Sazuna/UAT_keywords huggingface dataset:
Double-lined Spectroscopic Binaries Identified in LAMOST DR11 with a Deep Learning Model. Double-lined spectroscopic binaries (SB2s) hold significant importance for understanding stellar formation and evolution. With the release of massive spectra, deep learning techniques have achieved major breakthroughs in the search of spectroscopic binaries. However, traditional deep learning models are unable to provide predicted uncertainty, which raises concerns about the reliability of the results. In this study, we propose a model called SB2Net to identify SB2s in the Large Sky Area Multi-Object Fiber Spectroscopic Telescope Medium-resolution Survey (LAMOST-MRS) and estimate predicted uncertainty. SB2Net integrates multiple techniques, enabling the efficient extraction of critical features from the cross-correlation function and providing high-confidence classification results. On the test set, our model achieved a precision of 99.53%, with 99% of the samples having epistemic uncertainty less than 0.0017 and aleatoric uncertainty less than 0.0681. By applying SB2Net to 2,526,141 spectra from LAMOST-MRS data release 11, we identified 7711 SB2 candidates, of which 3037 were newly discovered. Additionally, we estimated radial velocities (RVs) and RV errors for all candidate spectra, while validating the consistency of the candidates properties with other catalogs through the color–magnitude diagram. Finally, by fitting the RV curves, we obtained 246 well-fitted orbital solutions with most systems having mass ratio converge to 1. The period–eccentricity relationship reveals that short-period binaries tend to exhibit more circular orbits. In the future, we plan to separate the composite spectra of SB2s to study the properties and atmospheric parameters of the component stars.

Keywords: Spectroscopic binary stars, Radial velocity, Astronomy data analysis, Orbit determination
An Iterative, Deep Learning Approach for Switchback Classification in Parker Solar Probe Data. A neural network model known as ParkerNet has been implemented for classifying switchbacks in Parker Solar Probe (PSP) data. ParkerNet is a binary classification neural network model that combines convolutional neural network layers with bidirectional long short-term memory layers. We employ a targeted, human-in-the-loop approach, where a small set of labels is initially provided to the network for training, and select predictions are iteratively corrected and fed back for retraining. The predictions from the network are compared to two switchback catalogs by J. Huang et al. and F. Pecora et al. ParkerNet only needed approximately 12% of data labeled as switchbacks to demonstrate strong performance, showing high agreement with the core/spike region in the J. Huang et al. catalog. The application of ParkerNet to PSP data highlights the potential of a data-forward approach to unify the identification and characterization of switchbacks and provides a framework for future switchback detection.

Keywords: Solar wind, Interplanetary magnetic fields, Classification, Neural networks
Relativistic Effects and Gamma-Ray Burst Polarization in Power-law Evolution. Despite decades of polarization observations and high significance of polarized γ-ray, X-ray, optical, and radio emissions in gamma-ray bursts (GRBs) accumulating in dozens of cases, people have yet to find a consistent scenario for understanding the globally observed timing properties of GRB polarization to date. Here, we report that the observed properties of GRB polarization exhibit a four-segment timing evolution at a cosmological distance: (i) an initial hump early on (within the first few seconds); (ii) a later-on power-law decay (from ∼101 to ∼104 s), which takes the form of πobs ∝ t−0.50 ± 0.02; (iii) afterward a late-time rebrightening hump (from ∼104 to ∼105 s); and (iv) finally a flattening power-law decay (from ∼105 to ∼107 s), with the form of πobs ∝ t−0.21 ± 0.08. We show that these results can be explained by relativistic and geometric effects of a highly relativistic and magnetized jet generated by a central engine, and "magnetic patches" distributed as a globally random but locally coherent form. The long-term timing evolution of observed GRB polarization follows a scaling law πobs ∝ 1/Sobs, dominantly determined by how "magnetic patches" are randomly distributed in the observed emission region Sobs on the jet plane of 1/Γ cone. It predicts the polarization hump and tail form in accordance with the luminosity jet-break phenomenon. Our analysis suggests that there is a single dominant mechanism (relativistic and geometric effects) that may account for the global observational properties of GRB polarization, and other emission mechanisms and effects may play a role in spatially local and temporally short effects on GRB polarization.

Keywords: Time domain astronomy, Gamma-ray bursts, Astronomy data analysis
A Multimodal Encoder–Decoder Neural Network for Forecasting Solar Wind Speed at L1. The solar wind, accelerated within the solar corona, sculpts the heliosphere and continuously interacts with planetary atmospheres. On Earth, high-speed solar wind streams may lead to severe disruption of satellite operations and power grids. Accurate and reliable forecasting of the ambient solar wind speed is therefore highly desirable. This work presents an encoder–decoder neural network framework for simultaneously forecasting the daily averaged solar wind speed for the subsequent 4 days. The encoder–decoder framework is trained with the two different modes of solar observations. The history of solar wind observations from prior solar rotations and EUV coronal observations up to 4 days prior to the current time forms the input to two different encoders. The decoder is designed to output the daily averaged solar wind speed from 4 days prior to the current time to 4 days into the future. Our model outputs the solar wind speed with rms errors (RMSEs) of 55, 58, 58, and 58 km s‑1 and Pearson correlations of 0.78, 0.66, 0.64, and 0.63 for 1 to 4 days in advance, respectively. While the model is trained and validated on observations between 2010 and 2018, we demonstrate its robustness via application on unseen test data between 2019 and 2023, yielding RMSEs of 53 km s‑1 and Pearson correlations of 0.55 for a 4 day advance prediction. Our encoder–decoder model thus produces much improved RMSE values compared to previous works and paves the way for developing comprehensive multimodal deep learning models for operational solar wind forecasting.

Keywords: Solar-terrestrial interactions, Space weather, Solar wind, Neural networks
The Synchrotron-based Far-infrared Spectrum of Propargylamine. Propargylamine (HC≡C–CH2–NH2), with a triple bond and an amine group, is a nitrogen-bearing organic molecule of astrochemical interest due to its potential role as a precursor to aminoacetonitrile and other complex organic molecules in the interstellar medium (ISM). Despite the detection of the structurally related molecules propargylimine (HC≡C–CH=NH) and aminoacetonitrile (NH2–CH2–CN) in the ISM, it remains undetected. This work presents the first high-resolution far-infrared spectroscopic study of propargylamine, conducted using a synchrotron-based Fourier transform infrared spectrometer at the Canadian Light Source. The rotationally resolved vibrational spectra were measured in the range of 50–480 cm‑1, targeting its three lowest vibrational modes. The rovibrational analysis led to the assignment of two bands centered at 338.8342332(25) and 228.5012023(26) cm‑1, corresponding to the out-of-plane HC≡C–C linear bending (ν20) and –NH2 torsion (ν21) modes, respectively. Also, a band origin of 199.10535(19) cm‑1 was determined for the in-plane HC≡C–C linear bending (ν13) mode through a Coriolis-type resonance with the ν21 mode. The accurate assignment clears up a discrepancy over these vibrational energy levels between previous millimeter-wave and low-resolution infrared studies and made possible the determination of an updated rovibrational partition function. These data provide spectroscopic parameters for future astronomical searches in the ISM.

Keywords: Interstellar molecules, Molecular spectroscopy
A Rapid and Efficient Optical Transients Identification Framework Based on a Multimodal Neural Network and Machine Learning Operations. Wide-field small-aperture telescopes are generally used for transient observations in the optical band, as they can effectively capture images with moderate spatial resolution and depth in a cost-effective way. However, fluctuations in observational conditions and noise from various sources can result in numerous false optical transients, which significantly impede the timely detection of important time-domain astronomical events. This paper introduces a framework aimed at the rapid and efficient identification of optical transients. The proposed framework features a fast image-preprocessing pipeline utilizing graphics processing unit computing to identify transient candidates, alongside a multimodal neural network for obtaining transients from candidates according to not only image features but also properties of observation systems and celestial objects. Additionally, it includes a machine learning operations framework for managing both the neural networks and the image-preprocessing pipeline. Our framework not only automates data processing but also optimizes the parameters of the entire pipeline to yield satisfactory results. We have evaluated the performance of our framework using data obtained from the Ground-based Wide-Angle Camera Array (or GWAC). The results indicate that our framework significantly enhances the detection efficiency of optical transients. Our framework is provided as a Docker image, facilitating easy download and implementation in various projects.

Keywords: Optical astronomy, Astronomy image processing, Neural networks
Rapid Stellar and Binary Population Synthesis with COMPAS: Methods Paper II. The COMPAS (https://compas.science) public rapid binary population synthesis code has undergone a number of key improvements since the original COMPAS methods paper was published. These include more sophisticated and robust treatments of binary interactions: mass transfer physics; common-envelope events; tides and gravitational-wave radiation reaction; and updated prescriptions for stellar evolution, winds, and supernovae. The code structure and outputs have also been updated, with a focus on improving resolution without sacrificing computational speed. This paper describes the substantive changes in the code between the previous methods paper and COMPAS v03.22.01.

Keywords: Binary stars, Stellar populations, Stellar evolutionary models, Stellar remnants, Stellar evolution
Automatic 3D Reconstruction of Coronal Mass Ejections Based on Dual-viewpoint Observations and Machine Learning. Coronal mass ejections (CMEs) are the major driver of severe space weather events, causing substantial economic losses to space-based and ground-based human assets. It is essential to advance our understanding of CME propagation dynamics for better predictions. We have developed an algorithm that automatically reconstructs CME structure, integrating dual-viewpoint observations with machine learning techniques. It consists of three stages: (1) region acquisition, (2) model construction, and (3) function optimization. First, we use two independent convolutional neural networks to identify and detect CMEs in the coronagraph images from two spacecraft. Next, we construct the projections of the graduated cylindrical shell (GCS) model in the fields of view of the coronagraphs. In the final step, optimal parameters are retrieved by minimizing the function that quantifies the morphological discrepancies between the image of the GCS model and the CME detection. Four CME events are reconstructed and analyzed to demonstrate the accuracy of our algorithm. A statistical analysis of 97 CME events from 2007 to 2018 is conducted to investigate both the two-dimensional (2D) and three-dimensional (3D) parameters. According to our statistics, the actual velocities are underestimated by 8%, and the widths are overestimated by 47% because of the projection effect from 2D observations. Furthermore, the widths and velocities are investigated to have a positive correlation coefficient of 0.67 (2D) and 0.52 (3D). The proposed method can further be used to provide CME initial parameters for magnetohydrodynamics simulations, enabling a deeper understanding of CME kinematics.

Keywords: Space weather, Solar coronal mass ejections
Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data. A newly born millisecond magnetar has been proposed as one possible central engine of some gamma-ray bursts (GRBs) with X-ray plateau emission. In this work, we systematically analyzed the Swift/X-Ray Telescope data of long GRBs with plateau emission that were detected before 2023 December, estimated the physical parameters of the magnetar by considering the R/I evolutionary effects, and investigated possible relationships among these parameters and their relation to the GRB jet and magnetar wind radiation. We found that neglecting the evolutionary effects of R/I can lead to systematic overestimation or underestimation of magnetar physical parameters such as magnetic field strength (Bp), spin period (P0), and ellipticity (ϵ) from 20% to 50%. We also found that the correlations among different magnetar parameters can be approximately expressed as  ϵ∝P01.57±0.22 ,  ϵ∝Bp0.97±0.13 , and  Bp∝P01.30±0.16  for our selected equation of states (EoSs), with the 1σ deviation included. The correlations between the GRB jet emission and the magnetar wind emission can be approximately described as  Ewind∝Ejet,iso0.83±0.07(Ejet0.76±0.06) ,  P0∝Ejet,iso‑0.29±0.03(Ejet‑0.26±0.02) ,  Bp∝Ejet,iso‑0.58±0.06(Ejet‑0.55±0.05) , and  ϵ∝Ejet,iso‑0.55±0.07(Ejet‑0.52±0.06)  for our selected EoSs. The universal correlations suggest that a nascent magnetar with the faster P0, lower Bp, as well as lower ϵ are more inclined to power a more energetic GRB jet, and the ellipticity deformation and initial spin period of the newborn magnetar are likely to originate from the magnetically induced distortion mechanism and correspond to the equilibrium spin period as a result of interaction between the magnetar and its accretion disk, respectively. Finally, we found that the gravitational-wave (GW) signals from the remnants of those GW-dominated GRBs with redshift measurements cannot reach the sensitivity threshold of the current Advanced LIGO detector, and only two cases (GRB 150323A and 170607A) can reach the sensitivity threshold of the prospective ET detector. Future GW observations could not only offer the first smoking gun that a protomagnetar can serve as the central engine of GRBs but also play a crucial role in precisely constraining the neutron star EoS.

Keywords: Magnetars, Gamma-ray bursts
Proper Motion of Dense Circumstellar Knots in the Supernova Remnant Cassiopeia A. The young supernova remnant Cassiopeia A is currently interacting with the circumstellar medium expelled by its progenitor star. We analyzed the proper motion of dense circumstellar clumps, known as quasi-stationary flocculi (QSFs), embedded within the remnant using archived images from the Hubble Space Telescope (HST). Our study focused on approximately 40 QSFs that are bright in optical bands. We determined the proper motions within each QSF by cross-correlating the HST images taken 15 yr apart, and presented the results as vector maps that show both the proper-motion magnitudes and directions. For most QSFs, the proper-motion vectors are well aligned, but some show significant variations in expansion direction within the QSF. We derived the mean proper motion of each QSF and investigated its properties. The mean proper-motion vectors of the QSFs predominantly point radially away from the explosion center of Cas A, though not perfectly aligned with a strict radial direction, with corresponding velocities typically in the range of a few hundred kilometers per second. We found no clear correlation between the proper motion and the projected distance from the explosion center, nor between the proper motion and the line-of-sight velocity of the QSFs. This result indicates that the apparent expansion of QSFs is primarily driven by interactions with the supernova blast wave, rather than by their initial ejection. We present an exploratory model of the three-dimensional distribution of QSFs.

Keywords: Supernova remnants, Circumstellar matter, Core-collapse supernovae, Stellar mass loss
Precise Frequencies of H<SUB>2</SUB><SUP>16</SUP>O Lines Protected for Radio Astronomy. Precise frequency values have been determined for  H216O  radio lines appearing in protected line lists of the International Astronomical Union and the Panel on Frequency Allocations of the US National Academy of Sciences. The improved precision is attributable to a spectroscopic network built from a large set of near-infrared Lamb-dip lines augmented with a handful of ultra-high-accuracy rotational transitions. The ultraprecise  H216O  network contains 376 Lamb-dip lines recorded previously via our frequency-comb-locked cavity-enhanced spectrometer. During the present study, a total of 55 target lines have been (re)measured at high accuracy. Due to our network-assisted measurements, the accuracy has been significantly improved with respect to previous direct radio-frequency measurements for all the protected lines of  H216O  above 750 GHz. Furthermore, 43 of these protected transitions now benefit from the accuracy of the new near-infrared Lamb dips reported in this paper.

Keywords: Radio astronomy
The Yin–Yang Magnetic Flux Eruption (Yin–Yang MFE) Code: A Global Corona Magnetohydrodynamic Code with the Yin–Yang Grid. We describe the numerical algorithms of a global magnetohydrodynamic (MHD) code utilizing the Yin–Yang grid, called the Yin–Yang Magnetic Flux Eruption code, suitable for modeling the large-scale dynamical processes of the solar corona and the solar wind. It is a single-fluid MHD code taking into account the nonadiabatic effects of the solar corona, including the electron heat conduction, optically thin radiative cooling, and empirical coronal heating. We describe the numerical algorithms used to solve the set of MHD equations (with the semirelativistic correction, or the Boris correction) in each of the partial spherical shell Yin–Yang domains, and the method for updating the boundary conditions in the ghost zones of the two overlapping domains with the code parallelized with the Message Passing Interface. We validate the code performance with a set of standard test problems, and finally present a solar wind solution with a dipolar magnetic flux distribution at the solar surface, representative of solar minimum configuration.

Keywords: Magnetohydrodynamics, Magnetohydrodynamical simulations
Microlensing Optical Depth, Event Rate, and Limits on Compact Objects in Dark Matter Based on 20 Yr of OGLE Observations of the Small Magellanic Cloud. Some previous studies have suggested that massive and intermediate-mass primordial black holes (PBHs) could comprise a substantial fraction of dark matter in the Universe. Such black holes, if they existed in the Milky Way halo, would give rise to long-duration microlensing events that may potentially last for years. However, earlier searches were not sufficiently sensitive to detect such events. Here, we present the results of searches for long-timescale gravitational microlensing events toward the Small Magellanic Cloud (SMC) using nearly 20 yr of photometric observations collected by the Optical Gravitational Lensing Experiment (OGLE) from 2001─2020. We found six events, three of which are new discoveries. We use a sample of five events to measure the microlensing optical depth toward the SMC τ = (0.32 ± 0.18) × 10−7 and the event rate Γ = (1.18 ± 0.57) × 10−7 yr−1 star−1. The properties of the detected events are consistent with lenses originating from known stellar populations within the SMC or in the Milky Way disk. No events with timescales longer than 1 yr were detected, which provides competitive limits on the fraction of massive compact objects, including PBHs, in the Milky Way dark matter halo. Together with the earlier OGLE studies of microlensing events in the direction of the Large Magellanic Cloud, these observations rule out PBHs and other compact objects with masses ranging from 10−8─103 M⊙ as dominant components of dark matter.

Keywords: Milky Way dark matter halo, Intermediate-mass black holes, Small Magellanic Cloud, Gravitational microlensing, Dark matter, Primordial black holes
Reconstruction of Solar Extreme-ultraviolet Irradiance Using Ca II K Images and SOHO/SEM Data with Bayesian Deep Learning and Uncertainty Quantification. Solar extreme-ultraviolet (EUV) irradiance plays a crucial role in heating the Earth's ionosphere, thermosphere, and mesosphere, affecting atmospheric dynamics over varying time scales. Although significant effort has been spent studying short-term EUV variations from solar transient events, there is little work to explore the long-term evolution of the EUV flux over multiple solar cycles. Continuous EUV flux measurements have only been available since 1995, leaving significant gaps in earlier data. In this study, we propose a Bayesian deep learning model, named SEMNet, to fill the gaps. We validate our approach by applying SEMNet to construct Solar and Heliospheric Observatory/Solar EUV Monitor EUV flux measurements in the period between 1998 and 2014 using Ca II K images from the Precision Solar Photometric Telescope. We then extend SEMNet through transfer learning to reconstruct solar EUV irradiance in the period between 1950 and 1960 using Ca II K images from the Kodaikanal Solar Observatory. Experimental results show that SEMNet provides reliable predictions along with uncertainty bounds, demonstrating the feasibility of Ca II K images as a robust proxy for long-term EUV fluxes. These findings contribute to a better understanding of solar influences on Earth's climate over extended periods.

Keywords: Solar atmosphere, Ground-based astronomy, Solar extreme ultraviolet emission, Neural networks
Validation of Long-term Solar Coronal Modeling Using FORWARD. This research aims to evaluate the capabilities of two of the most widely used coronal models over multiple solar cycles and to study variations in solar coronal structures over an extended period across multiple channels. We have created a comprehensive database of solar corona (FORWARD data cubes) and inner heliosphere simulations using the Space Weather Modeling Framework's Alfvén Wave Solar Atmosphere Model (AWSoM) for SC 24 and SC 25 to examine the distribution of solar wind source regions over time: coronal holes, active regions, and quiet Sun. The model accuracy was evaluated by comparing the synthetic images to Atmospheric Imaging Assembly observations from the same period across six different channels (94, 131, 171, 193, 211, and 335 Å). Additionally, to evaluate the simulations' accuracy, we compared the AWSoM solar wind plasma parameter output at 1 au to the available OMNI data set for each Carrington rotation. Through SolarSoft's FORWARD tool, we also accessed the Predictive Science Inc. Magnetohydrodynamic Algorithm outside a Sphere (MAS) model results, and performed the same analysis for MAS to demonstrate the evaluative power of our methodology. We share the similarities and differences in predictive power of the two models through multiple solar cycles in an unprecedented way. We found that both AWSoM and MAS slightly underperform near solar maximum—albeit in different ways with respect to active regions and coronal holes. Overall, we assert that active regions are more accurately reproduced and recognized than coronal holes in both models.

Keywords: Solar corona, Magnetohydrodynamical simulations