Sazuna/UAT_keywords huggingface dataset:
A Hybrid Scheme for Fuzzy Dark Matter Simulations Combining the Schrödinger and Hamilton–Jacobi–Madelung Equations. This paper introduces a hybrid numerical scheme for the fuzzy dark matter (FDM) model: it combines a wave-based approach to solve the Schrödinger equation using Fourier continuations with Gram polynomials and a fluid-based approach to solve the Hamilton–Jacobi–Madelung equations. This hybrid scheme facilitates zoom-in simulations for cosmological volumes beyond the capabilities of wave-based solvers alone and accurately simulates the full nonlinear dynamics of FDM. We detail the implementation of a Hamilton–Jacobi–Madelung solver, the methodology for phase matching at fluid–wave boundaries, the development of a local pseudospectral wave solver based on Fourier continuations, new grid refinement criteria for both fluid and wave solvers, an interpolation algorithm based on Fourier continuations, and the integration of these building blocks into the adaptive mesh refinement code GAMER. The superiority of the scheme is demonstrated through various performance and accuracy tests, tracking the linear power spectrum evolution in a 10 Mpc h–1 box, and a hybrid cosmological simulation in a 5.6 Mpc h–1 box. The corresponding code is published as part of the GAMER project on https://github.com/gamer-project/gamer.

Keywords: GPU computing, Computational astronomy, Dark matter, Computational methods
Identifying Carbon Star Candidates from LAMOST DR11 and SDSS DR18 Based on a Parallel Feature Recognition Method. Carbon stars are critical for understanding stellar evolution and exploring cosmic chemistry. This paper provides 7809 carbon star candidates identified from the Large-Area Multi-Object Fiber Optic Spectroscopic Telescope (LAMOST) DR11 and Sloan Digital Sky Survey (SDSS) DR18, using a parallel carbon star feature recognition method. This method deploys a parallel multi-interval feature representation model and a k-nearest neighbor classification model, effectively characterizing local and global molecular bands of carbon stars and enhancing feature discrimination, especially for weak features that are highly susceptible to noise interference. The recognition performance F1-score can exceed 96%. Moreover, with parallel strategies for interval representation, the search efficiency has improved. And we construct a new catalog of carbon stars subtypes, including 1616 barium (Ba) stars, 1720 C-H stars, 1621 C-N stars, 1205 C-R stars, and 1647 stars labeled as "Unknown" due to their low signal-to-noise ratios (S/Ns). Compared with the LAMOST DR11 pipeline, carbon stars by L. Li et al., and the SDSS DR18 pipeline, we identify 1403 and 1644 new candidates from LAMOST, and 661 new candidates from SDSS DR18, respectively. In our catalog, the majority of C-N, C-R, and Ba stars from LAMOST are located at low Galactic latitudes with  b&lt;=30 °, while most candidates from SDSS are located at high Galactic latitudes. And the vast majority of Ba, C-H, C-R, and Unknown stars show relatively high effective temperatures. Some carbon stars exhibit stellar activity, potentially influenced by dense interstellar materials, internal magnetic fields, and rotation effects. In the g, r, and i bands, C-N and Unknown stars from LAMOST exhibit lower S/Ns, influenced by high interstellar density at low Galactic latitudes, while Unknown stars from SDSS show higher S/Ns. Among the newly discovered carbon stars, Ba from LAMOST, Unknown from SDSS, and C-R stars have higher S/Ns than other subclasses.

Keywords: Carbon stars, Distributed computing, Catalogs, Astrostatistics
The Chemical Yields of Stars in the Mass Range 9–15 M<SUB>⊙</SUB>. In M. Limongi et al. we presented and discussed the main evolutionary properties and final fate of stars in the mass range 7‑15 M⊙. The evolutions of those models were computed by means of a medium-sized nuclear network that guaranteed a proper calculation of the nuclear energy generation and hence a good modeling of the physical evolution of these stars. In the present paper, we extend this study by computing the detailed chemical yields of stars in the mass range 9‑15 M⊙, i.e., those stars that explode as core-collapse supernovae. The explosive nucleosynthesis is then computed in the framework of the thermal bomb–induced explosion by means of the HYPERION code. We find that: (1) the yields of the intermediate mass elements (i.e., O to P) show a steep decrease as the initial mass decreases; (2) the yields of s-weak components, i.e., those produced by the slow neutron captures from Ga to to the first neutron closure shell, decrease almost linearly as a function of the initial mass with respect to the ones produced by the more massive stars; and (3) the global contribution of the stars in the mass range 9.22‑13 M⊙ to the yields of a generation of massive stars averaged over a standard initial mass function is negligible for essentially all the isotopes. In spite of this, however, the models of stars in this mass range can be fundamental to interpreting the observations of specific supernovae.

Keywords: Massive stars, Supernovae, Nucleosynthesis, Stellar evolution
A Dual Polyphase Decomposition Overlapped Polyphase Filter Bank and Its Implementation in a Field-programmable Gate Array. Real-time processing of ultra-wideband signals requires dividing them into several subbands by using a channelizer filter such as a polyphase filter bank. Original channelizers, like the critically sampling polyphase filter bank (CSPFB), face issues such as subband edge attenuation and spectral aliasing. An overlapped polyphase filter bank (OPFB) addresses these challenges, but in existing OPFB structures, the overlap factor is typically an integer, and the output data rate is usually at least twice that of a CSPFB. In practice, some OPFBs could have an overlap factor of less than 2, but their implementation structures are more complex. In this context, this paper proposes a novel OPFB structure based on dual polyphase decomposition (DPD-OPFB). It can be implemented in FPGAs by using existing IP cores, such as the SMAC finite impulse response filters. This makes it more suitable for FPGA implementation in high-speed broadband signal processing compared to original OPFB structures, especially when the overlap factor is not an integer. The algorithm has been implemented using an FPGA and validated through pulsar astronomical observation experiments. Experimental results demonstrate that the DPD-OPFB effectively mitigates issues in original channelizers, thereby preserving the original data more comprehensively. It significantly simplifies the FPGA implementation of the OPFB with wideband output subbands.

Keywords: Astronomical instrumentation, Astronomy data analysis, Astronomy data acquisition, Pulsars
Probing Obscured Star Formation in Galaxy Clusters Using JWST Medium-band Images: 3.3 μm PAH Emitter Sample in A2744. Star-forming galaxies in galaxy clusters play a crucial role in understanding the advanced stages of galaxy evolution within dense environments. We present a sample of 3.3 μm polycyclic aromatic hydrocarbon (PAH)-bright galaxies in the A2744 galaxy cluster. Using F430M medium-band images, we select PAH emitters in the galaxy cluster, which capture the 3.3 μm PAH emission at the redshift of A2744. Our multiwavelength study demonstrates consistent star formation rates (SFRs) derived from PAH emission and spectral energy distribution fitting, indicating that the 3.3 μm PAH flux estimated from the medium-band image alone can reveal the entirety of star formation, immune to dust obscuration. We find that the PAH emitters are located in relatively low-mass surface density regions of A2744, with SFRs aligning with the field star-forming main sequence at z = 0.3. The PAH emission morphologies show more asymmetry than that of the F444W image when the asymmetry index is &gt;0.4. With these results, we suggest that these star-forming galaxies in A2744 are in the stage of falling into the cluster from the field, and have not been quenched yet. We further explore a potential link between these galaxies and cosmic filaments being accreted onto the cluster, which may channel gas inflows to fuel star formation. JWST medium-band imaging provides a powerful new tool for identifying heavily dust-obscured star-forming populations. Future H I and low-J CO observations should be prioritized to resolve the cold gas kinematics and star formation processes in these systems, which would directly test the role of environmental stripping versus filamentary gas supply.

Keywords: Star formation, Abell clusters, Polycyclic aromatic hydrocarbons, Starburst galaxies
SageNet: Fast Neural Network Emulation of the Stiff-amplified Gravitational Waves from Inflation. Accurate modeling of the inflationary gravitational waves (GWs) requires time-consuming, iterative numerical integrations of differential equations to take into account their backreaction on the expansion history. To improve computational efficiency while preserving accuracy, we present the Stiff-amplified Gravitational-wave Emulator Network (SageNet), a deep learning framework designed to replace conventional numerical solvers (code available at https://github.com/YifangLuo/SageNet). SageNet employs a long short-term memory architecture to emulate the present-day energy density spectrum of the inflationary GWs with possible stiff amplification, ΩGW(f). Trained on a data set of 25,689 numerically generated solutions, SageNet allows accurate reconstructions of ΩGW(f) and generalizes well to a wide range of cosmological parameters; 90.9% of the test emulations with randomly distributed parameters exhibit errors of under 4%. In addition, SageNet demonstrates its ability to learn and reproduce the artificial, adaptive sampling patterns in numerical calculations, which implement denser sampling of frequencies around changes in spectral indices in ΩGW(f). The dual capability of learning both physical and artificial features of the numerical GW spectra establishes SageNet as a robust alternative to exact numerical methods. Finally, our benchmark tests show that SageNet reduces the computation time from tens of seconds to milliseconds, achieving a speedup of ∼104 times over standard CPU-based numerical solvers with the potential for further acceleration on GPU hardware. These capabilities make SageNet a powerful tool for accelerating Bayesian inference procedures for extended cosmological models. In a broad sense, the SageNet framework offers a fast, accurate, and generalizable solution to modeling cosmological observables whose theoretical predictions demand costly differential equation solvers.

Keywords: Cosmology, Early universe, Astronomy data modeling, Inflationary universe, Regression, Astronomy software, Neural networks, Gravitational waves
Measurements of Velocity-dependent Single- and Double-electron-capture Cross Sections for O<SUP>7+</SUP> Ions Impacting He, H<SUB>2</SUB>, N<SUB>2</SUB>, O<SUB>2</SUB>, and CH<SUB>4</SUB>. Charge-exchange processes induced by highly charged ions impacting neutral materials have received considerable attention in astrophysics for decades, as they are believed to be responsible for the widespread ultraviolet and X-ray emissions observed in various astrophysical environments. In this work, with uncertainties of 8.2% in single-electron-capture (SEC) measurements, systematic measurements of single- and double-electron-capture cross sections are performed for O7+ colliding with He, H2, N2, O2, and CH4 in the energy range of 3.06–43.75 keV u‑1, filling critical gaps in the existing experimental database. Clear dependences of SEC on both collision energy and target species are observed, which is interpreted using an absorbing-sphere model.

Keywords: Collision physics, Charge exchange recombination, Atomic data benchmarking, Atomic physics
Forecasting Major Flares Using Magnetograms and Knowledge-informed Features: A Comparative Study of Deep Learning Models with Generalization to Multiple Data Products. In this study, we construct two kinds of data sets from distinct time periods, both comprising line-of-sight magnetograms and knowledge-informed features. We develop eight models for forecasting ≥M-class flares within 24 hr, including the image-based convolutional neural network (CNN), CNN-BiLSTM, CNN-BiLSTM-Attention, and Vision Transformer models, as well as the knowledge-informed neural network, BiLSTM, BiLSTM-Attention, and iTransformer models. We analyze the importance of knowledge-informed features by assessing categorical and probabilistic performance using the true skill statistic (TSS) and the Brier skill score (BSS), respectively. This is the first time the iTransformer has been applied to flare forecasting. Subsequently, we compare the forecasting performance of the eight models. Then, we investigate the generalization ability of the models across three different data products. Finally, we fairly compare the forecasting performance of iTransformer with that of the currently advanced NASA/CCMC models. The major results are as follows. (1) The R_VALUE feature consistently shows the best performance in both categorical and probabilistic forecasting for the knowledge-informed models. (2) The iTransformer yields the highest forecasting performance, with TSS and BSS scores of 0.768 ± 0.072 and 0.513 ± 0.063, respectively. The knowledge-informed deep learning models consistently outperform image-based models. (3) The three image-based models demonstrate good generalization performance in categorical forecasting on Space-weather Helioseismic and Magnetic Imager (HMI) Active Region Patches (SHARP), HMI, and Full-Disk Magnetograph (FMG), while the two knowledge-informed models exhibit excellent generalization performance on SHARP and HMI. This is the first time that FMG magnetograms and knowledge-informed features are used for flare forecasting. Additionally, the five models also demonstrate strong generalization ability on SHARP across different time periods. (4) The iTransformer exhibits superior forecasting performance compared to NASA/CCMC.

Keywords: Solar active region magnetic fields, Solar flares, Neural networks, Solar active regions, Astronomy image processing
Identification of 30,000 White Dwarf–Main-sequence Binary Candidates from Gaia DR3 BP/RP (XP) Low-resolution Spectra. White dwarf–main-sequence (WD–MS) binary systems are essential probes for understanding binary stellar evolution and play a pivotal role in constraining theoretical models of various transient phenomena. In this study, we construct a catalog of WD–MS binaries using Gaia DR3's low-resolution BP/RP (XP) spectra. Our approach integrates a model-independent neural network for spectral modeling with Gaussian process classification to accurately identify WD–MS binaries among over 10 million stars within 1 kpc. This study identifies approximately 30,000 WD–MS binary candidates, including 1700 high-confidence systems confirmed through spectral fitting. Our technique is shown to be effective at detecting systems where the MS star dominates the spectrum—cases that have historically challenged conventional methods. Validation using Galaxy Evolution Explorer photometry reinforces the reliability of our classifications: 70% of candidates with an absolute magnitude MG &gt; 7 exhibit UV excess, a characteristic signature of WD companions. Our all-sky catalog of WD–MS binaries expands the available data set for studying binary evolution and WD physics and sheds light on the formation of WD–MS binaries.

Keywords: Low mass stars, White dwarf stars, Gaia, Binary stars
TESS Light Curves of Cataclysmic Variables. VI. Intermediate Polars. 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 &amp; 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 (&lt;1 day) bursts, coherent variations of an unidentified origin, and other sometimes enigmatic phenomena.

Keywords: DQ Herculis stars, Cataclysmic variable stars, Binary stars, Variable stars
Nuclear Neural Networks: Emulating Late Burning Stages in Core-collapse Supernova Progenitors. One of the main challenges in modeling massive stars to the onset of core collapse is the computational bottleneck of nucleosynthesis during advanced burning stages. The number of isotopes formed requires solving a large set of fully coupled stiff ordinary differential equations, making the simulations computationally intensive and prone to numerical instability. To overcome this barrier, we design a nuclear neural network (NNN) framework with multiple hidden layers to emulate nucleosynthesis calculations and conduct a proof of concept to evaluate its performance. The NNN takes the temperature, density, and composition of a burning region as input and predicts the resulting isotopic abundances along with the energy generation and loss rates. We generate training sets for initial conditions corresponding to oxygen core depletion and beyond using large nuclear reaction networks, and compare the predictions of the NNNs to results from a commonly used small net. We find that the NNNs improve the accuracy of the electron fraction by 280%–660%, the average atomic and mass numbers by 150%–360%, and the nuclear energy generation by 250%–750%, consistently outperforming the small network across all time steps. They also achieve significantly better predictions of neutrino losses on relatively short timescales, with improvements ranging from 100% to 1,000,000%. While further work is needed to enhance their accuracy and applicability to different stellar conditions, integrating NNN-trained models into stellar evolution codes is promising for facilitating the large-scale generation of core-collapse supernova progenitors with higher physical fidelity.

Keywords: Massive stars, Nucleosynthesis, Core-collapse supernovae
The TESS Ten Thousand Catalog: 10,001 Uniformly Vetted and Validated Eclipsing Binary Stars Detected in Full-frame Image Data by Machine Learning and Analyzed by Citizen Scientists. 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″).

Keywords: Eclipsing binary stars, Binary stars
The FAST Globular Cluster Pulsar Survey (GC FANS). By 2025 January, 60 pulsars were discovered by the Five-hundred-meter Aperture Spherical radio Telescope globular cluster (GC) pulsar survey (GC FANS), with spin periods spanning 1.98–3960.72 ms. Of these, 55 are millisecond pulsars (MSPs; P &lt; 30 ms), while 35 are binaries with orbital periods spanning 0.12–466.47 days. This paper describes GC FANS, a deep, thorough search for pulsars in 41 GCs in the FAST sky (‑14° &lt; δ &lt; 65°), and describes new discoveries in 14 of them. We present updated timing solutions for M92A, NGC 6712A, M71A, and M71E, all of which are "spider" pulsars with short orbital periods. We present new timing solutions for M71B, C, and D. With orbital periods of ∼466 and 378 days, M71B and M71C are the widest known GC binaries; these systems resemble the normal wide MSP-He WD systems in the Galactic disk. With a spin period of 101 ms, M71D is in an eccentric (e ∼ 0.63) orbit with an 11 day period and a massive companion; the system has a total mass of 2.63 ± 0.08 M⊙. These features and its large characteristic age suggest it is a double neutron star system (DNS) formed via massive binary evolution early in the cluster's history, akin to Galactic disk DNSs—unlike other candidate GC DNSs, which typically form dynamically. A comparative analysis of GC pulsar populations within FAST's sky reveals that most clusters (10 of 14) resemble the Galactic disk MSP population, likely due to lower stellar densities.

Keywords: Globular star clusters, Radio telescopes, Binary pulsars, Millisecond pulsars
Identifying Ring Galaxies in DESI Legacy Imaging Surveys Using Machine Learning Methods. The formation and evolution of ring structures in galaxies are crucial for understanding the nature and distribution of dark matter, galactic interactions, and the internal secular evolution of galaxies. However, the limited number of existing ring galaxy catalogs has constrained deeper exploration in this field. To address this gap, we introduce a two-stage binary classification model based on the Swin Transformer architecture to identify ring galaxies from the DESI Legacy Imaging Surveys. This model first selects potential candidates and then refines them in a second stage to improve classification accuracy. During model training, we investigated the impact of imbalanced data sets on the performance of the two-stage model. We experimented with various model combinations applied to the data sets of the DESI Legacy Imaging Surveys DR9, processing a total of 573,668 images with redshifts ranging from z_spec = 0.01–0.20 and magr &lt; 17.5. After applying the two-stage filtering and conducting visual inspections, the overall precision of the models exceeded 64.87%, successfully identifying a total of 8052 newly discovered ring galaxies. With our catalog, the forthcoming spectroscopic data from DESI will facilitate a more comprehensive investigation into the formation and evolution of ring galaxies.

Keywords: Astronomy data analysis, Ring galaxies, Galaxy evolution, Galaxies