Sazuna/UAT_keywords huggingface dataset:
Deep Active Learning–Based Classification of Solar Radio Spectrogram Data. The study of solar burst activity can provide early warnings for the environmental protection of the solar–terrestrial space environment. With the improvement of solar radio observation techniques, observation devices have generated enormous amounts of observation data. To solve the shortcomings of time-consuming and error-prone manual recognition, researchers have begun to use deep learning to recognize and automatically classify solar radio outbursts. Deep learning will depend on a large number of labeled samples; however, the labeling of samples requires a lot of time and manual labor. This leads to low efficiency. In addition, the labeled samples are not all valuable samples, so it is necessary to improve the effectiveness of the labeled samples and select the high-value samples. The occurrence of active-learning techniques provides an opportunity to solve this problem. In this study, we developed a progressive deep convolutional generative adversarial network model. Then, we combined it with deep active learning to complete the automatic classification of solar radio spectrum data. We used solar radio spectrum data from the Chashan Observatory (CSO) of Shandong University and Learmonth Observatory in Australia. The results show that the method proposed in this paper can achieve high accuracy in the automatic recognition of solar radio spectrum data and solve the time-consuming problem of labeling a huge number of data samples. Finally, we applied the results to the CSO and realized the automatic recognition of solar radio spectral data.

Keywords: Solar radio telescopes
Morpho-photometric Classification of KiDS DR5 Sources Based on Neural Networks: A Comprehensive Star─Quasar─Galaxy Catalog. 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 (ZYHJKs) 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.

Keywords: Galaxies, A stars, Classification, Neural networks, Surveys, Quasars, Catalogs
Solar Flare Prediction Using Long Short-term Memory (LSTM) and Decomposition-LSTM with Sliding Window Pattern Recognition. We investigate the use of long short-term memory (LSTM) and decomposition-LSTM (DLSTM) networks, combined with an ensemble algorithm, to predict solar flare occurrences using time series data from the GOES catalog. The data set spans from 2003 to 2023 and includes 151,071 flare events. Among approximately possible patterns, 7552 yearly pattern windows are identified, highlighting the challenge of long-term forecasting due to the Sun's complex, self-organized-criticality-driven behavior. A sliding window technique is employed to detect temporal quasi-patterns in both irregular and regularized flare time series. Regularization reduces complexity, enhances large flare activity, and captures active days more effectively. To address class imbalance, resampling methods are applied. LSTM and DLSTM models are trained on sequences of peak fluxes and waiting times from irregular time series, while LSTM and DLSTM, integrated with an ensemble approach, are applied to sliding windows of regularized time series with a 3 hr interval. Performance metrics, particularly the true skill statistic (0.74), recall (0.95), and the area under the curve (AUC = 0.87) in the receiver operating characteristic, indicate that DLSTM with an ensemble approach on regularized time series outperforms other models, offering more accurate large-flare forecasts with fewer false errors compared to models trained on irregular time series. The superior performance of DLSTM is attributed to its ability to decompose time series into trend and seasonal components, effectively isolating random noise. This study underscores the potential of advanced machine learning techniques for solar flare prediction and highlights the importance of incorporating various solar cycle phases and resampling strategies to enhance forecasting reliability.

Keywords: Neural networks, Solar flares, Time series analysis, Solar x-ray flares
Identification of Ion-kinetic Instabilities in Hybrid-PIC Simulations of Solar Wind Plasma with Machine Learning. Analysis of ion-kinetic instabilities in solar wind plasmas is crucial for understanding energetics and dynamics throughout the heliosphere, as evident from spacecraft observations of complex ion velocity distribution functions (VDFs) and ubiquitous ion-scale kinetic waves. In this work, we explore machine learning (ML) and deep learning (DL) classification models to identify unstable cases of ion VDFs driving kinetic waves. Using 34 hybrid particle-in-cell simulations of kinetic protons and α-particles initialized using plasma parameters derived from solar wind (SW) observations, we prepare a data set of nearly 1600 VDFs representing stable/unstable cases and associated plasma and wave properties. We compare feature-based classifiers applied to VDF moments, such as support vector machine and random forest (RF), with DL convolutional neural networks (CNNs) applied directly to VDFs as images in the gyrotropic velocity plane. The best-performing classifier, RF, has an accuracy of 0.96 ± 0.01, and a true skill score of 0.89 ± 0.03, with the majority of missed predictions made near stability thresholds. We study how the variations of the temporal derivative thresholds of anisotropies and magnetic energies, and sampling strategies for simulation runs, affect classification. CNN-based models have the highest accuracy of 0.88 ± 0.18 among all considered if evaluated on the runs entirely not used during the model training. The addition of the E⊥ power spectrum as an input for the ML models leads to the improvement of instability analysis for some cases. The results demonstrate the potential of ML and DL for the detection of ion-scale kinetic instabilities using spacecraft observations of SW and magnetospheric plasmas.

Keywords: Random Forests, Convolutional neural networks, The Sun, Solar wind
A 50–55 GHz Millimeter-wave Radiometer Spectrometer for Solar Flare Detection. During solar flare eruptions, millimeter-wave radiation is emitted, which is highly efficient and sensitive to high-energy electrons, allowing for the extraction of unique magnetic field information. Therefore, we have developed a 50–55 GHz solar millimeter-wave radiometer system. The system employs a 50 cm diameter Cassegrain antenna to receive circularly polarized solar radiation signals. These signals enter the analog front-end system, where they undergo power division, filtering, and detection operations, resulting in voltage signals. Subsequently, the signals are processed by the digital receiver for analog-to-digital conversion and smoothing and are finally transmitted to the host computer via the RS422 protocol to display the intensity of solar radiation. The system's performance metrics are as follows: a noise figure of &lt;2.5 dB, system linearity ≥0.9999, a time resolution range of 0.001–1 s, and a dynamic range exceeding 30 dB. The system began routine observations in 2024 October and successfully captured the world's first 50 GHz band solar flare data in December. Currently, the system is effectively observing during the 25th solar activity maximum period, which is expected to provide valuable data for solar physics research.

Keywords: Solar radio telescopes, Solar radio flares, Radio bursts
PROTOCALC, a W-band Polarized Calibrator for Cosmic Microwave Background Telescopes: Application to Simons Observatory and CLASS. Current- and next-generation cosmic microwave background (CMB) experiments will measure polarization anisotropies with unprecedented sensitivities. The need for high precision in these measurements underscores the importance of gaining a comprehensive understanding of instrument properties, with a particular emphasis on the study of the beam properties, and especially their polarization characteristics and the measurement of the polarization angle. In this context, a major challenge lies in the scarcity of millimeter polarized astrophysical sources with sufficient brightness and calibration knowledge to meet the stringent accuracy requirements of future CMB missions. This led to the development of a drone-borne calibration source designed for the frequency band centered on approximately 90 GHz, matching a commonly used channel in ground-based CMB measurements. The Prototype Calibrator for Cosmology, PROTOCALC, has undergone thorough in-lab testing, and its properties have been subsequently modeled through simulation software integrated into the standard Simons Observatory analysis pipeline. Moreover, the PROTOCALC system has been tested in the field, having been deployed twice on calibration campaigns with CMB telescopes in the Atacama Desert. The data collected constrain the roll angle of the source with a statistical accuracy of 0 .° 045.

Keywords: Calibration, Astronomical techniques, Astronomical instrumentation
Asteroseismic Characterization of 142 Solar-like Oscillators Hosting Confirmed Exoplanets. We present the largest catalog of exoplanet host stars with solar-like oscillations compiled to date, providing a powerful resource for refining stellar and planetary characterization via asteroseismology. By combining high-precision photometric data from Kepler and the Transiting Exoplanet Survey Satellite with the automated pySYD pipeline, we systematically searched thousands of confirmed host stars for solar-like pulsations. From this process, we identified 174 stars displaying distinct oscillatory signatures, of which 142 offered sufficiently high signal-to-noise ratios and frequency resolution. For these 142 targets, we derived global stellar parameters—such as mass, radius, and surface gravity—confirming consistency with published spectroscopic data. This synergy between asteroseismic and spectroscopic methods fosters robust constraints on stellar properties and enables more accurate planetary analyses, while also highlighting the value of long-baseline photometry for probing stellar interiors and bridging observational gaps across various evolutionary phases. We additionally draw attention to 32 host stars that exhibit low-frequency pulsations but remain only partially characterized, primarily due to their large radii or limited observational baselines. These systems underscore the potential for future observations to explore interactions between planets and giant host stars, as well as broader implications for planetary evolution. Altogether, the curated stellar parameters and asteroseismic data establish a foundation for deeper investigations into planetary system architectures and the long-term evolution of exoplanets under varying stellar conditions.

Keywords: Asteroseismology, Exoplanets, Planet hosting stars
CRAFTS for H I Cosmology. I. Data Processing Pipeline and Validation Tests. We present the calibration procedures and validation of source measurement with the data of the Commensal Radio Astronomy FAST Survey for H I intensity mapping by the Five-hundred-meter Aperture Spherical Radio Telescope. Using a 70 hr drift-scan observation with the L-band (1.05–1.45 GHz) 19 beam receiver, we obtain the data covering a 270 deg2 sky area. We employ both the pulsar backend and the spectrum backend to calibrate the spectral time-ordered data (TOD) before projecting them onto HEALPix maps. We produce calibrated TOD with a frequency resolution of 30 kHz and time resolution of 1 s and the map data cube with a frequency resolution of 30 kHz and spatial resolution of 2.95 arcmin2. We examine the pointing errors, noise overflow, radio-frequency interference (RFI) contamination, and their effect on the data quality. The resulting noise level is ∼5.7 mJy for the calibrated TOD and 1.6 mJy for the map, consistent with the theoretical predictions within 5% at RFI-free channels. We also validate the data by principal component analysis and find that the residual map looks thermal noise dominated after removing 30 modes. We identify 447 isolated bright continuum sources in our data matching the NRAO VLA Sky Survey catalog, with relative flux error of 8.3% for TOD and 6.6% for the map level. We also measure the H I emission of 90 galaxies with redshift z &lt; 0.07 and compare them with H I-MaNGA spectra, yielding an overall relative H I integral flux error of 16.7%. These results provide an important first step in assessing the feasibility of conducting cosmological H I detection with CRAFTS.

Keywords: Observational cosmology
Revisiting the Dynamical Model for the Evolution of Hot Spots and Its Cosmological Application. Compact symmetric objects (CSOs) typically refer to small-scale extragalactic radio sources (usually &lt;1 kpc) that exhibit strong radio emission on both sides (hot spots) and have a symmetric morphology. In this paper, we revisit the dynamical model for the evolution of hot spots in powerful radio sources, based on the latest bona fide CSO sample with well-defined statistical characteristics. Specifically, we focus on the subsample of CSOs that exhibit edge brightening, high luminosity, and prominent hot spots, and furthermore derive their luminosity and linear sizes between tens to hundreds of parsecs. The luminosity–linear size relation highlights the significant potential of CSOs in the study of modern cosmology. Finally, we investigate the possibility of testing the dynamical hot spot model with a much higher accuracy using CSOs observed in future very long baseline interferometry surveys. It is shown that our method could provide an independent test of the cosmic distance duality relation, reinforcing the potential of CSOs acting as a reliable cosmological probe.

Keywords: Radio active galactic nuclei, Radio hot spots, Observational cosmology
Simons Observatory: Characterization of the Large Aperture Telescope Receiver. The Simons Observatory (SO) is a ground-based cosmic microwave background (CMB) survey experiment that currently consists of three 0.42 m small-aperture telescopes and one 6 m large-aperture telescope (LAT), located at an elevation of 5200 m in the Atacama Desert in Chile. At the LAT's focal plane, SO will install &gt;62,000 transition-edge sensor detectors across 13 optics tubes (OTs) within the Large Aperture Telescope Receiver (LATR), the largest cryogenic camera ever built to observe the CMB. Here we report on the validation of the LATR in the laboratory and the subsequent dark testing and validation within the LAT. We show that the LATR meets cryogenic, optical, and detector specifications required for high-sensitivity measurements of the CMB. At the time of writing, the LATR is installed in the LAT with six OTs (corresponding to &gt;31,000 detectors), and the LAT mirrors and remaining seven OTs are undergoing development.

Keywords: CMBR detectors, Cosmic microwave background radiation, Astronomical instrumentation, Cosmic background radiation
The Karl G. Jansky Very Large Array Local Group L-Band Survey (LGLBS). We present the Local Group L-Band Survey, a Karl G. Jansky Very Large Array (VLA) survey producing the highest-quality 21 cm and 1–2 GHz radio continuum images to date, for the six VLA-accessible, star-forming, Local Group galaxies. Leveraging the VLA's spectral multiplexing power, we simultaneously survey the 21 cm line at high 0.4 km s‑1 velocity resolution, the 1–2 GHz polarized continuum, and four OH lines. For the massive spiral M31, the dwarf spiral M33, and the dwarf irregular galaxies NGC 6822, IC 10, IC 1613, and the Wolf–Lundmark–Melotte Galaxy, we use all four VLA configurations and the Green Bank Telescope to reach angular resolutions of &lt;5″ (10–20 pc) for the 21 cm line with &lt;1020 cm‑2 column density sensitivity, and even sharper views (&lt;2″; 5–10 pc) of the continuum. Targeting these nearby galaxies (D ≲ 1 Mpc) reveals a sharp, resolved view of the atomic gas, including 21 cm absorption, and continuum emission from supernova remnants and H II regions. These data sets can be used to test theories of the abundance and formation of cold clouds, the driving and dissipation of interstellar turbulence, and the impact of feedback from massive stars and supernovae. Here, we describe the survey design and execution, scientific motivation, data processing, and quality assurance. We provide a first look at and publicly release the wide-field 21 cm H I data products for M31, M33, and four dwarf irregular targets in the survey, which represent some of the highest-physical-resolution 21 cm observations of any external galaxies beyond the LMC and SMC.

Keywords: Local Group, Triangulum Galaxy, Interstellar medium, Radio interferometry, Andromeda Galaxy
A White Dwarf Catalog from LAMOST DR11 Using Deep Learning. White dwarfs, also referred to as degenerate dwarfs, represent the ultimate fate for the majority of stars in the Universe, unless their mass is large enough for them to evolve into black holes or neutron stars. In recent years, the astronomical community has experienced an unprecedented influx of data from initiatives like LAMOST, the Sloan Digital Sky Survey, and Gaia. This surge in observational data has underscored the pressing need for efficient analysis and categorization of newly discovered white dwarfs. Traditional methods for star classification heavily rely on manual visual identification, which is a laborious and time-consuming process. This study presents a pioneering approach by integrating 1D spectral data and 2D spectral images to enhance the accuracy of white dwarf classification. It involves the development of convolutional neural networks for processing 1D spectral data, alongside residual neural networks for handling 2D spectral images. The synergy between these methodologies significantly improves the accuracy of white dwarf classification. Our model represents a substantial advancement in white dwarf classification accuracy, alleviating the substantial manual workload and time required for classification, and aligning with the requirements of contemporary astronomy characterized by the era of big data. Our two-class classification model with multimodal data achieved an accuracy rate of 98.69% on the test set. Subsequently, we design a white dwarf identification flowchart applied for the LAMOST Data Release 11 low-resolution spectra. Finally, we compile a comprehensive white dwarf catalog, which includes 362 new white dwarfs, alongside 4456 known white dwarfs.

Keywords: Stellar classification, White dwarf stars, Convolutional neural networks, Astronomy databases
Asteroseismology of 687 TESS Red Giants: Individual Frequencies and Asymptotic Parameters. The individual modes and asymptotic parameters are important characteristics of stellar oscillation. Using TESS 2 minute cadence data from Sectors 1 to 75, we perform asteroseismic analysis of 687 red giants, and provide their oscillation mode parameters. The mode parameters, including frequencies νn,l, amplitudes An,l, and line widths Γn,l, are obtained through Markov Chain Monte Carlo fitting. The median uncertainty for frequency is 0.047 μHz. Using the radial mode frequencies, we calculate the values of Δν, and derive asymptotic parameters (ϵp, ϵc, δν0l, q, and ΔΠ1) based on l = 0, 1, 2 modes. For red giants with Δν &gt; 15.6 μHz, there is a weaker correlation between δν02 and Δν, and a stronger mass dependence, compared to stars with lower Δν. Additionally, the ratio δν02/Δν presents a pronounced increase with Δν decrease, as these stars undergo rapid core contraction and their convective envelopes extend deep into the interior. At this stage, the ratio δν02/Δν may serve as a robust indicator for diagnosing the location of the convective boundary. Furthermore, the ϵc values can be a potential index to distinguish red clump (RC) and secondary RC stars from red giant branch stars in the TESS field.

Keywords: Asteroseismology, Low mass stars, Giant stars
Searching for Dark Galaxies with H I Detection from the Arecibo Legacy Fast ALFA (ALFALFA) Survey. We present a catalog of 142 dark galaxy candidates in a region covered by the Arecibo Legacy Fast ALFA (ALFALFA) survey. We start with 344 ALFALFA H I sources without optical counterparts and remove those that do not seem to have a dark galaxy origin. To do that, we first eliminate 83 sources that are known H I clouds probably formed from tidal interactions between galaxies and 13 sources that have optical counterparts. We then remove 56 sources located near other H I sources, which are likely to be H I clouds. We further exclude 10 sources that have nearby H I sources within the ALFALFA beam and 40 sources potentially associated with nearby galaxies. We perform visual inspection of optical images from the DESI Legacy Imaging Survey with an r-band surface brightness limit of ∼28.5 mag arcsec‑2 as well as near-ultraviolet images from GALEX to confirm the absence of stellar emission. We additionally inspect infrared images from the Wide-field Infrared Survey Explorer and AKARI for dust emission. As a result, we identify 142 dark galaxy candidates and analyze their physical properties by comparing them with luminous galaxies. We find that the dark galaxy candidates generally have smaller dynamical masses, higher H I-to-dynamical mass ratios, and are located in less dense regions when compared to luminous galaxies, which is consistent with results from cosmological simulations. This sample provides an important testbed for studying the role of dark matter in galaxy formation and evolution.

Keywords: Galaxy formation, Dwarf galaxies, H I line emission, Galaxy evolution, Dark matter