Sazuna/UAT_keywords huggingface dataset:
Power-spectrum Space Decomposition of Frequency Tomographic Data for Intensity-mapping Experiments. We present a Bayesian framework to establish a power-spectrum space decomposition of frequency tomographic (PSDFT) data for future intensity-mapping (IM) experiments. In contrast to most traditional component separation methods that work in the map domain, this new technique treats multifrequency power spectra as raw data and can reconstruct component power spectra by taking advantage of distinct component correlation patterns in the frequency domain. We validated this new technique for future IM experiments using synthesized mock data that contain bright foreground contaminants, IM signals, and instrumental effects at different frequencies. The PSDFT approach can effectively remove the bright foreground contamination and extract the targeted IM signals using a Bayesian approach in a power-spectrum subspace. This new approach can be directly applied to a broad range of IM analyses and will be well suited to future high-quality IM data sets, providing a powerful tool for future IM surveys.

Keywords: Cosmology, Astronomy data analysis, H I line emission, Baryon acoustic oscillations, Dark energy
Solar Radio Burst Detection Based on Deformable DETR. Solar radio burst (SRB) detection is crucial for solar physics research and space weather forecasting. The main challenges faced are noise interference in the spectrum and the diversity of SRBs. However, most research focuses on classifying whether SRBs exist or detecting a single type of SRB. Existing detection models exhibit deficiencies in the accuracy of SRB detection. Moreover, existing detection models cannot effectively handle background noise interference in solar radio spectrograms and the significant scale variations among different burst types. This paper proposes a high-performance detection model for SRBs based on the Deformable DEtection TRansformer (DETR) called DETR4SBRs. First, this study designed a scale-sensitive attention module to better address the scale variations of SRBs. Subsequently, this study introduced collaborative hybrid auxiliary training to mitigate the positive–negative sample imbalance issue in Deformable DETR. The experimental results demonstrate that the proposed model achieves a mAP@50 of 83.5% and a recall rate of 99.4% on the SRBs data set. Additionally, the model exhibits excellent noise-robust performance and can efficiently detect and locate Type II, III, IV, and V SRBs. The model proposed in this study provides robust support for preliminary SRB data processing and has significant implications for space weather forecasting. The source code and data are available on https://github.com/onewangqianqian/SSA-Co-Deformable-DETR.git, and the software is archived on Zenodo.

Keywords: Detection, Convolutional neural networks, Solar radio emission
Prediction of the Next Solar Rotation Synoptic Maps Using an Artificial Intelligence–based Surface Flux Transport Model. In this study, we develop an artificial intelligence (AI)-based solar surface flux transport (SFT) model. We predict synoptic maps for the next solar rotation (27.2753 days) using deep learning. Our model takes the latest synoptic maps and their sine-latitude grid data as inputs. Synoptic maps, which represent global magnetic field distributions on the solar surface, have been widely used as initial boundary conditions in the Sun and space-weather prediction models. Here we train and evaluate our deep-learning model, based on the Pix2PixCC architecture, using data sets of Solar Dynamics Observatory/Helioseismic and Magnetic Imager, Solar and Heliospheric Observatory/Michelson Doppler Imager, and National Solar Observatory/Global Oscillation Network Group synoptic maps with a resolution of 360 by 180 (longitude and sine latitude) from 1996 to 2023. We present results of our model and compare them with those from the persistent model and the conventional SFT model, including the effects of differential rotation, meridional flow, and diffusion on the solar surface. The average pixel-to-pixel correlation coefficient between the target and our AI-generated data, after 10 by 10 binning with a 10° resolution in longitude, is 0.71. This result is qualitatively similar to the results of the conventional SFT model (0.65–0.68) and better than the results of the persistent model (0.56). Our model successfully generates magnetic features, such as the diffusion of solar active regions and the motions of supergranules. Using synthetic input data with bipolar structures, we confirm that our model successfully reproduces differential rotation and meridional flow. Finally, we discuss the advantages and limitations of our model in view of magnetic field evolution and its potential applications.

Keywords: The Sun, Convolutional neural networks, Astronomy data analysis, Solar magnetic fields
Finding Quasars behind the Galactic Plane. III. Spectroscopic Identifications of ∼1300 New Quasars at ∣b∣ ≤ 20° from LAMOST DR10. Quasars behind the Galactic plane (GPQs) are excellent tracers to probe the chemistry and kinematics of the interstellar/intergalactic medium of the Milky Way along sight lines via absorption line spectroscopy. Moreover, quasars located at low Galactic latitudes will fill the gap in the spatial distribution of known quasars near the Galactic plane, and can be used to construct an astrometric reference frame for accurate measurements of proper motions of stars, and substructures of the Milky Way. We started a survey of background quasars in the low Galactic latitude region with the LAMOST phase II survey in 2017. Quasar candidates have been selected from the optical and infrared photometric data of the Pan-STARRS1 and Wide-field Infrared Survey Explorer surveys based on their variability and color properties. In this paper, we present a sample of 1982 spectroscopically confirmed GPQs with ∣b∣ ≤ 20° based on LAMOST Data Release 10 (DR10). Among them, 1338 are newly discovered. Most GPQs are located around 240° &lt; l &lt; 90°, and the spatial distribution is nonuniform. These GPQs have a magnitude distribution with a peak at i-mag 19.0, and are mostly located around 18.0–19.5 mag. The peak of the redshift distribution is around 1.5, and most of the GPQs have redshifts between 0.3 and 2.5. Our finding demonstrates the potential discovery space for GPQs from spectroscopic surveys and promising applications for future research.

Keywords: Quasars
Atomic Transition Probabilities for UV and Optical Lines of Gd II. We report new branching fraction (BF) measurements for 156 ultraviolet and optical transitions of Gd II. These transitions range in wavelength (wavenumber) from 2574 to 6766 Å (38,838–14,777 cm‑1) and originate in one odd-parity and 11 even-parity upper levels. Nine of the 12 levels, accounting for 126 of the 156 transitions, are studied for the first time. BFs are determined for three levels studied previously for the purpose of comparison. The levels studied for the first time are high lying, ranging in energy from 36,845 to 40,774 cm‑1. The BFs are determined from emission spectra from two different high-resolution spectrometers. These are combined with radiative lifetimes reported in an earlier study to produce a set of transition probabilities and log(gf) values with accuracy ranging from 5% to 30%. Comparison is made to experimental and theoretical transition probabilities from the literature where such data exist. Abundances derived from these new log(gf) values for 21 Gd II lines in two metal-poor stars yield results consistent with previous studies, and they demonstrate that the new log(gf) values can be used in stellar abundance analysis as a self-consistent extension of previous work. *Based in part on observations made with the NASA/ESA Hubble Space Telescope, obtained at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc. under NASA contract NAS5–26555 (program GO-15657). This paper also includes data gathered with the 6.5 m Magellan Telescopes located at Las Campanas Observatory, Chile.

Keywords: Atomic spectroscopy, Transition probabilities, Stellar abundances
Revised Point-spread Functions for the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory. We present revised point-spread functions (PSFs) for the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory. These PSFs provide a robust estimate of the light diffracted by the meshes holding the entrance and focal plane filters and the light that is diffusely scattered over the detector by the microroughness of the mirrors. We first calibrate the diffracted light using flare images. Our modeling of the diffracted light provides reliable determinations of the mesh parameters and finds that about 24%–33% of the collected light is diffracted, depending on the AIA channel. Then, we fit the diffuse scattered light using partially lunar-occulted images. We find that the diffuse scattered light can be modeled as a superposition of two power-law functions that scatter light over the entire detector. The amount of diffuse scattered light ranges from 10% to 35%, depending on the AIA channel. In total, AIA diffracts and diffusely scatters about 37%–55% of the collected light over the detector. When correcting for this, bright image regions increase in intensity by about 30%, dark image regions decrease by up to 90%, and the associated differential emission measure analysis of solar features is affected accordingly. Finally, we compare the image reconstructions using our new PSFs to those from the AIA team and B. Poduval et al. We find that our PSFs outperform the others, better correcting for the flare diffraction pattern and far more accurately predicting long-distance scattered light in lunar occultations.

Keywords: Solar physics, Deconvolution, Solar instruments
ASR: Archival Solar Flares Catalog. Solar flares result from the rapid conversion of stored magnetic energy within the Sun's corona. These energy releases are associated with coronal magnetic loops, which are rooted in dense photospheric plasma and are passively transported by surface advection. Their emissions cover a wide range of wavelengths, with soft X-rays being the primary diagnostic for the past 50 years. Despite the efforts of multiple authors, we are still far from a complete theory capable of explaining the observed statistical and individual properties of flares. Here, we exploit the availability of stable and long-term soft X-ray measurements from NASA's Geostationary Operational Environmental Satellites mission to build a new solar flare catalog, with a novel approach to linking sympathetic events. Furthermore, for the most energetic events since 2010, we have also provided a method to identify the origin of the observed flare and eventual link to the photospheric active region by exploiting the array of instruments on board NASA's Solar Dynamic Observatory. Our catalog provides a robust resource for studying space weather events and training machine learning models to develop a reliable early warning system for the onset of eruptive events in the solar atmosphere.

Keywords: Solar flares, Solar physics, Solar x-ray flares, Astronomical methods, Catalogs
Illuminating Youth: Decades of Mid-infrared Variability and Color Evolution of Young Stellar Objects. The variability of young stellar objects (YSOs) is a crucial tool for understanding the mechanisms driving flux changes. In this study, we present an infrared variability analysis of a large sample of over 20,000 candidate YSOs, using data from the ALLWISE and NEOWISE surveys, which span around a decade with a 6 month cadence. We applied Lomb─Scargle periodogram analysis and linear fitting to the light curves, classifying them into distinct categories: secular (linear, curved, and periodic) and stochastic (burst, drop, and irregular). Our findings show that 5467 (26.2% ± 0.3%) of the sources exhibit variability, with most (19.7% ± 0.3%) showing irregular variations, followed by curved and periodic variations. In addition, 235 burst sources and 122 drop sources were identified. Variability is more pronounced in Class I sources with a higher fraction of variables (36.3% ± 0.6%) compared to Class II (22.1% ± 0.4%) and Class III (22.5% ± 1.0%) sources. The color (W1 − W2) versus magnitude analysis (W2) using linear fitting shows that the "redder-when-brighter" trend is more prevalent (85.4% ± 0.5%) among YSOs. In contrast, the "bluer-when-brighter" (BWB) trend is more common in younger sources compared to more evolved ones, having a BWB fraction of 29.0% ± 1.1% for Class I to 4.0% ± 0.9% for Class III.

Keywords: Young stellar objects, Star formation
Discerning Misclassified Flat-spectrum Radio Quasars from Low-frequency-peaked BL Lacertae Objects. A sample of 312 low-frequency-peaked BL Lacertae objects (LBLs) and 694 flat-spectrum radio quasars (FSRQs) with the parameters of both redshift and gamma-ray photon spectral index (Γγ) is compiled from the Fourth Catalog of Active Galactic Nuclei Data Release 2 from the Fermi Large Area Telescope (LAT). The multiwavelength data of the sample sources are downloaded from the Space Science Data Center, and then matched to the corresponding gamma-ray data from the Fermi-LAT Fourth Source Catalog Data Release 2. The synchrotron radiation peak frequency and Compton dominance (CD) parameters of the sources are obtained by using a log-parabolic model to fit the average-state multiwavelength spectral energy distribution. A support vector machine in the  –Γγ frame is utilized to delineate the optimal boundary between FSRQs and LBL sources. The 1σ position of the Gaussian fitting on the histograms of the Γγ,  , and CD parameter distributions are also introduced. With this criterion, 25 FSRQ candidates are selected from LBL sample sources. The optical spectral identification result confirms that 8 out of 13 candidate sources available with the optical spectral data exhibit the relationship of EW &gt; 5 Å.

Keywords: Flat-spectrum radio quasars, BL Lacertae objects
Solar Cycle Variation of the Mass-loss Rate of Coronal Mass Ejections. Coronal mass ejections (CMEs) are known as the main drivers of the most severe space weather disturbances. Usually, the most severe geomagnetic storms are caused by massive and fast CMEs. The latitudinal dependence of the mass loss of CMEs for different types (regular and specific events) and in different solar cycles is rarely studied. Based on the latest data acquired by the Coordinated Data Analysis Workshop catalog covering solar cycles 23 and 24, we investigated the solar cycle variation of their mass-loss rate in detail. The main results are shown as follows: (1) The temporal distribution of the mass-loss rate is noticeably different from the occurrence rate of CMEs, and this difference might be attributed to the different types. The mass-loss rate of regular CMEs significantly follows the pattern of the solar cycle. (2) The latitudinal evolutions of the mass-loss rate of regular CMEs are indeed different from those of specific CMEs during solar cycles 23 and 24, suggesting that the source region and configuration of regular and specific CMEs differ from each other. (3) Rieger-type periodicity and the quasi-biennial oscillations are found in the long-term evolution of the CME mass-loss rate, but they exhibit different temporal aspects in different subsamples of CMEs. Our results might provide new insight into the solar-cycle dependence of the mass-loss rate of CMEs at different latitudes, and have potential implications for our understanding of the long-term evolution of the mass-loss process on solar-type stars.

Keywords: Solar activity, Solar-terrestrial interactions, Solar coronal mass ejections, Solar cycle
Tiny-scale Properties within the Interstellar Medium toward PSR J1644‑4559. I. Observational Evidence of Turbulence-induced Tiny-scale Atomic Structures. We investigated H I absorption toward a single pulsar, PSR J1644‑4559, and its variability over timescales from days to years, using Murriyang, CSIRO's Parkes Radio Telescope. Our 19 epochs of spectral observations, spanning 1.2 yr with intervals as short as 1 day, provide the most comprehensive cadence coverage for monitoring H I absorption to date. We identified two significant detections of tiny-scale atomic structure (TSAS) with spatial scales ranging from a lower limit of ∼11 au to an upper limit of 165 au, both exhibiting integrated signal-to-noise ratios exceeding 5.0. We find a relationship between linear size and optical depth variation in the cold neutral medium (CNM) component hosting the TSAS, described by a power-law relationship, Δτint = Δτ0(ΔL)(α‑2)/2, with α = 4.1 ± 0.4. This is the first observational evidence explicitly connecting TSAS to turbulence in CNM. This power-law index is significantly steeper than previously reported values for the CNM, where α ranges from 2.3 to 2.9, but similar to those observed in the warm ionized gas. Additionally, we observe no significant variation in α across the entire range of spatial scales traced in our study, indicating that turbulence may be cascading down and dissipating at smaller scales. While there is no precise proper-motion measurement for this pulsar, our estimates for the turbulence dissipation in the CNM place the lower and upper limits at less than 0.03 au and 0.4 au, respectively.

Keywords: Interstellar medium, Interstellar absorption, Interstellar atomic gas, Pulsars
ALEXIS: Recreating the X-Ray Emission from the Full Solar Disk as a Linear Combination of Discrete Regions in the Extreme Ultraviolet and Soft X-Rays. Despite a wealth of multiwavelength, spatially resolved, time-domain solar activity data, an accurate and complete temporospatial solar flare census is unavailable, which impedes our understanding of the physics of flare production. We present an Automatically Labeled EUV and X-Ray Incident SolarFlares (ALEXIS) pipeline, designed to decompose the X-ray flux of the full solar disk into a minimum set of discrete regions on the solar surface. ALEXIS returns an average rms error between the X-Ray Sensor time series and the discrete EUV signals of 0.066 ± 0.036 for a randomly selected test bed sample of 1000 hr-long data segments from 2010 May to 2020 March. Flare emission that requires multiple regions was found to be synchronous (flares occurring at the same time), sympathetic (flares separated by minutes), or needed to capture the background emission before and/or after the main flare. ALEXIS uses the original full resolution and cadence of both the Atmospheric Imaging Assembly instrument and the GOES13–15 Solar X-Ray Imager. Comparison of ALEXIS's catalog with those produced by the SWPC and SolarSoft show that these canonical databases need revisiting for 62% and 15% of the subsample, respectively. Additionally, we increased the number of flares reported by the SWPC and SolarSoft by 15%. Our pipeline misses 6.7% of the 1057 flare subsample and returns 5% of false positives from 1211 flares reported by ALEXIS. The ALEXIS catalog returns flare peak times, coordinates, the corrected scaled X-ray magnitude, and the associated NOAA active region with a HARP identifier number independently from any external data products.

Keywords: Solar flares, Solar x-ray emission, Solar extreme ultraviolet emission
FAST Ultra-Deep Survey (FUDS): The Star Formation Histories of FUDS0 Galaxies. We present the ultraviolet, optical, and infrared counterparts of 128 galaxies detected in neutral hydrogen (H I) in the FAST Ultra-Deep Survey (FUDS) field 0 (FUDS0). H I mass upper limits are also calculated for 134 non-detections in the field. Stellar masses (M*), star formation rates (SFRs), and star formation histories are computed by fitting spectral energy distributions using PROSPECT. The results show that H I-selected galaxies prefer recent long-lasting, but mild star formation activity, while H I non-detections have earlier and more intense star formation activity. Based on their distribution on the SFR versus M* diagram, the typical evolution of H I-selected galaxies follows three distinct stages: (i) early stage: the total SFR increases, though the specific SFR decreases from 10‑8 to 10‑9 yr‑1; (ii) mass accumulation stage: the SFR is steady, and stellar mass increase linearly with time; (iii) quenching stage: star formation activity quenches on a rapid timescale and at constant stellar mass. 37 non-detections are located on the star-forming main sequence, but are not detected in H I due to low sensitivity close to the field edges or close to strong radio frequency interference. Comparisons with the existing optical, optically selected H I, and H I catalogs show a good agreement with respect to measured M* and SFR, with minor discrepancies due to selection effects. The ongoing full FUDS survey will help us better explore the evolutionary stages of H I galaxies through a larger sample.

Keywords: Sky surveys, Late-type galaxies, Galaxy evolution, High-redshift galaxies, Galaxies
Identification of Star Clusters in M31 from PAndAS Images Based on Deep Learning. The identification of star clusters holds significant importance in studying galaxy formation and evolution history. However, the task of swiftly and accurately identifying star clusters from vast amounts of photometric images presents an immense challenge. To address these difficulties, we employ deep learning models for image classification to identify young disk star clusters in M31 from the Pan-Andromeda Archaeological Survey (PAndAS) images. For training, validation, and testing, we utilize the Panchromatic Hubble Andromeda Treasury survey catalogs. We evaluate the performance of various deep learning models, using different classification thresholds and limiting magnitudes. Our findings indicate that the ResNet-50 model exhibits the highest overall accuracy. Moreover, using brighter limiting magnitudes and increasing the classification thresholds can effectively enhance the accuracy and precision of cluster identification. Through our experiments, we found that the model achieves optimal performance when the limiting magnitude is set to brighter than 21 mag. Based on this, we constructed a training data set with magnitudes less than 21 mag and trained a second ResNet-50 model. This model achieved a purity of 89.30%, a recall of 73.55%, and an F1 score of 80.66% when the classification threshold was set to 0.669. Applying the second model to all sources in the PAndAS fields within a projected radius of 30 kpc from the center of M31, we identified 2228 new unique star cluster candidates. We conducted visual inspections to validate the results produced by our automated methods, and we ultimately obtained 1057 star cluster candidates, of which 745 are newly identified.

Keywords: Convolutional neural networks, Young star clusters, Andromeda Galaxy, Star clusters