burst duration of the observed 113 X-ray bursts (∼122 ms) is ∼30% shorter than in 2020 April but longer than most other activities. The mean spectral parameters (Γ_CPL ∼ 0.82, E_{p CPL} ∼ 26 keV and kT_BBs ∼ 5.10 keV, kT_BBh ∼ 8.96 keV) are generally comparable to the previous activities but with a softer Γ_CPL and larger R_BBh than in 2020 April. The ${R}_{\mathrm{BB}}^{2}\mbox{--}{{kT}}_{\mathrm{BB}}$ parameter space shows a marked contrast where two distinctive, nonoverlapping, and smaller soft and hard blackbody regions are manifested in 2020 April but not in 2022 October and the other episodes. The fluence–duration power-law correlation is ∼60% steeper in 2022 October and the other episodes than in 2020 April. The three new X-ray bursts associated with radio bursts stand out in being ∼2–10-fold longer than the mean duration, with X-ray-to-radio duration, flux, and fluence ratios of ∼10^2–3, 10^3–4, and 10^5–7, respectively, comparable to the X_FRB1/FRB1 ratio for duration but larger for flux and fluence due to fainter radio bursts in 2022. We discuss the results in the context of the new emission models that propose physical mechanisms for the X-ray and radio burst emission from magnetars.

We present a catalog of 536 fast radio bursts (FRBs) detected by the Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst (CHIME/FRB) Project between 400 and 800 MHz from 2018 July 25 to 2019 July 1, including 62 bursts from 18 previously reported repeating sources. The catalog represents the first large sample, including bursts from repeaters and nonrepeaters, observed in a single survey with uniform selection effects. This facilitates comparative and absolute studies of the FRB population. We show that repeaters and apparent nonrepeaters have sky locations and dispersion measures (DMs) that are consistent with being drawn from the same distribution. However, bursts from repeating sources differ from apparent nonrepeaters in intrinsic temporal width and spectral bandwidth. Through injection of simulated events into our detection pipeline, we perform an absolute calibration of selection effects to account for systematic biases. We find evidence for a population of FRBs—composing a large fraction of the overall population—with a scattering time at 600 MHz in excess of 10 ms, of which only a small fraction are observed by CHIME/FRB. We infer a power-law index for the cumulative fluence distribution of $\alpha =-1.40\pm 0.11({\rm{stat.}}{)}_{-0.09}^{+0.06}({\rm{sys.}})$, consistent with the −3/2 expectation for a nonevolving population in Euclidean space. We find that α is steeper for high-DM events and shallower for low-DM events, which is what would be expected when DM is correlated with distance. We infer a sky rate of $[820\pm 60({\rm{stat.}}{)}_{-200}^{+220}({\rm{sys.}})]/{\rm{sky}}/{\rm{day}}$ above a fluence of 5 Jy ms at 600 MHz, with a scattering time at 600 MHz under 10 ms and DM above 100 pc cm⁻³.

The Lomb–Scargle periodogram is a well-known algorithm for detecting and characterizing periodic signals in unevenly sampled data. This paper presents a conceptual introduction to the Lomb–Scargle periodogram and important practical considerations for its use. Rather than a rigorous mathematical treatment, the goal of this paper is to build intuition about what assumptions are implicit in the use of the Lomb–Scargle periodogram and related estimators of periodicity, so as to motivate important practical considerations required in its proper application and interpretation.

We present a novel multimodal neural network (MNN) for classifying astronomical sources in multiband ground-based observations, from optical to near-infrared (NIR), to separate sources in stars, galaxies, and quasars. Our approach combines a convolutional neural network branch for learning morphological features from r-band images with an artificial neural network branch for extracting spectral energy distribution (SED) information. Specifically, we have used nine-band optical (ugri) and NIR (ZYHJK_s) data from the Kilo-Degree Survey (KiDS) Data Release 5. The two branches of the network are concatenated and feed into fully connected layers for final classification. We train the network on a spectroscopically confirmed sample from the Sloan Digital Sky Survey crossmatched with KiDS. The trained model achieves 98.76% overall accuracy on an independent testing data set, with F1-scores exceeding 95% for each class. Raising the output probability threshold, we obtain higher purity at the cost of lower completeness. We have also validated the network using external catalogs crossmatched with KiDS, correctly classifying 99.74% of a pure star sample selected from Gaia parallaxes and proper motions, and 99.74% of an external galaxy sample from the Galaxy and Mass Assembly survey, adjusted for low-redshift contamination. We apply the trained network to 27,335,836 KiDS DR5 sources with r ≤ 23 mag to generate a new classification catalog. This MNN successfully leverages both morphological and SED information to enable efficient and robust classification of stars, quasars, and galaxies in large photometric surveys.

The long and (almost) continuous high-cadence light curves provided by the TESS space mission are ideally suited to study in detail brightness variations in stellar sources on the broad range of timescales between minutes and months. By applying Fourier techniques, even low-amplitude coherent variations in noisy data can be identified, and their periods can be measured with high accuracy. Here, the available TESS light curves of all intermediate polars (IPs) and candidates listed on Koji Mukai’s Intermediate Polar Home Page and in the Ritter & Kolb catalog are subjected to a frequency analysis. A total of 121 systems are studied. In about half of them—mostly confirmed IPs, but also some candidates—variations caused by the white dwarf (WD) rotation are detected allowing the determination of precise periods. Comparison with previous measurements permitted, in some cases, confirming or newly uncovering period variations. The relative strength of the WD spin signals, their orbital sidebands, and overtones in power spectra—having the potential to shed light on the structure of emission, reflection, and reprocessing sites and their variations over time in the IP systems—was measured. Apart from IP-type variations, a wealth of other periodic or aperiodic brightness changes was observed in many of the target stars and is documented here. This includes refined or newly detected orbital periods, the temporal evolution of waveforms, superhumps, quasiperiodic oscillations, short-timescale (<1 day) bursts, coherent variations of an unidentified origin, and other sometimes enigmatic phenomena.

The Transiting Exoplanet Survey Satellite (TESS) has surveyed nearly the entire sky in full-frame image mode with a time resolution of 200 s to 30 minutes and a temporal baseline of at least 27 days. In addition to the primary goal of discovering new exoplanets, TESS is exceptionally capable at detecting variable stars, and in particular short-period eclipsing binaries, which are relatively common, making up a few percent of all stars, and represent powerful astrophysical laboratories for deep investigations of stellar formation and evolution. We combed Sectors 1–82 of the TESS full-frame image data searching for eclipsing binary stars using a neural network that identified ∼1.2 million stars with eclipse-like features. Of these, we have performed an in-depth analysis on ∼60,000 targets using automated methods and manual inspection by citizen scientists. Here we present a catalog of 10,001 uniformly vetted and validated eclipsing binary stars that passed all our ephemeris and photocenter tests, as well as complementary visual inspection. Of these, 7936 are new eclipsing binaries while the remaining 2065 are known systems for which we update the published ephemerides. We outline the detection and analysis of the targets, discuss the properties of the sample, and highlight potentially interesting systems. Finally, we also provide a list of ∼900,000 unvetted and unvalidated targets for which the neural network found eclipse-like features with a score higher than 0.9, and for which there are no known eclipsing binaries within a sky-projected separation of a TESS pixel (≈21″).

Magnetars are neutron stars with extremely strong magnetic fields, frequently powering high-energy activity in X-rays. Pulsed radio emission following some X-ray outbursts has been detected, albeit its physical origin is unclear. It has long been speculated that the origin of magnetars’ radio signals is different from those from canonical pulsars, although convincing evidence is still lacking. Five months after magnetar SGR 1935+2154's X-ray outburst and its associated fast radio burst 20200428, a radio pulsar phase was discovered. Here we report the discovery of X-ray spectral hardening associated with the emergence of periodic radio pulsations from SGR 1935+2154 and a detailed analysis of the properties of the radio pulses. The observations suggest that radio emission originates from the outer magnetosphere of the magnetar, and the surface heating due to the bombardment of inward-going particles from the radio emission region is responsible for the observed X-ray spectral hardening.

Many X-ray binaries (XRBs) are transiently accreting. Having statistics on their recurrence times is helpful to address questions related to binary evolution and populations, as well as the physics of binary systems. We compile a catalog of known outbursts of 87 transient neutron stars (identified through bursts or pulsations) and low-mass XRBs until mid-2025. Most outbursts are taken from the literature, but we also identify some outbursts from public X-ray monitoring lightcurves. We find 109 outbursts not previously identified in the literature; most are from the frequent transients GRS 1747-312 and the Rapid Burster MXB 1730-335, though we suspect that two outbursts from Liller 1 may be from another transient besides the Rapid Burster. We also find new outbursts for 10 other systems, and verify substantial quiescent intervals for XMM J174457-2850.3, XMMU J174716.1-281048, and AX J1754.2-2754. Outburst detection has been relatively efficient since 1996 for outbursts above F_X (2–10 keV) = 3 × 10⁻¹⁰ erg cm⁻² s⁻¹. While several systems have many known outbursts, 40 of the 87 systems we track have zero or one recorded outburst between 1996 and 2023. This suggests that many faint Galactic center XRBs may be neutron star XRBs, though we cannot completely rule out the proposition that most neutron star XRBs undergo frequent outbursts below all-sky monitor detection limits.

We present a catalog of 8440 candidate very metal-poor (VMP; [Fe/H] ≤ −2.0) main-sequence turn-off (MSTO) and red giant stars in the Milky Way, identified from low-resolution spectra in LAMOST DR10. More than 7000 of these candidates are brighter than G ∼ 16, making them excellent targets for high-resolution spectroscopic follow-up with 4–10 m class telescopes. Unlike most previous studies, we employed an empirical calibration to estimate metallicities from the equivalent widths of the calcium triplet lines, taking advantage of the high signal-to-noise ratio in the red arm of LAMOST spectra. We further refined this calibration to improve its reliability for more distant stars. This method enables robust identification of VMP candidates with metallicities as low as [Fe/H] = −4.0 among both MSTO and red giant stars. Comparisons with metal-poor samples from other spectroscopic surveys and high-resolution follow-up observations confirm the accuracy of our estimates, showing a typical median offset of ∼0.1 dex and a standard deviation of ∼0.2 dex.

The Gaia DR3 has provided a large sample of more than 6.6 million quasar candidates with high completeness but low purity. Previous work on the CatNorth quasar candidate catalog has shown that including external multiband data and applying machine learning methods can efficiently purify the original Gaia DR3 quasar candidate catalog and improve the redshift estimates. In this paper, we extend the Gaia DR3 quasar candidate selection to the Southern Hemisphere using data from SkyMapper, CatWISE, and Visible and Infrared Survey Telescope for Astronomy surveys. We train an XGBoost classifier on a unified set of high-confidence stars and spectroscopically confirmed quasars and galaxies. For sources with available Gaia BP/RP spectra, spectroscopic redshifts are derived using a pretrained convolutional neural network (RegNet). We also train an ensemble photometric redshift estimation model based on XGBoost, TabNet, and FT-Transformer, achieving a root mean square error of 0.2256 and a normalized median absolute deviation of 0.0187 on the validation set. By merging CatSouth with the previously published CatNorth catalog, we construct the unified all-sky CatGlobe catalog with nearly 1.9 million sources at G < 21, providing a comprehensive and high-purity quasar candidate sample for future spectroscopic and cosmological investigations.

Quasars are objects of high interest in extragalactic astrophysics, cosmology, and astrometry. One of their useful qualities is their potential radio-loudness. However, the fraction of radio-loud versus radio-quiet quasars is subject to ongoing investigations, where the statistical power is limited by the low number of known quasars with radio counterparts. In this analysis, we revisited the radio-loudness statistics of quasars by significantly expanding the pool of known sources. Our main goal was to create a new, value-added quasar catalog with information about their extinction-corrected magnitudes, radio flux density, possible contamination levels, and other flags, besides their sky coordinates and photometric redshifts. We cross-matched the optical Quaia catalog of about 1.3 million quasars (from the Gaia data) with 1.9 million sources from the Very Large Array Sky Survey (VLASS) radio catalog. We explored different thresholds for the matching radius, balancing completeness and purity of the resulting Quaia-VLASS catalog, and found 1$\mathop{.}\limits^{\unicode{x02033}}$5 a sufficient choice. Our main finding is that the quasar radio-loud fraction is in good agreement with previous works (<10%), and there is no significant large-scale sky pattern in radio-loudness. The exact estimate depends on the G-band magnitude limit, and we observed weak trends with redshift and absolute optical magnitude, possibly indicating remnant systematic effects in our data. The crossmatched Quaia-VLASS catalog with 43,650 sources and accompanying codes is available at doi:10.5281/zenodo.16035690. This latest census of QSOs with radio counterparts will facilitate further investigations of the dichotomy of radio-loud and radio-quiet quasars, and it may also support other lines of research.

F10.7, the solar radiation flux at a wavelength of 10.7 cm, serves as a crucial parameter in various space weather models and plays a significant role in measuring the intensity of solar activity. The study and prediction of F10.7 are of great significance for many applications. The motivation for this work stems from the close correlation between the F10.7 and various types and levels of solar activity, which can be well informed by solar images: By extracting relevant features from multimodal data sources and integrating them into the F10.7 prediction, we expect to improve prediction accuracy. To this end, we propose a multimodal F10.7 prediction model with Mamba, leveraging both the F10.7 data and several types of solar image data, such as ADAPT-GONG images, Helioseismic and Magnetic Imager magnetograms, and EUV images (AIA 131, 211, and 304 Å). We construct Mamba-based modules for the F10.7 index (MaFI) and sequential image (MaSI) representation learning. The temporal embeddings learned from these two modules are then fused by cross attention to capture relationships between the F10.7 and solar image data. Extensive experimental results demonstrate the superior performance of the proposed multimodal model compared to single-modal models in predicting F10.7 within approximately one solar activity cycle (from 2010 to 2024). We also studied the selection methodology for different types of images and found that, in general, the robustness of multimodal models increases with the number of image types used. However, as long as we select the appropriate data types, we can still achieve excellent prediction results with fewer image types.

The hemispheric asymmetry of solar activity provides important diagnostics of solar dynamo processes. In this study, we present a phase-resolved statistical analysis of hemispheric sunspot number asymmetry over solar cycles 17–25 (1939–2024), using monthly mean data from the National Astronomical Observatory of Japan. By combining normalized asymmetry indices, cumulative deviation tracking, and year-by-year significance testing, we identify four key results: (1) the northern hemisphere consistently dominates during the ascending phases of most cycles, suggesting a preferential emergence of magnetic flux at the northern hemisphere; (2) the hemispheric asymmetry significantly diminishes during polarity reversal periods, indicating a balancing effect of global magnetic reconfiguration; (3) solar cycles 23 and 25 exhibit remarkably similar asymmetry patterns, implying solar cycle 25 is likely to be similar to solar cycle 23; and (4) the strength of hemispheric dominance correlates with the overall solar activity level, with stronger asymmetries observed during high-activity phases and weaker signals near solar minima. These findings offer new observational constraints for modeling hemispheric solar dynamics and establish a reproducible, scalable framework for future investigations of long-term solar magnetic asymmetries.

The LAMOST-Kepler/K2 Medium-Resolution Spectroscopic Survey (LK-MRS) conducted time-domain (TD) medium-resolution spectroscopic observations of 20 LAMOST plates in the Kepler and K2 fields from 2018 to 2023, a phase designated as LK-MRS-I. A catalog of stellar parameters for a total of 36,588 stars, derived from the spectra collected during these 5 yr, including the effective temperature, the surface gravity, the metallicity, the α-element abundance, the radial velocity (RV), and $v\sin i$ of the target stars, is released, together with the weighted averages and uncertainties. At a signal-to-noise ratio = 10, the measurement uncertainties are 120 K, 0.18 dex, 0.13 dex, 0.08 dex, 1.9 km s⁻¹, and 4.0 km s⁻¹ for the above parameters, respectively. Comparisons with the parameters provided by the APOGEE and GALAH surveys validate the effective temperature and surface gravity measurements, showing minor discrepancies in metallicity and α-element abundance values. We identified some peculiar star candidates, including 764 metal-poor stars, 174 very metal-poor stars, and 30 high-velocity stars. Moreover, we found 2333 stars whose RV seems to be variable. Using Kepler/K2 or TESS photometric data, we confirmed 371 periodic variable stars among the RV variable candidates and classified their variability types. LK-MRS-I provides spectroscopic data that is useful for studies of the Kepler and K2 fields. The LK-MRS project will continue collecting TD medium-resolution spectra for target stars during the third phase of LAMOST surveys, providing data to support further scientific research.

The unprecedented remote-sensing observations obtained by the Wide Field Imager for Solar Probe (WISPR) on board the Parker Solar Probe (PSP) from distances inside Mercury’s orbit are revolutionizing our perception of the fine-scale morphology and evolution of the solar corona. For the first time, the inherent limitations of 1 au observations arising from the superposition of optically thin features along the long lines of sight are being overcome by observing from “within.” The richness of information makes the interpretation of the coronal scene highly subject to the processing performed on the images to remove the background, i.e., the bright, constant emission from interplanetary dust (F-corona). Several customized techniques to reveal the faint, solar-outflowing plasma and the large-scale K-corona structures have already been developed for WISPR. They differ on how the background is estimated. None, however, excels at revealing the faint and diffuse K-corona brightness component that permeates the scene. In this paper, we introduce a new, heuristic approach to model the evolution of the F-corona background as a function of the observer’s location. The methodology exploits WISPR data sets from PSP encounters that share the same orbital parameters. The resulting background-removed data products unveil the morphology and dynamics of the large-scale K-corona with superb clarity and demonstrate that the scientific interpretation of the coronal scene in WISPR images relies on the complementary vision provided by the existing methodologies.