Table of contents

In certain cases of astronomical data analysis, the meaningful physical quantity to extract is the ratio R between two data sets. Examples include the lensing ratio, the interloper rate in spectroscopic redshift samples, and the decay rate of gravitational potential and E_G to test gravity. However, simply taking the ratio of the two data sets is biased, since it renders (even statistical) errors in the denominator into systematic errors in R. Furthermore, it is not optimal in minimizing statistical errors of R. Based on Bayesian analysis and the usual assumption of Gaussian error in the data, we derive an analytical expression of the posterior probability density function P(R). This result enables fast and unbiased R measurement, with minimal statistical errors. Furthermore, it relies on no underlying model other than the proportionality relation between the two data sets. Even more generally, it applies to cases where the proportionality relation holds for the underlying physics/statistics instead of the two data sets directly. It also applies to the case of multiple ratios (R → R = (R₁, R₂, ⋯ )). We take the lensing ratio as an example to demonstrate our method. We take lenses as DESI imaging survey galaxies, and sources as DECaLS cosmic shear and Planck cosmic microwave background (CMB) lensing. We restrict the analysis to the ratio between CMB lensing and cosmic shear. The resulting P(R) values, for multiple lens–shear pairs, are all nearly Gaussian. The signal-to-noise ratio of measured R ranges from 4.9 to 8.4. We perform several tests to verify the robustness of the above result.

The Legacy Survey of Space and Time (LSST) is slated to commence in 2025 at the Vera Rubin Observatory. One of the crucial parts of preparing the survey is the choice of observing cadence in an effort to optimize auxiliary science goals while maintaining the core project requirements. Here we look at the impact of proposed cadences, encoded in different operation simulations (opsims), on non-time-critical eclipsing binary science. This is particularly pertinent because LSST is the first large-scale survey that will provide us with color information in addition to high-precision coverage of faint targets. We study the differences between the baseline opsim v2.1 and the latest opsim v3.0 runs. We find that all runs provide sufficient data coverage to enable in-depth studies in the field of eclipsing binaries, and that there are no adverse impacts from any proposed opsim modification studied here.

High-resolution absorption, dispersed fluorescence emission, and photoionization cross sections are presented for gas-phase adamantane excited by synchrotron radiation in the exciting-photon energy range of 6–30 eV. Relative and absolute absorption cross sections of so-far unmatched resolution of down to 0.27 cm⁻¹ line width in the region from 6.4–28 eV are shown along with newly discovered vibronic substructures around the HOMO–LUMO transition. Absorption line positions are provided with very high accuracy and listed in tabular form to be used as spectral fingerprints for the detection of adamantane in interstellar media, where its column density may be determined via the absolute cross sections. The fluorescence emission lies in the ultraviolet range from 190–250 nm and is excited starting at the HOMO–LUMO transition at 6.49 eV, which corresponds to the highest fluorescence emission energy. Hitherto unreported fluorescence in the same spectral range and relative photoionization cross sections in the exciting-photon energy range up to 30 eV are also presented along with lifetime measurements for differentiation of the involved electronic states.

The Atacama Large Millimeter/submillimeter Array high-frequency long-baseline campaign in 2019 (HF-LBC-2019) was arranged to undertake band 9 (690 GHz) and 10 (850 GHz) observations using the longest 16 km baselines in order to explore calibration feasibility and imaging capabilities. Observations were arranged using close calibrators between 0° and 4° from the target point-source quasars (QSOs) to also explore subtle effects of calibrator separation angle. A total of 13 observations were made, five using standard in-band observations and eight using the band-to-band (B2B) observing mode, where phase solutions are transferred from a lower frequency band. At bands 9 and 10, image angular resolutions as high as 7 and 5 mas were achieved, respectively. Both in-band and B2B experiments were successful in imaging the target QSOs but with varying degrees of quality. Target image coherence varied between 0.14 and 0.79, driven by the calibrator separation angle and effectiveness of phase referencing despite observing in correct stability conditions. We conclude that the phase rms conditions and calibrator selection, specifically separation angle from the target, must carefully be considered prior to observing in order to minimize imaging defects. For bands 9 and 10, in order to achieve a coherence >0.7 such that the image structure and source flux can be regarded as suitably accurate, a 1° separated calibrator should be used while the phase rms over the phase switching cycle time should ideally be <30°.

Next-generation surveys like the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory (Rubin) will generate orders of magnitude more discoveries of transients and variable stars than previous surveys. To prepare for this data deluge, we developed the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC), a competition that aimed to catalyze the development of robust classifiers under LSST-like conditions of a nonrepresentative training set for a large photometric test set of imbalanced classes. Over 1000 teams participated in PLAsTiCC, which was hosted in the Kaggle data science competition platform between 2018 September 28 and 2018 December 17, ultimately identifying three winners in 2019 February. Participants produced classifiers employing a diverse set of machine-learning techniques including hybrid combinations and ensemble averages of a range of approaches, among them boosted decision trees, neural networks, and multilayer perceptrons. The strong performance of the top three classifiers on Type Ia supernovae and kilonovae represent a major improvement over the current state of the art within astronomy. This paper summarizes the most promising methods and evaluates their results in detail, highlighting future directions both for classifier development and simulation needs for a next-generation PLAsTiCC data set.

A panchromatic investigation of morphology for the early-type spiral galaxy M81 is presented in this paper. We perform bulge–disk decomposition in M81 images at a total of 20 wave bands from far-UV to near-IR (NIR) obtained with GALEX, Swift, Sloan Digital Sky Survey, WIYN, Two Micron All Sky Survey, Wide-field Infrared Survey Explorer, and Spitzer. Morphological parameters such as Sérsic index, effective radius, position angle, and axis ratio for the bulge and the disk are thus derived at all of the wave bands, which enables quantifying the morphological K-correction for M81 and makes it possible to reproduce images for the bulge and the disk in the galaxy at any wave band. The morphology as a function of wavelength appears as a variable-slope trend of the Sérsic index and the effective radius, in which the variations are steep at UV–optical and shallow at optical–NIR bands; the position angle and the axis ratio keep invariable at least at optical–NIR bands. It is worth noting that the Sérsic index for the bulge reaches ∼4–5 at optical and NIR bands, but drops to ∼1 at UV bands. This difference brings forward a caveat that a classical bulge is likely misidentified for a pseudobulge or no bulge at high redshifts where galaxies are observed through rest-frame UV channels with optical telescopes. The next work of this series is planned to study spatially resolved spectral energy distributions for the bulge and the disk, respectively, and thereby explore stellar population properties and star formation/quenching history for the galaxy composed of the subsystems.

We present the completed catalog of ultradiffuse galaxy (UDG) candidates (7070 objects) from our search of the DR9 Legacy Survey images, including distance and total mass estimates for 1529 and 1436 galaxies, respectively, that we provide and describe in detail. From the sample with estimated distances, we obtain a sample of 585 UDGs (μ_0,g ≥ 24 mag arcsec⁻² and r_e ≥ 1.5 kpc) over 20,000 square degrees of sky in various environments. We conclude that UDGs in our sample are limited to 10¹⁰ ≲ M_h/M_⊙ ≲ 10^11.5 and are on average a factor of 1.5–7 deficient in stars relative to the general population of galaxies of the same total mass. That factor increases with increasing galaxy size and mass up to a factor of ∼10 when the total mass of the UDG increases beyond M_h = 10¹¹M_⊙. We do not find evidence that this factor has a dependence on the UDGs large-scale environment.

Faraday rotation measures (RMs) have been used for many studies of cosmic magnetism, and in most cases having more RMs is beneficial for those studies. This has lead to the development of RM surveys that have produced large catalogs, as well as meta-catalogs collecting RMs from many different publications. However, it has been difficult to take full advantage of all of these RMs, as the individual catalogs have been published in many different places, and in many different formats. In addition, the polarization spectra used to determine these RMs are rarely published, limiting the ability to reanalyze data as new methods or additional observations become available. We propose a standard convention for RM catalogs, RMTable2023, and a standard for source-integrated polarized spectra of radio sources, PolSpectra2023. These standards are intended to maximize the value and utility of these data for researchers and to make them easier to access. To demonstrate the use of the RMTable2023 standard, we have produced a consolidated catalog of 55,819 RMs collected from 42 published catalogs.

The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data during their third observing run, O3, composed of three phases: O3a starting in 2019 April and lasting six months, O3b starting in 2019 November and lasting five months, and O3GK starting in 2020 April and lasting two weeks. In this paper we describe these data and various other science products that can be freely accessed through the Gravitational Wave Open Science Center at https://gwosc.org. The main data set, consisting of the gravitational-wave strain time series that contains the astrophysical signals, is released together with supporting data useful for their analysis and documentation, tutorials, as well as analysis software packages.

In this paper we present the distribution of molecular gas in the Milky Way Galactic plane from l = [59.75, 74.75]° and b = [−5.25, +5.25]°, using the MWISP ¹²CO/¹³CO/C¹⁸O emission-line data. The molecular gas in this region can be mainly attributed to the Local Spur, Local Arm, Perseus arm, and Outer arm. Statistics of the physical properties of the molecular gas in each arm, such as excitation temperature, optical depth, and column density, are presented. Using the DBSCAN algorithm, we identified 15 extremely distant molecular clouds with kinematic distances of 14.72−17.77 kpc and masses of 363−520 M_⊙, which we find could be part of the Outer Scutum–Centaurus (OSC) arm identified by Dame & Thaddeus and Sun et al. It is also possible that 12 of these 15 extremely distant molecular clouds constitute an independent structure between the Outer and the OSC arms or a spur. Two Gaussian components exist in the vertical distribution of the molecular gas in the Perseus spiral arm. These two Gaussian components correspond to two giant filaments parallel to the Galactic plane. We find an upward warping of the molecular gas in the Outer spiral arm with a displacement of around 270 pc with respect to the Galactic midplane.

SkyPortal is an open-source software package designed to discover interesting transients efficiently, manage follow-up, perform characterization, and visualize the results. By enabling fast access to archival and catalog data, crossmatching heterogeneous data streams, and the triggering and monitoring of on-demand observations for further characterization, a SkyPortal-based platform has been operating at scale for >2 yr for the Zwicky Transient Facility Phase II community, with hundreds of users, containing tens of millions of time-domain sources, interacting with dozens of telescopes, and enabling community reporting. While SkyPortal emphasizes rich user experiences across common front-end workflows, recognizing that scientific inquiry is increasingly performed programmatically, SkyPortal also surfaces an extensive and well-documented application programming interface system. From back-end and front-end software to data science analysis tools and visualization frameworks, the SkyPortal design emphasizes the reuse and leveraging of best-in-class approaches, with a strong extensibility ethos. For instance, SkyPortal now leverages ChatGPT large language models to generate and surface source-level human-readable summaries automatically. With the imminent restart of the next generation of gravitational-wave detectors, SkyPortal now also includes dedicated multimessenger features addressing the requirements of rapid multimessenger follow-up: multitelescope management, team/group organizing interfaces, and crossmatching of multimessenger data streams with time-domain optical surveys, with interfaces sufficiently intuitive for newcomers to the field. This paper focuses on the detailed implementations, capabilities, and early science results that establish SkyPortal as a community software package ready to take on the data science challenges and opportunities presented by this next chapter in the multimessenger era.

A comprehensive understanding of molecular clumps is essential for investigating star formation. We present an algorithm for molecular clump detection, called FacetClumps. This algorithm uses a morphological approach to extract signal regions from the original data. The Gaussian facet model is employed to fit the signal regions, which enhances the resistance to noise and the stability of the algorithm in diverse overlapping areas. The introduction of the extremum determination theorem of multivariate functions offers theoretical guidance for automatically locating clump centers. To guarantee that each clump is continuous, the signal regions are segmented into local regions based on gradient, and then the local regions are clustered into the clump centers based on connectivity and minimum distance to identify the regional information of each clump. The experiments conducted with both simulated and synthetic data demonstrate that FacetClumps exhibits great recall and precision rates, small location error and flux loss, and a high consistency between the region of detected clump and that of simulated clump, and the experiments demonstrate that FacetClumps is generally stable in various environments. Notably, the recall rate of FacetClumps in the synthetic data, which comprises ¹³CO (J = 1−0) emission line of the MWISP within 117 ≤ l ≤ 134, 022 ≤ b ≤ 105, and 5 km s⁻¹ ≤ v ≤ 35 km s⁻¹ and simulated clumps, reaches 90.2%. Additionally, FacetClumps demonstrates satisfactory performance when applied to observational data.

From the first phase of the high-cadence Formation of the Outer Solar System: an Icy Legacy (FOSSIL) survey, we analyzed lightcurves, ranging from one to four nights in length, of 371 trans-Neptunian objects (TNOs) for periodicity. We found 29 TNOs with periodic lightcurves, one of which is a good candidate for a close/contact binary. Another of the periodic FOSSIL TNOs could potentially have the fastest of all known TNO spin rates, with a period of 1.3 hr. We do not have total confidence in the period and thus plan to obtain a more detailed lightcurve for confirmation. The periodic TNOs have an average rotation period of 11.2 hr, close to the value obtained by Alexandersen et al., which had similar cadence, but different from other surveys. In regards to contention in the literature about whether smaller TNOs are more irregular in shape and thus have larger lightcurve amplitudes, we found that there is a weak correlation between absolute magnitude and lightcurve amplitude in a subset of 194 FOSSIL TNOs, even when using the more appropriate brightest (minimum) absolute magnitude instead of the time-averaged value.

Despite having data for over 10⁹ stars from Gaia, less than 10⁴ star clusters and candidates have been discovered. In particular, distant star clusters are rarely identified, due to the challenges posed by heavy extinction and great distance. However, Gaia data has continued to improve, enabling even fainter cluster members to be distinguished from field stars. In this work, we introduce a star-cluster search method based on the DBSCAN algorithm; we have made improvements to make it better suited for identifying clusters on dimmer and more distant stars. After having removed member stars of known Gaia-based clusters, we identified 2086 objects with ∣b∣ < 10°, of which 1488 are highly reliable open star clusters, along with 569 candidates, 28 globular cluster candidates, and one irregular galaxy (IC 10) at low Galactic latitudes. We found that the proper motion of IC 10 is similar to, yet slightly different from, the water maser observations, which is an important result for the comparison with Gaia and the Very Long Baseline Array. When compared with the star clusters appearing in Gaia Data Release (DR) 2/EDR3, we found nearly 3 times as many new objects above a distance of 5 kpc, including hundreds of them above A_v > 5 mag. This has enabled us to detect a higher number of old clusters, over a billion years old, that are difficult to detect due to observational limitations. Our findings significantly expand the remote cluster sample and enhance our understanding of the limits of Gaia DR3 data in stellar aggregates research.

Ubiquitous vortical structures are considered to act as a natural source of various solar plasma phenomena, for example, a wide range of magnetohydrodynamic waves and jet excitations. This work aims to develop an advanced vortex detection algorithm based on the Γ method and using a separable convolution kernel technique. This method is applied to detect and analyze the photospheric vortices in 3D realistic magnetoconvection numerical and observational data. We present the advanced Γ method (AGM), and our results indicate that the AGM performs with better accuracy in comparison with the original Γ method. The AGM allows us to identify small- and large-scale vortices with no vortex interposition and without requiring the changing of the threshold. In this way, the nondetection issue is mostly prevented. It was found that the Γ method failed to identify the large and longer-lived vortices, which were detected by the AGM. The size of the detected vortical structures tends to vary over time, with most vortices shrinking toward their end. The vorticity at the center is also not constant, presenting a sharp decay as the vortex ceases to exist. Due to its capability of identifying vortices with minimum nondetection, the vortex properties—such as lifetime, geometry, and dynamics—are better captured by the AGM than by the Γ method. In this era of new high-resolution observation, the AGM can be used as a precise technique for identifying and performing statistical analysis of solar atmospheric vortices.

The main objective of this article, the first in a dedicated series, is to report basic results on systematic research of low-redshift optically selected SDSS Type 2 active galactic nuclei (AGNs) but with apparent optical variabilities. For all the pipeline-classified Type 2 AGNs in SDSS DR16 with z < 0.3 and signal-to-noise ratio > 10, long-term optical V-band light curves are collected from the Catalina Sky Survey. Through all light curves described by a damped random walk process with process parameters of σ/(mag/days^0.5) and τ/days, 156 Type 2 AGNs have apparent variabilities with process parameters at least 3 times larger than corresponding uncertainties and with ln(σ/(mag/days^0.5)) > −4, indicating central AGN activity regions directly in the line of sight, leading the 156 Type 2 AGNs as misclassified Type 2 AGNs. Furthermore, based on spectroscopic emission features around Hα, 31 out of the 156 AGNs have broad Hα, indicating the 31 Type 2 AGNs are actually Type 1.8 and/or 1.9 AGNs. Meanwhile, 14 out of the 156 AGNs have multiepoch SDSS spectra. After checking multiepoch spectra of the 14 objects, no clues for appearance and/or disappearance of broad lines indicates true Type 2 AGNs rather than changing-look AGNs are preferred in the collected Type 2 AGNs with long-term variabilities. Moreover, a small sample of Type 2 AGNs have long-term variabilities with features roughly described by theoretical tidal disruption events (TDEs) expected t^−5/3, indicating probable central TDEs as further and strong evidence to support true Type 2 AGNs.

Active galactic nuclei (AGNs) can often be identified in radio images as two lobes, sometimes connected to a core by a radio jet. This multicomponent morphology unfortunately creates difficulties for source finders, leading to components that are (a) separate parts of a wider whole, and (b) offset from the multiwavelength cross identification of the host galaxy. In this work we define an algorithm, DRAGNhunter, for identifying double radio sources associated with AGNs (DRAGNs) from component catalog data in the first epoch Quick Look images of the high-resolution (≈3'' beam size) Very Large Array Sky Survey (VLASS). We use DRAGNhunter to construct a catalog of >17,000 DRAGNs in VLASS for which contamination from spurious sources is estimated at ≈11%. A "high-fidelity" sample consisting of 90% of our catalog is identified for which contamination is <3%. Host galaxies are found for ≈13,000 DRAGNs as well as for an additional 234,000 single-component radio sources. Using these data, we explore the properties of our DRAGNs, finding them to be typically consistent with Fanaroff–Riley class II sources and to allow us to report the discovery of 31 new giant radio galaxies identified using VLASS.

We present the implementation of a two-moment-based general-relativistic multigroup radiation transport module in the General-relativistic multigrid numerical (Gmunu) code. On top of solving the general-relativistic magnetohydrodynamics and the Einstein equations with conformally flat approximations, the code solves the evolution equations of the zeroth- and first-order moments of the radiations in the Eulerian-frame. An analytic closure relation is used to obtain the higher order moments and close the system. The finite-volume discretization has been adopted for the radiation moments. The advection in spatial space and frequency-space are handled explicitly. In addition, the radiation–matter interaction terms, which are very stiff in the optically thick region, are solved implicitly. The implicit–explicit Runge–Kutta schemes are adopted for time integration. We test the implementation with a number of numerical benchmarks from frequency-integrated to frequency-dependent cases. Furthermore, we also illustrate the astrophysical applications in hot neutron star and core-collapse supernovae modelings, and compare with other neutrino transport codes.

A H ii region is a kind of emission nebula, and more definite samples of H ii regions can help study the formation and evolution of galaxies. Hence, a systematic search for H ii regions is necessary. The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) conducts medium-resolution spectroscopic surveys and provides abundant valuable spectra for unique and rare celestial body research. Therefore, the medium-resolution spectra of LAMOST are an ideal data source for searching for Galactic H ii regions. This study uses the LAMOST spectra to expand the current spectral sample of Galactic H ii regions through machine learning. Inspired by deep convolutional neural networks with wide first-layer kernels (WDCNN), a new spectral-screening method, multihead WDCNN, is proposed and implemented. Infrared criteria are further used for the identification of Galactic H ii region candidates. Experimental results show that the multihead WDCNN model is superior to other machine-learning methods and it can effectively extract spectral features and identify H ii regions from the massive spectral database. In the end, among all candidates, 57 H ii regions are identified and known in SIMBAD, and four objects are identified as "to be confirmed" Galactic H ii region candidates. The known H ii regions and H ii region candidates can be retrieved from the LAMOST website.

Solar energetic particle (SEP) events are one of the most crucial aspects of space weather. Their prediction depends on various factors including the source solar eruptions such as flares and coronal mass ejections (CMEs). The Geostationary Solar Energetic Particle (GSEP) events catalog was developed as an extensive data set toward this effort for solar cycles 22, 23, and 24. In the present work, we review and extend the GSEP data set by (1) adding "weak" SEP events that have proton enhancements from 0.5 to 10 pfu in the E >10 MeV channel and (2) improving the associated solar source eruptions information. We analyze and discuss spatiotemporal properties such as flare magnitudes, locations, rise times, and speeds and widths of CMEs. We check for the correlation of these parameters with peak proton fluxes and event fluences. Our study also focuses on understanding feature importance toward the optimal performance of machine-learning (ML) models for SEP event forecasting. We implement random forest, extreme gradient boosting, logistic regression, and support vector machine classifiers in a binary classification schema. Based on the evaluation of our best models, we find both the flare and CME parameters are requisites to predict the occurrence of an SEP event. This work is a foundation for our further efforts on SEP event forecasting using robust ML methods.

We investigate the roles of major and minor mergers during brightest cluster galaxy (BCG) assembly using surface brightness profiles, line indices, and fundamental plane relations. Based on our own sample and consistently reanalyzed Sloan Digital Sky Survey data, we find that BCGs and luminous normal ellipticals (LNEs) have similar central velocity dispersions, central absorption line strengths, and central surface brightnesses. However, BCGs are more luminous due to their much larger radial extent. These properties result in a flattening of the Faber–Jackson and Mg_b–luminosity relations above 10^10.6${L}_{\odot ,g^{\prime} }$. We use this effect to estimate an amount of 60%–80% of accreted and merged light in BCGs relative to LNEs, which agrees with results from cosmological simulations. We determine the contribution of this excess light (EL) at each radius from the difference between the surface flux profiles of BCGs and LNEs. It is small in the center but increases steeply to 50% at ∼3 kpc radius. The shape of these profiles suggests that BCGs could be formed from LNEs in three major merger processes. This is also consistent with the mild increase of the Sérsic indices from n ≈ 4 to n ≈ 6, as confirmed in merger simulations. We note that minor mergers cannot be the dominant origin of the BCG's EL because they deposit too few stars at intermediate radii r ≲ 20 kpc. The shape of the EL profile also explains a detected offset of 0.14 dex of the fundamental planes for BCGs and LNEs relative to each other.

Gamma-ray bursts (GRBs), as they are observed at high redshift (z = 9.4), are vital to cosmological studies and investigating Population III stars. To tackle these studies, we need correlations among relevant GRB variables with the requirement of small uncertainties on their variables. Thus, we must have good coverage of GRB light curves (LCs). However, gaps in the LC hinder the precise determination of GRB properties and are often unavoidable. Therefore, extensive categorization of GRB LCs remains a hurdle. We address LC gaps using a stochastic reconstruction, wherein we fit two preexisting models (the Willingale model; W07; and a broken power law; BPL) to the observed LC, then use the distribution of flux residuals from the original data to generate data to fill in the temporal gaps. We also demonstrate a model-independent LC reconstruction via Gaussian processes. At 10% noise, the uncertainty of the end time of the plateau, its correspondent flux, and the temporal decay index after the plateau decreases by 33.3%, 35.03%, and 43.32% on average for the W07, and by 33.3%, 30.78%, 43.9% for the BPL, respectively. The uncertainty of the slope of the plateau decreases by 14.76% in the BPL. After using the Gaussian process technique, we see similar trends of a decrease in uncertainty for all model parameters for both the W07 and BPL models. These improvements are essential for the application of GRBs as standard candles in cosmology, for the investigation of theoretical models, and for inferring the redshift of GRBs with future machine-learning analyses.

The study of the interaction between ionized jets, molecular outflows, and their environments is critical to understanding high-mass star formation, especially because jets and outflows are thought to be key in the transfer of angular momentum outward from accretion disks. We report a low spectral resolution Karl G. Jansky Very Large Array (VLA) survey for OH, NH₃, CH₃OH, and hydrogen radio recombination lines, toward a sample of 58 high-mass star-forming regions that contain numerous ionized jet candidates. The observations are from a survey designed to detect radio continuum; the novel aspect of this work is to search for spectral lines in broadband VLA data (we provide the script developed in this work to facilitate exploration of other data sets). We report detection of 25 GHz CH₃OH transitions toward 10 sources; 5 of them also show NH₃ emission. We found that most of the sources detected in CH₃OH and NH₃ have been classified as ionized jets or jet candidates and that the emission lines are coincident with, or very near (≲0.1 pc), these sources; hence, these molecular lines could be used as probes of the environment near the launching site of jets/outflows. No radio recombination lines were detected, but we found that the rms noise of stacked spectra decreases following the radiometer equation. Therefore, detecting radio recombination lines in a sample of brighter free–free continuum sources should be possible. This work demonstrates the potential of broadband VLA continuum observations as low resolution spectral-line scans.

The eighteenth data release (DR18) of the Sloan Digital Sky Survey (SDSS) is the first one for SDSS-V, the fifth generation of the survey. SDSS-V comprises three primary scientific programs or "Mappers": the Milky Way Mapper (MWM), the Black Hole Mapper (BHM), and the Local Volume Mapper. This data release contains extensive targeting information for the two multiobject spectroscopy programs (MWM and BHM), including input catalogs and selection functions for their numerous scientific objectives. We describe the production of the targeting databases and their calibration and scientifically focused components. DR18 also includes ∼25,000 new SDSS spectra and supplemental information for X-ray sources identified by eROSITA in its eFEDS field. We present updates to some of the SDSS software pipelines and preview changes anticipated for DR19. We also describe three value-added catalogs (VACs) based on SDSS-IV data that have been published since DR17, and one VAC based on the SDSS-V data in the eFEDS field.

In this study, we forecast solar wind speed for the next 3 days with a 6 hr cadence using a deep-learning model. For this we use Solar Dynamics Observatory/Atmospheric Imaging Assembly 211 and 193 Å images together with solar wind speeds for the last 5 days as input data. The total period of the data is from 2010 May to 2020 December. We divide them into a training set (January–August), validation set (September), and test set (October–December), to consider the solar cycle effect. The deep-learning model consists of two networks: a convolutional layer–based network for images and a dense layer–based network for solar wind speeds. Our main results are as follows. First, our model successfully predicts the solar wind speed for the next 3 days. The rms error (RMSE) of our model is from 37.4 km s⁻¹ (for the 6 hr prediction) to 68.2 km s⁻¹ (for the 72 hr prediction), and the correlation coefficient is from 0.92 to 0.67. These results are much better than those of previous studies. Second, the model can predict sudden increase of solar wind speeds caused by large equatorial coronal holes. Third, solar wind speeds predicted by our model are more consistent with observations than those by the Wang–Sheely–Arge–ENLIL model, especially in high-speed-stream regions. It is also noted that our model cannot predict solar wind speed enhancement by coronal mass ejections. Our study demonstrates the effectiveness of deep learning for solar wind speed prediction, with potential applications in space weather forecasting.

The effects of metallicity on the evolution of protoplanetary disks may be studied in the outer Galaxy where the metallicity is lower than in the solar neighborhood. We present the VLT/KMOS integral field spectroscopy in the near-infrared of ∼120 candidate young stellar objects (YSOs) in the CMa-ℓ224 star-forming region located at a Galactocentric distance of 9.1 kpc. We characterize the YSO accretion luminosities and accretion rates using the hydrogen Brγ emission and find a median accretion luminosity of $\mathrm{log}({L}_{\mathrm{acc}})=-{0.82}_{-0.82}^{+0.80}{L}_{\odot }$. Based on the measured accretion luminosities, we investigate the hypothesis of star formation history in the CMa-ℓ224. Their median values suggest that Cluster C, where most of YSO candidates have been identified, might be the most evolved part of the region. The accretion luminosities are similar to those observed toward low-mass YSOs in the Perseus and Orion molecular clouds, and they do not reveal the impact of lower metallicity. Similar studies in other outer Galaxy clouds covering a wide range of metallicities are critical to gain a complete picture of star formation in the Galaxy.

Smoothed particle hydrodynamics (SPH) is a frequently applied tool in computational astrophysics to solve the fluid dynamics equations governing the systems under study. For some problems, for example when involving asteroids and asteroid impacts, the additional inclusion of material strength is necessary in order to accurately describe the dynamics. In compact stars, that is white dwarfs and neutron stars, solid components are also present. Neutron stars have a solid crust, which is the strongest material known in nature. However, their dynamical evolution, when modeled via SPH or other computational fluid dynamics codes, is usually described as a purely fluid dynamics problem. Here, we present the first 3D simulations of neutron star crustal toroidal oscillations including material strength with the Los Alamos National Laboratory SPH code FleCSPH. In the first half of the paper, we present the numerical implementation of solid material modeling together with standard tests. The second half is on the simulation of crustal oscillations in the fundamental toroidal mode. Here, we dedicate a large fraction of the paper to approaches that can suppress numerical noise in the solid. If not minimized, the latter can dominate the crustal motion in the simulations.

Instabilities driven by some combination of rotation, velocity shear, and magnetic field in a stratified fluid under gravity play an important role in many astrophysical settings. Of particular note are the centrifugal instability, the magnetorotational instability, and magnetic buoyancy instability. Here, we consider a Cartesian model of an equatorial region incorporating all the physical ingredients necessary to study their competition. We investigate the linear instability to interchange ("axisymmetric") modes of an inviscid, perfectly conducting, isothermal gas, including the effects of rotation, velocity shear, and poloidal and toroidal magnetic fields. The stability problem can be reduced to a second-order boundary value problem, with the growth rate as the eigenvalue. We can make analytic progress through consideration of the physically relevant regime in which the transverse horizontal wavenumber k ≫ 1. Via a perturbation analysis, with 1/k as the small parameter, we can derive the growth rate and the spatial dependence of the eigenfunctions: the unstable modes are strongly localized in the vertical direction, being either wall modes (localized near a boundary of the domain) or body modes (localized in the interior). We describe the conditions under which the joint action of the separate instability mechanisms leads to enhancement or suppression of the instability. Our analytical results are supplemented by numerical solutions of the stability problem. The most unstable mode found analytically is typically in excellent agreement with that found numerically through consideration of a wide range of wavenumbers. Finally, we discuss how our results relate to the solar tachocline.