Sazuna/UAT_keywords huggingface dataset:
Tuning the Legacy Survey of Space and Time Observing Strategy for Solar System Science: Incremental Templates in Year 1. The Vera C. Rubin Observatory is due to commence the 10 yr Legacy Survey of Space and Time (LSST) at the end of 2025. To detect transient/variable sources and identify solar system objects (SSOs), the processing pipelines require templates of the static sky to perform difference imaging. During the first year of the LSST, templates must be generated as the survey progresses; otherwise, SSOs cannot be discovered nightly. The incremental template generation strategy has not been finalized; therefore, we use the Metric Analysis Framework (MAF) and a simulation of the survey cadence (one_snap_v4.0_10yrs) to explore template generation in Year 1. We have assessed the effects of generating templates over timescales of days–weeks, when at least four images of sufficient quality are available for ≥90% of the visit. We predict that SSO discoveries will begin ∼2–3 months after the start of the survey. We find that the ability of the LSST to discover SSOs in real time is reduced in Year 1. This is especially true for detections in areas of the sky that receive fewer visits, such as the North Ecliptic Spur (NES), and in less commonly used filters, such as the u and g bands. The lack of templates in the NES dominates the loss of real-time SSO discoveries; across the whole sky the MAF main-belt asteroid (MBA) discovery metric decreases by up to 63% compared to the baseline observing strategy, whereas the metric decreases by up to 79% for MBAs in the NES alone.

Keywords: Surveys, Small Solar System bodies, Solar system astronomy
BCDDM: Branch Correction Denoising Diffusion Model for Black Hole Image Generation. The properties of black holes and accretion flows can be inferred by fitting Event Horizon Telescope data to simulated images generated through general relativistic ray tracing (GRRT). However, due to the computationally intensive nature of GRRT, the efficiency of generating specific radiation flux images needs to be improved. This paper introduces the Branch Correction Denoising Diffusion Model (BCDDM), a deep learning framework that synthesizes black hole images directly from physical parameters. The model incorporates a branch correction mechanism and a weighted mixed-loss function to enhance accuracy and stability. We have constructed a data set of 2157 GRRT-simulated images for training the BCDDM, which spans seven key physical parameters of the radiatively inefficient accretion flow model. Our experiments show a strong correlation between the generated images and their physical parameters. By enhancing the GRRT data set with BCDDM-generated images and using ResNet50 for parameter regression, we achieve significant improvements in parameter prediction performance. BCDDM offers a novel approach to reducing the computational costs of black hole image generation, providing a faster and more efficient pathway for data set augmentation, parameter estimation, and model fitting.

Keywords: Astronomy image processing, General relativity, Astrophysical black holes, Neural networks
Inductively Coupled Radio-frequency Plasmas as Solar Vacuum Ultraviolet Simulators. The solar vacuum ultraviolet (VUV) is relevant to a variety of astrophysical phenomena. Radio-frequency (RF) plasmas of H2 and H2/inert gas mixtures are convenient sources of VUV for laboratory simulations. In previous work, we made extensive measurements of the emission spectra for the full range of H2 plasmas mixed with He, Ne, and Ar. In this work, we examine these data in the context of simulating the solar VUV. It was found that a gas mixture of 35% H2 : 65% Ar at 20 mTorr best mimics the solar spectrum in the range 110–160 nm. Spatially resolved optical measurements were used to estimate the spatial distribution of the plasma emissivity, and compared with photodiode measurements to estimate the total power emitted from the plasma in the VUV. At 400 W RF input power, approximately 32% (126 W) was emitted in the VUV spectral range, attenuated to 84.8 W by the MgF2 window. Fluxes in the range 10–40 mW cm‑2 were detected, varying with distance from the source aperture, enabling accelerated testing in a simulated space environment with up to 4000× equivalent solar VUV fluxes.

Keywords: Laboratory astrophysics, Solar ultraviolet emission, Plasma physics
PANCAKE: A Python-based Numerical Color─Magnitude Diagram Analysis Package. Stellar populations serve as a fossil record of galaxy formation and evolution, providing crucial information about the history of star formation and galaxy evolution. The color─magnitude diagram (CMD) stands out as the most accurate tool currently available for inferring the star formation histories (SFHs) of nearby galaxies with stellar-resolved multiband data. The launch of new space telescopes, including JWST, EUCLID, and the upcoming CSST and Roman, will significantly increase the number of stellar-resolved galaxies over the next decade. A user-friendly and customizable CMD-fitting package would be valuable for galaxy evolution studies with these data. We have developed the open-source Python-bAsed Numerical Color─magnitude diagram Analysis pacKagE (PANCAKE), which is fast and accurate in determining SFHs and stellar population parameters for nearby galaxies. We have validated our method via a series of comprehensive tests. First, PANCAKE performs well on mock data, while the random and systematic uncertainties are quantified. Second, PANCAKE performs well on observational data containing a star cluster and 38 dwarf galaxies (50 fields). Third, the star formation rates (SFRs) from PANCAKE are consistent with the SFRs determined with far-ultraviolet photometry. To ensure compatibility and accuracy, we have included isochrone libraries generated using PARSEC for most of the optical and near-infrared filters used by space telescopes such as the Hubble Space Telescope, JWST, and the upcoming CSST.

Keywords: Stellar populations, Hertzsprung Russell diagram, Galaxy stellar content
Radiative Parameters for Odd-parity Levels of V II. Experimental radiative lifetimes for 27 odd-parity levels of singly ionized vanadium (V II) ranging in energy from 34,592 to 62,286 cm‑1 are measured by the time-resolved laser-induced fluorescence technique, and the lifetime results of 23 of these levels are reported for the first time as far as we know. Branching fractions are also determined using the downloaded archival spectral data recorded by the Fourier transform spectrometer at the National Solar Observatory on Kitt Peak, USA (https://nso.edu/data/historical-archive/). And then transition probabilities and oscillator strengths of 107 transitions concerning 24 levels are derived by combining these lifetime and branching fraction results. The obtained results are compared with previously reported results available in the literature.

Keywords: Atomic spectroscopy, Transition probabilities, Atomic data benchmarking
Insights into the X-Ray Burst Pulse Morphology from SGR J1935+2154. SGR J1935+2154 is an extremely active magnetar and the source of the first fast radio burst in the Milky Way, characterized as a magnetar X-ray burst, but whether the temporal features of this magnetar are specific or not is not well known. Based on data from the Fermi Gamma-ray Burst Monitor (GBM), Gravitational Wave High-energy Electromagnetic Counterpart All-Sky Monitor (GECAM), and Hard X-ray Modulation Telescope (HXMT), we investigate the temporal properties of pulses and bursts, covering parameters such as the minimum variability timescale (MVT), duration, rise/decay/waiting times, skewness, peakedness, pulse width, and number of pulses. Except for the number of pulses, all parameters follow the log-Gaussian distribution. In the GBM and GECAM data, MVT and duration exhibit a negative correlation, while the number of pulses and duration (or MVT) exhibit a positive (or negative) correlation. Universal power-law relationships exist among parameters, for example, rise time is positively correlated with decay time, while decay/rise time (or width) and peakedness show a negative correlation. We did not find a significant difference compared with another magnetar, SGR J0501+4516. Finally, the parameters observed by GBM and GECAM show no significant differences, but some parameters observed by HXMT differ from both, suggesting that they may originate from a different physical mechanism.

Keywords: Magnetars
The Extended Atlas of Low-resolution Spectra from the Infrared Astronomical Satellite. We present an updated atlas of spectra from the Low-Resolution Spectrometer (LRS) on the Infrared Astronomical Satellite (IRAS), which took spectra from 7.67 to 22.73 μm with a spectral resolving power (λ/∆λ) of 20─60. The updated atlas includes 11,238 spectra, including 5425 spectra published in the original LRS Atlas, 5796 spectra published in three later papers, and 17 spectra previously available online but not published. The updated atlas has significantly more sources close to the Galactic plane than the original atlas. We have applied an improved spectral correction to remove an artifact at 8 μm in the original database. While the IRAS mission flew over 40 yr ago, the extended LRS atlas remains the single most complete database of mid-infrared spectra of nearby and bright objects in the Galaxy.

Keywords: Infrared spectroscopy
Mock Observations: Morphological Analysis of Galaxies in TNG100 Simulations. In this study, we investigate the morphology of galaxies in the TNG100 simulation by applying mock observation techniques and compare the results with the observational data from the Sloan Digital Sky Survey (SDSS). By employing a hierarchical convolutional neural network (CNN) approach, we classify galaxies into four morphological categories (ellipticals, S0/a, Sab/Sb, and Sc/Sd/irregulars). Our findings show that the morphological characteristics of the mock-observed galaxy samples closely match those observed in the SDSS, successfully reproducing key features such as distinct parameter distributions for different types. However, some discrepancies are identified: notably, a significant lack of early-type galaxies (ETGs) in the dwarf galaxy regime (M* &lt; 1010M⊙) and minimal distinction between Sab/Sb and Sc/Sd/irregular galaxies in the mock-observed samples, unlike the clear differences seen in actual observations. These divergences may stem from simulation properties such as elevated star formation efficiency at the low-mass end or resolution limits. Observational effects, including the impact of the point-spread function, sky background, and instrumental noise, can independently cause approximately 7.87% morphological misclassifications by our CNN model. Compared to previous studies using gravity-based definitions of galaxies that failed to clearly distinguish the parameter distributions of ETGs versus late-type galaxies, our brightness-based sample definition method better recovers the observed morphological parameter distributions, especially their distinct differences. Our study underscores that, alongside mock observations, employing galaxy segmentation methods consistent with observational practices is crucial for appropriately recovering realistic morphological parameters from simulations and enabling fair comparisons. A catalog with morphological parameters is provided for a further analysis.

Keywords: Galaxy structure
Galaxy Morphology Classification via Deep Semisupervised Learning with Limited Labeled Data. Galaxy morphology classification plays a crucial role in understanding the structure and evolution of the Universe. With galaxy observation data growing exponentially, machine learning has become a core technology for this classification task. However, traditional machine learning methods predominantly rely on supervised learning frameworks, and their dependence on large amounts of labeled samples limits practical applications. To address this challenge, we propose an innovative hybrid semisupervised model, Wasserstein GAN for galaxy classification (GC-SWGAN), designed to tackle galaxy morphology classification under conditions of limited labeled data. This model integrates semisupervised generative adversarial networks (GANs) with Wasserstein GAN with gradient penalty, establishing a multitask learning framework. Within this framework, the discriminator and classifier are designed independently while sharing part of the architecture. By collaborating with the generator, the model significantly enhances both classification performance and sample generation capabilities, while also improving convergence and stability during training. Experimental results demonstrate that, on the Galaxy10 DECaLS data set, GC-SWGAN achieves comparable or even superior classification accuracy (exceeding 75%) using only one-fifth of the labeled samples typically required by conventional fully supervised methods. Under identical labeled conditions, the model displays excellent generalization performance, attaining approximately 84% classification accuracy. Notably, in extreme scenarios where only 10% of the data is labeled, GC-SWGAN still achieves high classification accuracy (over 68%), fully demonstrating its stability and effectiveness in low-labeled data environments. Furthermore, galaxy images generated by GC-SWGAN are visually similar to real samples. Our approach provides a new solution to the challenge of needing to manually label large amounts of data in astronomy.

Keywords: Computational astronomy, Astronomy data modeling, Convolutional neural networks, Astronomy data analysis, Ground-based astronomy, Astronomy data visualization, Galaxies
A Full Active Galactic Nuclei Feedback Prescription for Numerical Models: Negative, Positive, and Hot Gas-ejection Modes. We build upon the state-of-the-art semianalytic model FEGA24 for the formation and evolution of galaxies, which integrates the latest prescriptions relevant to galaxy formation and evolution, alongside a comprehensive active galactic nuclei (AGN) feedback model. This model incorporates three modes of feedback: negative (preventing excessive cooling), positive (enhancing star formation), and hot gas ejection (expelling gas beyond the virial radius of halos). These modes operate in a coordinated manner: the negative mode regulates the cooling process, the positive mode promotes bursts of star formation, and the hot gas ejection mode expels gas beyond the virial radius when the AGN is sufficiently powerful. Our updated semianalytic model, FEGA25, retains the qualitative and quantitative consistency of the analyses presented by E. Contini et al. (2024a), while delivering more robust results. Notably, FEGA25 provides a more detailed characterization of the fraction of passive galaxies as a function of stellar mass and redshift, and predicts a main sequence of star-forming galaxies more consistent with observations and a more accurate cosmic star formation rate density with redshift. Moreover, it estimates the fraction of hot gas in halos closer to observed values. These findings underscore the importance of a physical mechanism capable of ejecting hot gas beyond the virialized region of dark matter halos without significantly altering the stellar and cold gas components. Such a mechanism is crucial to ensure the proper functioning of other processes, such as cooling and star formation. Since supernova feedback is already modeled at its maximum efficiency, AGN feedback emerges as the natural candidate for this role.

Keywords: Galaxy formation, Galaxy evolution
Emission-line Stacking of 21 cm Intensity Maps with MeerKLASS: Inference Pipeline and Application to the L-band Deep-field Data. We present a novel analysis of observational systematics through the emission-line stacking of the MeerKLASS L-band deep-field intensity maps, following the detection in MeerKLASS Collaboration et al. A stacking signal is obtained by stacking the 21 cm intensity map cubelets around the galaxy positions from the GAMA survey at 0.39 ≲ z ≲ 0.46. An extensive simulation framework is built to study the viability of the stacking detection, the covariance estimation, and the model inference, which are then applied to the data. The statistical significance of the detection is 8.66σ when averaged into an angular map, and 7.45σ when averaged into a spectrum. The stacked spectrum exhibits an oscillating component of systematics, and we provide evidence that these systematics are a convolutional effect on the map data. The oscillation frequency matches the diffraction from the secondary reflector into the primary beam of the MeerKAT telescope. Bayesian inference can be used to constrain the systematics and the average H I emission of the galaxies. The fitting of the parameters gives a constraint on the systematics frequency  νsys[MHz]=17.90‑4.27+6.53 . The posterior of the systematics amplitude reaches the wide prior and gives  Asys=0.50‑0.33+0.33 . A tentative measurement of the average H I mass of the sources is achieved at  log10[&lt;MHI&gt;/M⊙]=9.84‑0.59+0.48 , which is an underestimation limited by the narrow redshift bin, the strong degeneracy with the systematics, and the low-density galaxy sample. These shortfalls will be resolved for future MeerKLASS data to enable accurate measurements of the H I density through stacking of intensity maps.

Keywords: Observational cosmology, Radio astronomy, H I line emission
COSMIC-L: A Photometric Catalog of Observed Stars in the Large Magellanic Cloud. The Magellanic Clouds are two nearby dwarf irregular galaxies, the study of which can help us in understanding galaxy and stellar evolution. In particular, the Large Magellanic Cloud, the larger one, contains approximately 30 billion stars at various evolutionary stages. In this work, we present a Sloan Digital Sky Survey gri-band photometric analysis based on multiple images acquired with the Dark Energy Camera, installed on the Blanco telescope at Cerro Tololo Inter-American Observatory (Chile). We performed a full image analysis and photometric calibration, resulting in a photometric catalog named COSMIC-L, consisting of 57,997,665 stars, of which 18,676,294 contain estimates for all three gri magnitudes, resulting in a completeness magnitude of ≃21 and a limiting magnitude of ≃22 in all three bands.

Keywords: Photometry, Magellanic Clouds, Catalogs
Observational Parameters of Blue Large-amplitude Pulsators. Blue large-amplitude pulsators (BLAPs) are a recently discovered class of short-period pulsating variable stars. In this work, we present new information on these stars based on photometric and spectroscopic data obtained for known and new objects detected by the Optical Gravitational Lensing Experiment (OGLE) survey. BLAPs are evolved objects with pulsation periods in the range of 3–75 minutes, stretching between subdwarf B-type stars and upper main-sequence stars in the Hertzsprung–Russell diagram. In general, BLAPs are single-mode stars pulsating in the fundamental radial mode. Their phase-folded light curves are typically sawtooth-shaped, but many longer-period objects exhibit an additional bump. The long-term OGLE observations show that the period change rates of BLAPs are usually of the order of 10‑7 yr‑1 and in a quarter of the sample are negative. The spectroscopic data indicate that BLAPs form a homogeneous group in the period, surface gravity, and effective temperature spaces. However, we observe a split into two groups in terms of helium-to-hydrogen content. The atmospheres of He-enriched BLAPs are more abundant in metals (about 5 times) than the atmosphere of the Sun. We discover that BLAPs obey a period–gravity relationship and we use the distance to OGLE-BLAP-009 to derive a period–luminosity relation. Most of the stars observed in the OGLE Galactic bulge fields seem to reside in the bulge, while the remaining objects likely are in the foreground Galactic disk. *Based on photometric observations obtained with the 1.3 m Warsaw telescope and spectroscopic data collected with the 6.5 m Magellan–Baade telescope at the Las Campanas Observatory of the Carnegie Institution for Science.

Keywords: Blue large-amplitude pulsators, Pulsating variable stars
Infrared-selected Active Galactic Nuclei in the Kepler Fields. We utilized the R. Edelson &amp; M. Malkan and D. Stern et al. selection techniques and other methods to identify active galactic nuclei (AGN) candidates that were monitored during the Kepler prime and K2 missions. Subsequent to those observations, we obtained 125 long-slit optical spectra with the Lick 3 m telescope, 58 spectra with the Palomar 5 m telescope, and three spectra with the Keck 10 m telescope to test these identifications. Of these 186 AGN candidates, 105 were confirmed as Type 1 AGN and 35 as Type 2 AGN, while the remaining 46 were found to have other identifications (e.g., stars and normal galaxies). This indicated an overall reliability of ∼75%, while the two main methods had much higher reliability, 87%–96%. The spectra indicated redshifts out to z = 3.4. Then, we examined the AGN sample properties through the Baldwin–Phillips–Terlevich diagram and compared the AGNs' spectral energy distributions with those from the literature. We found that our sample yielded the same AGN population as those identified through other methods, such as optical spectroscopy.

Keywords: Quasars, Active galaxies, Emission line galaxies, Seyfert galaxies, Active galactic nuclei
A Deep Learning Method for Automatic Coronal Mass Ejection Feature Identification. Coronal mass ejections (CMEs), intense solar eruptive phenomena, are the primary drivers of extreme space weather storms on Earth. As space activities become increasingly frequent and infrastructure more reliant on space-based systems, the rapid and accurate detection and tracking of CMEs is critical. Here, we present a deep learning–based algorithm for automated CME feature extraction, comprising four key stages: image preprocessing, segmentation, tracking, and feature extraction. Initially, Solar and Heliospheric Observatory/Large Angle and Spectrometric Coronagraph C2 images undergo basic preprocessing. A segmentation model that integrates convolutional neural networks with transformer architectures is then employed to achieve pixel-level structural segmentation of CMEs. Subsequently, CME events in time series are tracked using Intersection over Union (IoU), Kalman filtering, and the Hungarian algorithm. Finally, critical physical parameters, including onset time, linear velocity, central position angle, and angular width, are extracted. Quantitative evaluations demonstrate that the proposed method performs well in metrics such as pixel accuracy, recall, F1 score, and IoU. Qualitative analyses further reveal the capability of the algorithm to rapidly and precisely identify and track CMEs, including faint early-stage features. This approach is well suited for real-time CME warning and dynamic forecasting, offering robust data support for assessing the potential impact of CMEs on Earth.

Keywords: Solar coronal mass ejections
A Novel Convolutional Neural Network–Long Short-term Memory Model for Interplanetary Coronal Mass Ejection Detection. Current models for detecting interplanetary coronal mass ejections (ICMEs) using spacecraft in situ measurements rely heavily on manual processes, resulting in time-consuming procedures, inconsistencies in identification criteria, and gaps in existing catalogs. Although several machine learning–based detection models have been proposed, there is still a need for improved accuracy. This study presents a convolutional neural network–long short-term memory (CNN-LSTM) model with dynamic loss function, designed to efficiently process complex multidimensional spatiotemporal data. The model leverages LSTM to capture evolving patterns in time series data, while employing a CNN to extract spatial features, making the model highly adaptable. Using in situ data collected by the Wind spacecraft from 1997 October 1 to 2016 January 1, the proposed model demonstrates strong competitiveness. Trained on data from 1998 January 1 to 2009 December 31, 189 out of 230 ICMEs are detected in the test set, achieving an F1 score of 81.29%.

Keywords: Computational methods, Astronomy data analysis