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Spatially resolved emission-line kinematics are invaluable for investigating fundamental galaxy properties and have become increasingly accessible for galaxies at z ≳0.5 through sensitive near-infrared imaging spectroscopy and millimeter interferometry. Kinematic modeling is at the core of the analysis and interpretation of such data sets, which at high z present challenges due to the lower signal-to-noise ratio (S/N) and resolution compared to the data of local galaxies. We present and test the 3D fitting functionality of DysmalPy, examining how well it recovers the intrinsic disk rotation velocity and velocity dispersion, using a large suite of axisymmetric models, covering a range of galaxy properties and observational parameters typical of z ∼ 1−3 star-forming galaxies. We also compare DysmalPy's recovery performance to that of two other commonly used codes, GalPak3D and 3DBarolo, which we use in turn to create additional sets of models to benchmark DysmalPy. Over the ranges of S/N, resolution, mass, and velocity dispersion explored, the rotation velocity is accurately recovered by all tools. The velocity dispersion is recovered well at high S/N, but the impact of methodology differences is more apparent. In particular, template differences for parametric tools and S/N sensitivity for the nonparametric tool can lead to differences of up to a factor of 2. Our tests highlight and the importance of deep, high-resolution data and the need for careful consideration of (i) the choice of priors (parametric approaches); and (ii) the masking (all approaches); and (iii), more generally, the evaluating of the suitability of each approach to the specific data at hand. This paper accompanies the public release of DysmalPy. [ABSTRACT FROM AUTHOR]

Using the atmospheric pulsation code written by George Bowen, we have performed a parameter study examining the effects of modifying various parameters of models of oxygen-rich AGB atmospheres pulsating in the fundamental and first-overtone modes. For each pulsation mode, we have examined the effects of adjusting the dust condensation temperature, dust condensation temperature range, pulsation amplitude, dust opacity, and metallicity. Our model grids are generated with the constraint that their luminosities are chosen to span the range of observed mass loss rates at a chosen mass. The dust condensation temperature, pulsation amplitude, and dust opacity have strong effects on the ultimate location and shape of the final model grids in the mass luminosity plane. The mass loss rate evolution of the fundamental and first-overtone mode models show a significant difference in behavior. While the fundamental mode models exhibit the typically assumed power–law relation with mass and luminosity, the first-overtone mode models show significant non-power law behavior at observed mass loss rates. Effectively, models in the first-overtone mode require somewhat higher luminosities to reach the same mass loss rates seen in fundamental mode models of the same mass, consistent with observed AGB stars. [ABSTRACT FROM AUTHOR]

We use the Athena++ Monte Carlo (MC) radiation transfer module to postprocess simulation snapshots from nonrelativistic Athena++ radiation magnetohydrodynamic (RMHD) simulations. These simulations were run using a gray (frequency-integrated) approach but were also restarted and ran with a multigroup approach that accounts for Compton scattering with a Kompaneets operator. These simulations produced moderately super-Eddington accretion rates onto a 6.62 M ⊙ black hole. Since we only achieve inflow equilibrium out to 20–25 gravitational radii, we focus on the hard X-ray emission. We provide a comparison between the MC and RMHD simulations, showing that the treatment of Compton scattering in the gray RMHD simulations underestimates the gas temperature in the funnel regions above and below the accretion disk. In contrast, the restarted multigroup snapshots provide a treatment for the radiation field that is more consistent with the MC calculations, and result in postprocessed spectra with harder X-ray emission compared to their gray snapshot counterparts. We characterize these MC postprocessed spectra using commonly employed phenomenological spectral fitting models. We also attempt to fit our MC spectra directly to observations of the ultraluminous X-ray source (ULX) NGC 1313 X-1, finding best-fit values that are competitive to phenomenological model fits, indicating that first principle models of super-Eddington accretion may adequately explain the observed hard X-ray spectra in some ULX sources. [ABSTRACT FROM AUTHOR]

Very long baseline interferometry (VLBI) provides the highest-resolution images in astronomy. The sharpest resolution is nominally achieved at the highest frequencies, but as the observing frequency increases, so too does the atmospheric contribution to the system noise, degrading the sensitivity of the array and hampering detection. In this paper, we explore the limits of high-frequency VLBI observations using ngehtsim, a new tool for generating realistic synthetic data. ngehtsim uses detailed historical atmospheric models to simulate observing conditions, and it employs heuristic visibility detection criteria that emulate single- and multifrequency VLBI calibration strategies. We demonstrate the fidelity of ngehtsim's predictions using a comparison with existing 230 GHz data taken by the Event Horizon Telescope (EHT), and we simulate the expected performance of EHT observations at 345 GHz. Though the EHT achieves a nearly 100% detection rate at 230 GHz, our simulations indicate that it should expect substantially poorer performance at 345 GHz; in particular, observations of M87* at 345 GHz are predicted to achieve detection rates of ≲20% that may preclude imaging. Increasing the array sensitivity through wider bandwidths and/or longer integration times—as enabled through, e.g., the simultaneous multifrequency upgrades envisioned for the next-generation EHT—can improve the 345 GHz prospects and yield detection levels that are comparable to those at 230 GHz. M87* and Sgr A* observations carried out in the atmospheric window around 460 GHz could expect to regularly achieve multiple detections on long baselines, but analogous observations at 690 and 875 GHz consistently obtain almost no detections at all. [ABSTRACT FROM AUTHOR]

It is well known that multiple Galactic thermal dust emission components may exist along the line of sight, but a single-component approximation is still widely used, since a full multicomponent estimation requires a large number of frequency bands that are only available with future experiments. In light of this, we present a reliable, quantitative, and sensitive criterion to test the goodness of all kinds of dust emission estimations. This can not only give a definite answer to the quality of current single-component approximations but also help determine preconditions of future multicomponent estimations. Upon the former, previous works usually depend on a more complicated model to improve the single-component dust emission; however, our method is free from any additional model, and is sensitive enough to directly discover a substantial discrepancy between the Planck High Frequency Instrument data (100–857 GHz) and associated single-component dust emission estimations. This is the first time that the single-component estimation is ruled out by the data itself. For the latter, a similar procedure will be able to answer two important questions for estimating the complicated Galactic emissions: the number of necessary foreground components and their types.

We have conducted a sensitivity analysis on the mid-infrared spectral decomposition of galaxies and the modeling of the polycyclic aromatic hydrocarbon (PAH) emission spectrum with the NASA Ames PAH Infrared Spectroscopic Database (PAHdb) to assess the variance on the average galaxy PAH population properties under a grid of different modeling parameters. We find that the short-low and short-low+long-low Spitzer-IRS decomposition with PAHFIT provides consistent modeling and recovery of the 5–15 μ m PAH emission spectrum. For PAHdb modeling, application of a redshift to the calculated spectra to account for anharmonic effects introduces a 15%–20% variance on the derived parameters, while its absence improves the fits by ∼13%. The 4.00- α release of PAHdb achieves the complete modeling of the 6–15 μ m PAH spectrum, including the full 6.2 μ m band, improving the average fitting uncertainty by a factor of 2. The optimal PAHdb modeling configuration requires selection of pure PAHs without applying a redshift to the bands. Although quantitatively the PAHdb-derived parameters change under different modeling configurations or database versions, their variation follows a linear scaling, with previously reported trends remaining qualitatively valid. PAHdb modeling of JWST observations, and JWST observations smoothed and resampled to the Spitzer-IRS resolution and dispersion have consistent PAHdb derived parameters. Decomposition with different codes, such as PAHFIT and CAFE, produce PAH emission spectra with noticeable variation in the 11–15 μ m region, driving a ∼7% difference in the neutral PAH fraction under PAHdb modeling. A new library of galaxy PAH emission templates is delivered to be utilized in galaxy spectral energy distribution modeling.

A key goal of exoplanet spectroscopy is to measure atmospheric properties, such as abundances of chemical species, in order to connect them to our understanding of atmospheric physics and planet formation. In this new era of high-quality JWST data, it is paramount that these measurement methods are robust. When comparing atmospheric models to observations, multiple candidate models may produce reasonable fits to the data. Typically, conclusions are reached by selecting the best-performing model according to some metric. This ignores model uncertainty in favor of specific model assumptions, potentially leading to measured atmospheric properties that are overconfident and/or incorrect. In this paper, we compare three ensemble methods for addressing model uncertainty by combining posterior distributions from multiple analyses: Bayesian model averaging, a variant of Bayesian model averaging using leave-one-out predictive densities, and stacking of predictive distributions. We demonstrate these methods by fitting the Hubble Space Telescope (HST) + Spitzer transmission spectrum of the hot Jupiter HD 209458b using models with different cloud and haze prescriptions. All of our ensemble methods lead to uncertainties on retrieved parameters that are larger but more realistic and consistent with physical and chemical expectations. Since they have not typically accounted for model uncertainty, uncertainties of retrieved parameters from HST spectra have likely been underreported. We recommend stacking as the most robust model combination method. Our methods can be used to combine results from independent retrieval codes and from different models within one code. They are also widely applicable to other exoplanet analysis processes, such as combining results from different data reductions. [ABSTRACT FROM AUTHOR]

Radio frequency interference (RFI) mitigation for pulsar signals is a long perplexing issue in astrophysical measurements. Linear mitigation methods are often criticized for limited RFI excision range and weakness of RFI modeling. Meanwhile, thresholding methods (e.g., the SumThreshold) suffer from empirical factors. In our opinion, the main defect of the current status is the lack of a concise definition to distinguish signals from RFI with the aid of certain techniques, e.g., sparse representation. This point is the root cause of these problems and also forms our motivation. This paper aims to expand the excision range (e.g., the on-pulse and quasiperiodic RFI) and cut down some empirical factors. The main contribution is that we give a definition and derive a widely practicable nonlinear framework for RFI excision. This framework can overcome the susceptibility of the least-square criterion to RFI, and excise almost all types of RFI once and for all. A robust LnCosh criterion based nonlinear maximum likelihood-type (M-type) penalized smoothing estimator is introduced. The novelty is that this estimator is first embedded into the iterative shrinkage-thresholding algorithm (ISTA) and the fast ISTA. Nonlinearity highlights this method. Curvelet sparsity gives satisfying approximation for pulsar signals containing dispersion feature. Finally, useful signal details will be retrieved from the excision residual by a morphological component analysis. This method is applied to the time-frequency signals collected by the Nanshan 26 m Radio Telescope. The numerical experiments can persuasively prove that it has desired application prospects. [ABSTRACT FROM AUTHOR]

To date, the search for radio technosignatures has focused on sky location as a primary discriminant between technosignature candidates and anthropogenic radio frequency interference (RFI). In this work, we investigate the possibility of searching for technosignatures by identifying the presence and nature of intensity scintillations arising from the turbulent, ionized plasma of the interstellar medium. Past works have detailed how interstellar scattering can both enhance and diminish the detectability of narrowband radio signals. We use the NE2001 Galactic free electron density model to estimate scintillation timescales to which narrowband signal searches would be sensitive, and discuss ways in which we might practically detect strong intensity scintillations in detected signals. We further analyze the RFI environment of the Robert C. Byrd Green Bank Telescope with the proposed methodology and comment on the feasibility of using scintillation as a filter for technosignature candidates. [ABSTRACT FROM AUTHOR]

Deep learning techniques are required for the analysis of synoptic (multi-band and multi-epoch) light curves in massive data of quasars, as expected from the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). In this follow-up study, we introduce an upgraded version of a conditional neural process (CNP) embedded in a multi-step approach for the analysis of large data of quasars in the LSST Active Galactic Nuclei Scientific Collaboration data challenge database. We present a case study of a stratified set of u-band light curves for 283 quasars with very low variability ∼0.03. In this sample, the CNP average mean square error is found to be ∼5% (∼0.5 mag). Interestingly, besides similar levels of variability, there are indications that individual light curves show flare-like features. According to the preliminary structure–function analysis, these occurrences may be associated with microlensing events with larger time scales of 5–10 years. [ABSTRACT FROM AUTHOR]

We combine data sets from the CGM2 and CASBaH surveys to model a transition point, R cross, between circumgalactic and intergalactic media (CGM and IGM, respectively). In total, our data consist of 7244 galaxies at z < 0.5 with precisely measured spectroscopic redshifts, all having impact parameters of 0.01–20 comoving Mpc from 28 QSO sightlines with high-resolution UV spectra that cover H i Ly α. Our best-fitting model is a two-component model that combines a 3D absorber–galaxy cross-correlation function with a simple Gaussian profile at inner radii to represent the CGM. By design, this model gives rise to a determination of R cross as a function of galaxy stellar mass, which can be interpreted as the boundary between the CGM and IGM. For galaxies with 108 ≤ M ⋆/ M ⊙ ≤ 1010.5, we find that R cross(M ⋆) ≈ 2.0 ± 0.6 R vir. Additionally, we find excellent agreement between R cross(M ⋆) and the theoretically determined splashback radius for galaxies in this mass range. Overall, our results favor models of galaxy evolution at z < 0.5 that distribute T ≈ 104K gas to distances beyond the virial radius. [ABSTRACT FROM AUTHOR]

I report on validation and testing of a novel 3D reconstruction method than can obtain coronal plasma properties from a single snapshot perspective. I first reported on the method in 2021, and I have since named it the Coronal Reconstruction Onto B -Aligned Regions (CROBAR) method. The testing and validation are carried out with a cube from a MURaM 3D MHD simulation, which affords a coronal-like "ground truth" against which the reconstruction method can be applied and compared. I find that the method does quite well, recovering the "coronal veil"−like features recently reported from the MURaM simulations and allaying concerns that these features would thwart recovery of valid 3D coronal structure from a limited number of perspectives. I also find that a second perspective between ∼45° and 90° does significantly improve the reconstructions. Two distinct channels with soft-X-ray-like temperature response (peaking above 5 MK) would suffice for CROBAR's optically thin observables A suite of AIA-like EUV passbads, with good coverage in the 3–8 MK range, is also well suited to CROBAR. [ABSTRACT FROM AUTHOR]

The Markarian Multiwavelength Data Center ( MMDC ) is a web-based tool designed for accessing and retrieving multiwavelength and multimessenger data from blazar observations. MMDC facilitates the construction and interactive visualization of time-resolved multiband spectral energy distributions of blazars by integrating (i) archival data from over 80 catalogs and databases, (ii) optical data from all-sky survey facilities such as the All-Sky Automated Survey for Supernovae, Zwicky Transient Facility, and Panoramic Survey Telescope and Rapid Response System, and (iii) newly analyzed data sets in the optical/UV band from Swift-UVOT, in the X-ray band from Swift-X-Ray Telescope and NuSTAR observations, and the high-energy γ -ray band from Fermi-Large Area Telescope observations. MMDC distinguishes itself from other online platforms by the large quantity of available data. For instance, it includes data from all blazar observations by Swift and NuSTAR, as well as the results of detailed spectral analysis in the γ -ray band during different emission states, covering the period from 2008 to 2023. Another important distinguishing feature of MMDC is its ability to enable precise, self-consistent theoretical modeling of the observed data using machine learning algorithms trained on leptonic and lepto-hadronic models, which consider the injection of particles and all relevant cooling processes. MMDC is an innovative tool that significantly enhances blazar research by providing a comprehensive framework for data accessibility, analysis, and theoretical interpretation, thereby advancing our understanding of blazar emissions and the underlying astrophysical processes.

The search for extraterrestrial intelligence at radio frequencies has focused on spatial filtering as a primary discriminant from terrestrial interference. Individual search campaigns further choose targets or frequencies based on criteria that theoretically maximize the likelihood of detection, serving as high-level filters for interesting targets. Most filters for technosignatures do not rely on intrinsic signal properties, as the radio-frequency interference (RFI) environment is difficult to characterize. In B. Brzycki et al. (2023), we proposed that the effects of interstellar medium (ISM) scintillation on narrowband technosignatures may be detectable under certain conditions. In this work, we perform a dedicated survey for scintillated technosignatures toward the Galactic center and Galactic plane at the C band (3.95–8.0 GHz) using the Robert C. Byrd Green Bank Telescope (GBT) as part of the Breakthrough Listen program. We conduct a Doppler drift search and directional filter to identify potential candidates and analyze results for evidence of scintillation. We characterize the C -band RFI environment at the GBT across multiple observing sessions spread over months and detect RFI signals with confounding scintillation-like intensity modulation. We do not find evidence of putative narrowband transmitters with drift rates between ±10 Hz s ^−1 toward the Galactic center, ISM-scintillated or otherwise, above an equivalent isotropic radiated power of 1.9 × 10 ^17 W up to 8.5 kpc.

The discovery of two thin rings around the ∼ 250 km sized Centaur Chariklo was the first of its kind, and their formation and evolutionary mechanisms are not well understood. Here, we explore a single shepherd satellite as a mechanism to confine Chariklo’s rings. We also investigate the impact of such a perturber on reaccretion, which is a likely process for material located outside the Roche limit. We have modified N -body code that was developed for Saturn’s rings to model the Chariklo system. Exploration of a reasonable parameter space indicates that rings like those observed could be stable as the result of a single satellite with a mass of a few ×10 ^13 kg that is in orbital resonance with the rings. There is a roughly linear relationship between the model optical depth and the mass of the satellite required to confine a ring. Ring particles do not accrete into moonlets during hard-sphere simulations. However, a reasonable fraction of the ring material forms into moonlets after a few tens of orbits for soft-sphere collisions. The ring-particle properties are thus key parameters in terms of moonlet accretion or destruction in this system.

The existing superresolution (SR) models for solar magnetograms are mostly borrowed from the SR models for natural images. They are less effective for processing solar magnetograms with a very large dynamic range and very rich image features. In this paper, a multibranch superresolution (MBSR) model is specially designed for solar magnetograms. First, we split a low-resolution magnetogram into a group of overlapping image patches, and classify them into three categories according to magnetic flux intensity, namely simple, medium, and complex. Then, image patches of each category are fed into the corresponding branch of the MBSR network, the lightweight branch for simple image patches and the heavyweight one for complex image patches. The advantage of such a strategy is twofold. On the one hand, active regions are allocated more computational resources to train a heavyweight branch more fully, while quiet regions are allocated fewer computational resources to train a lightweight branch for saving computational resources. On the other hand, a lightweight network with a simple nonlinear function is preferable to simple regions, while a heavyweight one may be underfitting. Additionally, to verify the effectiveness of the proposed model, a magnetic field structure similarity metric is proposed to measure the artifacts of the generated high-resolution (HR) magnetograms. Experimental results show that the proposed MBSR model generates HR magnetograms highly consistent with the HMI ones, and achieves the best performance over five objective metrics, including peak signal-to-noise ratio and structure similarity, etc.

Multiwavelength observations are now the norm for studying blazars’ various states of activity, classifying them, and determining the possible underlying physical processes driving their emission. Broadband emission models became unavoidable tools for testing emission scenarios and setting the values of physical quantities such as the magnetic field strength, Doppler factor, or shape of the particle distribution of the emission zone(s). We announce here the first public release of a new tool, Bjet _ MCMC , that can automatically fit the broadband spectral energy distributions (SEDs) of blazars. The complete code is available on GitHub and allows for testing leptonic synchrotron self-Compton models with or without external inverse-Compton processes from the thermal environment of supermassive black holes (accretion disk and broad-line region). The code is designed to be user-friendly and computationally efficient. It contains a core written in C++ and a fully parallelized SED fitting method. The original multi-SSC zone model of Bjet is also available on GitHub but is not included in the Markov Chain Monte Carlo fitting process at the moment. We present the features, performance, and results of Bjet _ MCMC , as well as user advice.

The variable continuum emission of an active galactic nucleus (AGN) produces corresponding responses in the broad emission lines, which are modulated by light travel delays, and contain information on the physical properties, structure, and kinematics of the emitting gas region. The reverberation mapping technique, a time series analysis of the driving light curve and response, can recover some of this information, including the size and velocity field of the broad-line region (BLR). Here we introduce a new forward-modeling tool, the Broad Emission Line MApping Code, which simulates the velocity-resolved reverberation response of the BLR to any given input light curve by setting up a 3D ensemble of gas clouds for various specified geometries, velocity fields, and cloud properties. In this work, we present numerical approximations to the transfer function by simulating the velocity-resolved responses to a single continuum pulse for sets of models representing a spherical BLR with a radiatively driven outflow and a disklike BLR with Keplerian rotation. We explore how the structure, velocity field, and other BLR properties affect the transfer function. We calculate the response-weighted time delay (reverberation “lag”), which is considered to be a proxy for the luminosity-weighted radius of the BLR. We investigate the effects of anisotropic cloud emission and matter-bounded (completely ionized) clouds and find the response-weighted delay is only equivalent to the luminosity-weighted radius when clouds emit isotropically and are radiation-bounded (partially ionized). Otherwise, the luminosity-weighted radius can be overestimated by up to a factor of 2.

Active galactic nuclei (AGNs) can power relativistic jets, which are called blazars when pointed close to our line of sight. Depending on the presence or absence of emission lines in their optical spectra, blazars are categorized into flat spectrum radio quasars (FSRQs) or BL Lacertae (BL Lac) objects. According to the “blazar sequence,” as synchrotron peak frequency ( ${\nu }_{\mathrm{pk}}^{\mathrm{sy}}$ ) shifts to higher energies, the synchrotron peak luminosity decreases. This means that BL Lac objects as luminous as FSRQs, and with synchrotron peak frequencies ${\nu }_{\mathrm{pk}}^{\mathrm{sy}}\gt {10}^{15}$ Hz, should not exist. Detected as a high-synchrotron peak (HSP; ${\nu }_{\mathrm{pk}}^{\mathrm{sy}}\gt {10}^{15}$ Hz) BL Lac object, 4FGL J1520.8-0348 shows high γ -ray luminosity ( L _γ > 10 ^46 erg s ^−1 ), being at a high redshift of z = 1.46. Since it is an outlier in the “blazar sequence,” the process of its jet acceleration and power may be different from bona fide BL Lac objects. In this work, we constrain its spectral energy distribution (SED) by modeling the multiwavelength data from infrared to γ -ray regime. Simultaneous X-ray data were obtained from X-ray Multi-Mirror Mission and Nuclear Spectroscopic Telescope Array to constrain the synchrotron emission and underlying electron distribution. On undertaking the SED modeling of the source, including the effect of extragalactic background light, we conclude that the source is more likely to be a “blue FSRQ” or “masquerading BL Lac” where the BL Lac object is actually an FSRQ in disguise.

Data-driven models of stellar spectra are useful tools to study nonstellar information, such as the diffuse interstellar bands (DIBs) caused by intervening interstellar material. Using ∼55,000 spectra of ∼17,000 red clump stars from the APOGEE DR16 data set, we create second-order polynomial models of the continuum-normalized flux as a function of stellar parameters ( T _eff , $\mathrm{log}g$ , [Fe/H], [ α /Fe], and age). The model and data show good agreement within uncertainties across the APOGEE wavelength range, although many regions reveal residuals that are not in the stellar rest-frame. We show that many of these residual features—having average extrema at the level of ∼3% in stellar flux on average—can be attributed to incompletely removed spectral lines from the Earth’s atmosphere and DIBs from the interstellar medium (ISM). After removing most of the remaining contamination from Earth’s sky, we identify 84 absorption features not seen in unreddened sightlights that have

Gaussian process (GP) regression is a nonparametric Bayesian approach that has been used successfully in various astronomical domains, especially in time-domain astronomy. The most common applications are the smoothing of data for interpolation and the detection of periodicities. The ability to create unbiased data-driven models without a predefined physical model can be a major advantage over conventional regression methods. Prior knowledge can be included by setting boundary conditions or constraining hyperparameter values, while unknown hyperparameters are optimized during the conditioning of the model. We have adapted and transformed previous approaches of GP regression and introduce three new applications for this regression method, especially in the context of stellar occultations: the modeling of occultation light curves, the correction of public JPL ephemerides of minor planets based on publicly available image data of the Zwicky Transient Facility, and the detection of natural satellites. We used data from observations of stellar occultations to validate the models and achieved promising results in all cases, and thus we confirmed the flexibility of GP regression models. Considering various existing use cases in addition to our novel applications, GP regression can be used to model diverse data sets addressing a wide range of problems. The accuracy of the model depends on the input data and on the set boundary conditions. Generally, high-quality data allow the usage of loose boundary conditions, while low-quality data require more restrictive boundary conditions to avoid overfitting.

We use the Very Energetic Radiation Imaging telescope Array System (VERITAS) imaging air Cherenkov telescope array to obtain the first measured angular diameter of β UMa at visual wavelengths using stellar intensity interferometry (SII) and independently constrain the limb-darkened angular diameter. The age of the Ursa Major moving group has been assessed from the ages of its members, including nuclear member Merak ( β UMa), an A1-type subgiant, by comparing effective temperature and luminosity constraints to model stellar evolution tracks. Previous interferometric limb-darkened angular-diameter measurements of β UMa in the near-infrared (Center for High Angular Resolution Astronomy (CHARA) Array, 1.149 ± 0.014 mas) and mid-infrared (Keck Nuller, 1.08 ± 0.07 mas), together with the measured parallax and bolometric flux, have constrained the effective temperature. This paper presents current VERITAS-SII observation and analysis procedures to derive squared visibilities from correlation functions. We fit the resulting squared visibilities to find a limb-darkened angular diameter of 1.07 ± 0.04 (stat) ± 0.05 (sys) mas, using synthetic visibilities from a stellar atmosphere model that provides a good match to the spectrum of β UMa in the optical wave band. The VERITAS-SII limb-darkened angular diameter yields an effective temperature of 9700 ± 200 ± 200 K, consistent with ultraviolet spectrophotometry, and an age of 390 ± 29 ± 32 Myr, using MESA Isochrones and Stellar Tracks. This age is consistent with 408 ± 6 Myr from the CHARA Array angular diameter.

We present molecular gas-dynamical mass measurements of the central black holes in the giant elliptical galaxies NGC 4786 and NGC 5193, based on CO (2−1) observations from the Atacama Large Millimeter/submillimeter Array (ALMA) and Hubble Space Telescope near-infrared imaging. The central region in each galaxy contains a circumnuclear disk that exhibits orderly rotation with projected line-of-sight velocities of ∼270 km s ^−1 . We build gas-dynamical models for the rotating disk in each galaxy and fit them directly to the ALMA data cubes. At 0.″31 resolution, the ALMA observations do not fully resolve the black hole sphere of influence (SOI), and neither galaxy exhibits a central rise in rotation speed, indicating that emission from deep within the SOI is not detected. As a result, our models do not tightly constrain the central black hole mass in either galaxy, but they prefer the presence of a central massive object in both galaxies. We measure the black hole mass to be $({M}_{\mathrm{BH}}/{10}^{8}\,{M}_{\odot })=5.0\pm 0.2\,[1\sigma \,\mathrm{statistical}]{\,}_{-1.3}^{+1.4}\,[\mathrm{systematic}]$ in NGC 4786 and $({M}_{\mathrm{BH}}/{10}^{8}\,{M}_{\odot })=1.4\pm 0.03\,[1\sigma \,\mathrm{statistical}{]}_{-0.1}^{+1.5}\,[\mathrm{systematic}]$ in NGC 5193. The largest component of each measurement’s error budget is from the systematic uncertainty associated with the extinction correction in the host galaxy models. This underscores the importance of assessing the impact of dust attenuation on the inferred M _BH .

In the field of astronomy, machine-learning technologies are becoming increasingly crucial for identifying radio pulsars. However, the process of acquiring labeled data, which is both time-consuming and potentially biased, poses a significant limitation to current methodologies. In response to these challenges, this study proposes and validates a self-tuning pseudolabeling semisupervised learning approach. This approach synthesizes a vast unlabeled data set with a considerably smaller set of labeled data, markedly enhancing classifier performance and effectuating a transition from traditional fully supervised learning methods to more efficient radio pulsar detection strategies. Our experimental outcomes demonstrate that even with a training set comprised of only 100 labeled pulsar candidates, this method can attain a recall rate of 92.35% and an F1 score of 93.89%. When the number of labeled examples is increased to 800, we observe a further improvement in performance, with the recall rate rising to 97.50% and the F1 score reaching 97.16%. The utility of the semisupervised learning approach is evident even with minimal labeled data, which is a common scenario in the search for pulsars, including in environments like globular clusters. What stands out is the method’s capacity to detect pulsar candidates effectively with only a limited number of labeled examples. This emphasizes the robust potential of our approach to facilitate early-stage pulsar surveys and highlights its capability to yield substantial results even when labeled data are in short supply.

We present an analysis of high-angular-resolution images of the microlensing target MOA-2007-BLG-192 using Keck adaptive optics and the Hubble Space Telescope. The planetary host star is robustly detected as it separates from the background source star in nearly all of the Keck and Hubble data. The amplitude and direction of the lens–source separation allows us to break a degeneracy related to the microlensing parallax and source radius crossing time. Thus, we are able to reduce the number of possible binary-lens solutions by a factor of ∼2, demonstrating the power of high-angular-resolution follow-up imaging for events with sparse light-curve coverage. Following Bennett et al., we apply constraints from the high-resolution imaging on the light-curve modeling to find host star and planet masses of M _host = 0.28 ± 0.04 M _☉ and ${m}_{p}={12.49}_{-8.03}^{+65.47}\,{M}_{\oplus }$ at a distance from Earth of D _L = 2.16 ± 0.30 kpc. This work illustrates the necessity for the Nancy Grace Roman Galactic Exoplanet Survey to use its own high-resolution imaging to inform light-curve modeling for microlensing planets that the mission discovers.

Multiwavelength modeling of the synchrotron radiation from relativistic transients such as gamma-ray burst (GRB) afterglows is a powerful means of exploring the physics of relativistic shocks and of deriving properties of the explosion, such as the kinetic energy of the associated relativistic outflows. Capturing the location and evolution of the synchrotron cooling break is critical to break parameter degeneracies associated with such modeling. However, the shape of the spectrum above the cooling break, as well as the location and evolution of the break itself can be significantly altered by synchrotron self-Compton (SSC) cooling. We present an observer’s guide to applying SSC cooling with and without Klein–Nishina (KN) corrections to GRB afterglow modeling. We provide a publicly available Python code to calculate the Compton Y -parameter as a function of electron Lorentz factor, from which we compute changes to the electron distribution, along with KN-corrected afterglow spectra and light curves. In this framework, the canonical synchrotron spectral shapes split into multiple subregimes. We summarize each new spectral shape and describe its observational significance. We discuss how KN corrections can account for harder spectra and shallower decline rates observed in some GRB X-ray afterglows. Our overall aim is to provide an easy application of SSC+KN corrections into analytical multiwavelength modeling frameworks for relativistic transients.

In the past decade, various sky surveys with a wide range of wavelengths have been conducted, resulting in an explosive growth of survey data. There may be overlapping regions between different surveys, but the data quality and brightness are different. The translation of data quality between different surveys provides benefits for studying the properties of galaxies in specific regions that high-quality surveys have not yet covered. In this paper, we create a data set for analyzing the quality transformation of different surveys, AstroSR, using the galaxy images from overlapping regions from the Subaru/Hyper Suprime-Cam (HSC) and the Sloan Digital Sky Survey (SDSS). In addition, we use superresolution (SR) techniques to improve the quality of low-resolution images in the AstroSR and explore whether the proposed data set is suitable for SR. We try four representative models: EDSR, RCAN, ENLCN, and SRGAN. Finally, we compare the evaluation metrics and visual quality of the above methods. SR models trained with AstroSR successfully generate HSC-like images from SDSS images, which enhance the fine structure present in the SDSS images while retaining important morphological information and increasing the brightness and signal-to-noise. Improving the resolution of astronomical images by SR can improve the size and quality of the sky surveys. The data set proposed in this paper provides strong data support for the study of galaxy SR and opens up new research possibilities in astronomy. The data set is available online at https://github.com/jiaweimmiao/AstroSR .

The recent development of statistical methods that can distinguish between stellar activity and dynamical signals in radial velocity (RV) observations has facilitated the discovery and characterization of planets orbiting young stars. One such technique, Gaussian process (GP) regression, has been regularly employed to improve the detection of a growing number of planets, but the impact of this model for mitigating stellar activity has not been uniformly analyzed for a large sample with real observations. The goal of this study is to investigate how GPs can affect the inferred parameters of RV-detected planets. We homogeneously analyze how two commonly adopted GP frameworks, a GP trained on RVs alone and a GP pretrained on photometry and then applied to RVs, can influence the inferred physical and orbital parameters compared to a traditional Keplerian orbit fit. Our sample comprises 17 short-period giant planets orbiting stars that exhibit a broad range of activity levels. We find that the decision to adopt GPs, as well as the choice of GP framework, can result in variations of inferred parameters such as minimum planet mass and eccentricity by up to 67% and 95%, respectively. This implies that the method for modeling stellar activity in RVs of young planet-hosting stars can have widespread ramifications on the interpretation of planet properties including their masses, densities, circularization timescales, and tidal quality factors. When mitigating stellar activity with GPs, we recommend carrying out comparative tests between different models to assess the sensitivity of planet physical and orbital parameters to these choices.

Main-sequence radio pulse emitters (MRPs) are magnetic early-type stars that produce coherent radio emission observed in the form of periodic radio pulses. The emission mechanism behind this is the electron-cyclotron maser emission (ECME). Among all kinds of magnetospheric emission, ECME is unique due to its high directivity and intrinsically narrow bandwidth. The emission is also highly circularly polarized and the sign of polarization is opposite for the two magnetic hemispheres. This combination of properties makes ECME highly sensitive to the three-dimensional structures in the stellar magnetospheres. This is especially significant for late-B and A-type magnetic stars that do not emit other types of magnetospheric emission such as H α , the key probe used to trace magnetospheric densities. In this paper, we use an ultra-wideband observation (0.4–2 GHz) of a late B-type MRP HD 133880 to demonstrate how we can extract information on plasma distribution from ECME. We achieve this by examining the differences in pulse arrival times (“lags”) as a function of frequencies and qualitatively comparing those with lags obtained by simulating ECME ray paths in hot stars’ magnetospheres. This reveals that the stellar magnetosphere has a disk-like overdensity inclined to the magnetic equator with a centrally concentrated density that primarily affects the intermediate frequencies (400–800 MHz). This result, which is consistent with the recent density model proposed for hotter centrifugally supported magnetospheres, lends support to the idea of a unifying model for magnetospheric operations in early-type stars, and also provides further motivation to fully characterize the ECME phenomenon in large-scale stellar magnetospheres.

We use simulated galaxy observations from the NIHAO-SKIRT-Catalog to test the accuracy of spectral energy distribution (SED) modeling techniques. SED modeling is an essential tool for inferring star formation histories from nearby galaxy observations but is fraught with difficulty due to our incomplete understanding of stellar populations, chemical enrichment processes, and the nonlinear, geometry-dependent effects of dust. The NIHAO-SKIRT-Catalog uses hydrodynamic simulations and radiative transfer to produce SEDs from the ultraviolet (UV) through the infrared (IR), accounting for dust. We use the commonly used Prospector software to perform inference on these SEDs and compare the inferred stellar masses and star formation rates (SFRs) to the known values in the simulation. We match the stellar population models to isolate the effects of differences in the star formation history, the chemical evolution history, and the dust. For the high-mass NIHAO galaxies (>10 ^9.5 M _⊙ ), we find that model mismatches lead to inferred SFRs that are on average underestimated by a factor of 2 when fit to UV through IR photometry, and a factor of 3 when fit to UV through optical photometry. These biases lead to significant inaccuracies in the resulting specific SFR–mass relations, with UV through optical fits showing particularly strong deviations from the true relation of the simulated galaxies. In the context of massive existing and upcoming photometric surveys, these results highlight that star formation history inference from photometry may remain imprecise and inaccurate and that there is a pressing need for more realistic testing of existing techniques.

Object GRB 221009A is the brightest gamma-ray burst (GRB) detected in more than 50 yr of study. In this paper, we present observations in the X-ray and optical domains obtained by the GRANDMA Collaboration and the Insight Collaboration. We study the optical afterglow with empirical fitting using the GRANDMA+HXMT-LE data sets augmented with data from the literature up to 60 days. We then model numerically using a Bayesian approach, and we find that the GRB afterglow, extinguished by a large dust column, is most likely behind a combination of a large Milky Way dust column and moderate low-metallicity dust in the host galaxy. Using the GRANDMA+HXMT-LE+XRT data set, we find that the simplest model, where the observed afterglow is produced by synchrotron radiation at the forward external shock during the deceleration of a top-hat relativistic jet by a uniform medium, fits the multiwavelength observations only moderately well, with a tension between the observed temporal and spectral evolution. This tension is confirmed when using the augmented data set. We find that the consideration of a jet structure (Gaussian or power law), the inclusion of synchrotron self-Compton emission, or the presence of an underlying supernova do not improve the predictions. Placed in the global context of GRB optical afterglows, we find that the afterglow of GRB 221009A is luminous but not extraordinarily so, highlighting that some aspects of this GRB do not deviate from the global known sample despite its extreme energetics and the peculiar afterglow evolution.

The newfound ability to detect SO _2 in exoplanet atmospheres presents an opportunity to measure sulfur abundances and so directly test between competing modes of planet formation. In contrast to carbon and oxygen, whose dominant molecules are frequently observed, sulfur is much less volatile and resides almost exclusively in solid form in protoplanetary disks. This dichotomy leads different models of planet formation to predict different compositions of gas giant planets. Whereas planetesimal-based models predict roughly stellar C/S and O/S ratios, pebble-accretion models more often predict superstellar ratios. To explore the detectability of SO _2 in transmission spectra and its ability to diagnose planet formation, we present a grid of atmospheric photochemical models and corresponding synthetic spectra for WASP-39b (where SO _2 has been detected). Our 3D grid contains 11 ^3 models (spanning 1–100× the solar abundance ratio of C, O, and S) for thermal profiles corresponding to the morning and evening terminators, as well as mean terminator transmission spectra. Our models show that for a WASP-39b-like O/H and C/H enhancement of ∼10× solar, SO _2 can only be seen for C/S and O/S ≲ 1.5× solar, and that WASP-39b’s reported SO _2 abundance of 1–10 ppm may be more consistent with planetesimal accretion than with pebble-accretion models (although some pebble models also manage to predict similarly low ratios). More extreme C/S and O/S ratios may be detectable in higher-metallicity atmospheres, suggesting that smaller and more metal-rich gas and ice giants may be particularly interesting targets for testing planet formation models. Future studies should explore the dependence of SO _2 on a wider array of planetary and stellar parameters, both for the prototypical SO _2 planet WASP-39b, as well as for other hot Jupiters and smaller gas giants.

Data from high-energy observations are usually obtained as lists of photon events. A common analysis task for such data is to identify whether diffuse emission exists, and to estimate its surface brightness, even in the presence of point sources that may be superposed. We have developed a novel nonparametric event list segmentation algorithm to divide up the field of view into distinct emission components. We use photon location data directly, without binning them into an image. We first construct a graph from the Voronoi tessellation of the observed photon locations and then grow segments using a new adaptation of seeded region growing that we call Seeded Region Growing on Graph , after which the overall method is named SRGonG . Starting with a set of seed locations, this results in an oversegmented data set, which SRGonG then coalesces using a greedy algorithm where adjacent segments are merged to minimize a model comparison statistic; we use the Bayesian Information Criterion. Using SRGonG we are able to identify point-like and diffuse extended sources in the data with equal facility. We validate SRGonG using simulations, demonstrating that it is capable of discerning irregularly shaped low-surface-brightness emission structures as well as point-like sources with strengths comparable to that seen in typical X-ray data. We demonstrate SRGonG ’s use on the Chandra data of the Antennae galaxies and show that it segments the complex structures appropriately.

The Probabilistic Value-Added Bright Galaxy Survey (PROVABGS) catalog will provide the posterior distributions of physical properties of >10 million DESI Bright Galaxy Survey galaxies. Each posterior distribution will be inferred from joint Bayesian modeling of observed photometry and spectroscopy using Markov Chain Monte Carlo sampling and the Hahn et al. stellar population synthesis (SPS) model. To make this computationally feasible, PROVABGS will use a neural emulator for the SPS model to accelerate the posterior inference. In this work, we present how we construct the emulator using the Alsing et al. approach and verify that it can be used to accurately infer galaxy properties. We confirm that the emulator is in excellent agreement with the original SPS model with ≪1% error and is 100× faster. In addition, we demonstrate that the posteriors of galaxy properties derived using the emulator are also in excellent agreement with those inferred using the original model. The neural emulator presented in this work is essential in bypassing the computational challenge posed in constructing the PROVABGS catalog. Furthermore, it demonstrates the advantages of emulation for scaling sophisticated analyses to millions of galaxies.

Starless cores represent the initial stage of evolution toward (proto)star formation, and a subset of them, known as prestellar cores, with high density (∼ 10 ^6 cm ^−3 or higher) and being centrally concentrated are expected to be embryos of (proto)stars. Determining the density profile of prestellar cores therefore provides an important opportunity to gauge the initial conditions of star formation. In this work, we perform rigorous modeling to estimate the density profiles of three nearly spherical prestellar cores among a sample of five highly dense cores detected by our recent observations. We employed multiscale observational data of the (sub)millimeter dust continuum emission, including those obtained by SCUBA-2 on the James Clerk Maxwell Telescope with a resolution of ∼ 5600 au and by multiple Atacama Large Millimeter/submillimeter Array observations with a resolution as high as ∼ 480 au. We are able to consistently reproduce the observed multiscale dust continuum images of the cores with a simple prescribed density profile, which bears an inner region of flat density and an r ^−2 profile toward the outer region. By utilizing the peak density and the size of the inner flat region as a proxy for the dynamical stage of the cores, we find that the three modeled cores are most likely unstable and prone to collapse. The sizes of the inner flat regions, as compact as ∼ 500 au, signify them as being the highly evolved prestellar cores rarely found to date.

Developments in the stability of modern spectrographs have led to extremely precise instrumental radial velocity (RV) measurements. For most stars, the detection limit of planetary companions with these instruments is expected to be dominated by astrophysical noise sources such as starspots. Correlated signals caused by rotationally modulated starspots can obscure or mimic the Doppler shifts induced by even the closest, most massive planets. This is especially true for young, magnetically active stars where stellar activity can cause fluctuation amplitudes of ≳0.1 mag in brightness and ≳100 m s ^−1 in RV semiamplitudes. Techniques that can mitigate these effects and increase our sensitivity to young planets are critical to improving our understanding of the evolution of planetary systems. Gaussian processes (GPs) have been successfully employed to model and constrain activity signals in individual cases. However, a principled approach of this technique, specifically for the joint modeling of photometry and RVs, has not yet been developed. In this work, we present a GP framework to simultaneously model stellar activity signals in photometry and RVs that can be used to investigate the relationship between both time series. Our method, inspired by the FF ${}^{{\prime} }$ framework of Aigrain et al., models spot-driven activity signals as the linear combinations of two independent latent GPs and their time derivatives. We also simulate time series affected by starspots by extending the starry software to incorporate time evolution of stellar features. Using these synthetic data sets, we show that our method can predict spot-driven RV variations with greater accuracy than other GP approaches.

To facilitate the study of solar flares and active regions, we have created a modeling framework, the freely distributed GX Simulator IDL package, that combines 3D magnetic and plasma structures with thermal and nonthermal models of the chromosphere, transition region, and corona. Its object-based modular architecture, which runs on Windows, Mac, and Unix/Linux platforms, offers the ability to either import 3D density and temperature distribution models, or to assign numerically defined coronal or chromospheric temperatures and densities, or their distributions, to each individual voxel. GX Simulator can apply parametric heating models involving average properties of the magnetic field lines crossing a given voxel, as well as compute and investigate the spatial and spectral properties of radio, (sub)millimeter, EUV, and X-ray emissions calculated from the model, and quantitatively compare them with observations. The package includes a fully automatic model production pipeline that, based on minimal users input, downloads the required SDO/HMI vector magnetic field data, performs potential or nonlinear force-free field extrapolations, populates the magnetic field skeleton with parameterized heated plasma coronal models that assume either steady-state or impulsive plasma heating, and generates non-LTE density and temperature distribution models of the chromosphere that are constrained by photospheric measurements. The standardized models produced by this pipeline may be further customized through specialized IDL scripts, or a set of interactive tools provided by the graphical user interface. Here, we describe the GX Simulator framework and its applications.

A planet’s orbital eccentricity is fundamental to understanding the present dynamical state of a system and is a relic of its formation history. There is high scientific value in measuring the eccentricities of Kepler and Transiting Exoplanet Survey Satellite (TESS) planets given the sheer size of these samples and the diversity of their planetary systems. However, Kepler and TESS light curves typically only permit robust determinations of the planet-to-star radius ratio r , orbital period P , and transit midpoint t _0 . Three other orbital properties, including the impact parameter b , eccentricity e , and argument of periastron ω , are more challenging to measure because they are all encoded in the light curve through subtle effects on a single observable—the transit duration T _14 . In Gilbert et al., we showed that a five-parameter transit description { P , t _0 , r , b , T _14 } naturally yields unbiased measurements of r and b . Here, we build upon our previous work and introduce an accurate and efficient prescription to measure e and ω . We validate this approach through a suite of injection-and-recovery experiments. Our method agrees with previous approaches that use a seven-parameter transit description { P , t _0 , r , b , ρ _⋆ , e , ω }, which explicitly fits the eccentricity vector and mean stellar density. The five-parameter method is simpler than the seven-parameter method and is “future-proof” in that posterior samples can be quickly reweighted (via importance sampling) to accommodate updated priors and updated stellar properties. This method thus circumvents the need for an expensive reanalysis of the raw photometry, offering a streamlined path toward large-scale population analyses of the eccentricity from transit surveys.

In this empirical work, we aim to quantify the systematic uncertainties in stellar-mass ( M _⋆ ) estimates made from spectral energy distribution (SED) fitting through stellar population synthesis (SPS) for galaxies in the local Universe by using the dynamical mass ( M _dyn ) estimator as an SED-independent check on stellar mass. We first construct a statistical model of the high-dimensional space of galaxy properties; including size ( R _e ), velocity dispersion ( σ _e ), surface brightness ( I _e ), mass-to-light ratio ( M _⋆ / L ), rest-frame color, Sérsic index ( n ), and dynamical mass ( M _dyn ), and accounting for selection effects and covariant errors. We disentangle the correlations among galaxy properties and find that the variation in M _⋆ / M _dyn is driven by σ _e , Sérsic index and color. We use these parameters to calibrate an SED-independent M _⋆ estimator, ${\hat{M}}_{\star }$ . We find the random scatter of the relation ${M}_{\star }-{\hat{M}}_{\star }$ to be 0.108 dex and 0.147 dex for quiescent and star-forming galaxies, respectively. Finally, we inspect the residuals as a function of SPS parameters (dust, age, metallicity, and star formation rate) and spectral indices (H α , H δ , and D _n 4000). For quiescent galaxies, ∼65% of the scatter can be explained by the uncertainty in SPS parameters, with dust and age being the largest sources of uncertainty. For star-forming galaxies, while age and metallicity are the leading factors, SPS parameters account for only ∼13% of the scatter. These results leave us with remaining unmodelled scatters of 0.055 dex and 0.122 dex for quiescent and star-forming galaxies, respectively. This can be interpreted as a conservative limit on the precision in M _⋆ that can be achieved via simple SPS modeling.

We introduce the neural network architecture spender as a core differentiable building block for analyzing, representing, and creating galaxy spectra. It combines a convolutional encoder, which pays attention to up to 256 spectral features and compresses them into a low-dimensional latent space, with a decoder that generates a restframe representation, whose spectral range and resolution exceeds that of the observing instrument. The decoder is followed by explicit redshift, resampling, and convolution transformations to match the observations. The architecture takes galaxy spectra at arbitrary redshifts and is robust to glitches like residuals of the skyline subtraction, so that spectra from a large survey can be ingested directly without additional preprocessing. We demonstrate the performance of spender by training on the entire spectroscopic galaxy sample of SDSS-II; show its ability to create highly accurate reconstructions with substantially reduced noise; perform deconvolution and oversampling for a superresolution model that resolves the [O ii ] doublet; introduce a novel method to interpret attention weights as proxies for important spectral features; and infer the main degrees of freedom represented in the latent space. We conclude with a discussion of future improvements and applications.

Individual vibrational band spectroscopy presents an opportunity to examine exoplanet atmospheres in detail, by distinguishing where the vibrational state populations of molecules differ from the current assumption of a Boltzmann distribution. Here, retrieving vibrational bands of OH in exoplanet atmospheres is explored using the hot Jupiter WASP-33b as an example. We simulate low-resolution spectroscopic data for observations with the JWST's NIRSpec instrument and use high-resolution observational data obtained from the Subaru InfraRed Doppler instrument (IRD). Vibrational band–specific OH cross-section sets are constructed and used in retrievals on the (simulated) low- and (real) high-resolution data. Low-resolution observations are simulated for two WASP-33b emission scenarios: under the assumption of local thermal equilibrium (LTE) and with a toy non-LTE model for vibrational excitation of selected bands. We show that mixing ratios for individual bands can be retrieved with sufficient precision to allow the vibrational population distributions of the forward models to be reconstructed. A fit for the Boltzmann distribution in the LTE case shows that the vibrational temperature is recoverable in this manner. For high-resolution, cross-correlation applications, we apply the individual vibrational band analysis to an IRD spectrum of WASP-33b, applying an “unpeeling” technique. Individual detection significances for the two strongest bands are shown to be in line with Boltzmann-distributed vibrational state populations, consistent with the effective temperature of the WASP-33b atmosphere reported previously. We show the viability of this approach for analyzing the individual vibrational state populations behind observed and simulated spectra, including reconstructing state population distributions.

We present a Spitzer/Herschel focused survey of the Aquila molecular clouds ( d ∼ 436 pc) as part of the eHOPS (extension of the Herschel orion protostar survey, or HOPS, Out to 500 ParSecs) census of nearby protostars. For every source detected in the Herschel/PACS bands, the eHOPS-Aquila catalog contains 1–850 μ m SEDs assembled from the Two Micron All Sky Survey, Spitzer, Herschel, the Wide-field Infrared Survey Explorer, and James Clerk Maxwell Telescope/SCUBA-2 data. Using a newly developed set of criteria, we classify objects by their SEDs as protostars, pre-main-sequence stars with disks, and galaxies. A total of 172 protostars are found in Aquila, tightly concentrated in the molecular filaments that thread the clouds. Of these, 71 (42%) are Class 0 protostars, 54 (31%) are Class I protostars, 43 (25%) are flat-spectrum protostars, and four (2%) are Class II sources. Ten of the Class 0 protostars are young PACS bright red sources similar to those discovered in Orion. We compare the SEDs to a grid of radiative transfer models to constrain the luminosities, envelope densities, and envelope masses of the protostars. A comparison of the eHOPS-Aquila to the HOPS protostars in Orion finds that the protostellar luminosity functions in the two star-forming regions are statistically indistinguishable, the bolometric temperatures/envelope masses of eHOPS-Aquila protostars are shifted to cooler temperatures/higher masses, and the eHOPS-Aquila protostars do not show the decline in luminosity with evolution found in Orion. We briefly discuss whether these differences are due to biases between the samples, diverging star formation histories, or the influence of environment on protostellar evolution.

We present a multiband photometric analysis of CRTS J163819.6+03485, the first low-mass-ratio contact binary system with a period of 0.2053321 day under the contact binary period limit. The unprecedented combination of mass ratio and period makes this system unique for eclipsing binary (EB) research. Using new multiband photometric observations, we explored the parameter space of this unique total EB system through a detailed scan in the mass ratio–inclination plane and using the pikaia genetic algorithm optimizer. The best set of relative physical parameters and corresponding uncertainties was adopted through Markov Chain Monte Carlo sampling of the parameter space. The resulting mass ratio of the system is q = 0.16 ± 0.01. The absolute parameters were derived by adopting an empirical mass–luminosity relation. Period changes are also investigated by using new observations and archival photometric light curves from massive astronomical surveys, which revealed in a preliminary solution the presence of a possible low-mass tertiary companion. The origin and evolutionary status of the system are investigated through the detached binary formation scenario.

Embedded class 0/I protostellar disks represent the initial condition for planet formation. This calls for a better understanding of their bulk properties and the dust grains within them. We model multiwavelength dust continuum observations of the disk surrounding the class I protostar TMC1A to provide insight on these properties. The observations can be well fit by a gravitationally self-regulated (i.e., marginally gravitationally unstable and internally heated) disk model with surface density Σ ∼ 1720( R /10 au) ^−1.96 g cm ^−2 and midplane temperature T _mid ∼ 185( R /10 au) ^−1.27 K. The observed disk contains an m = 1 spiral substructure; we use our model to predict the spiral’s pitch angle, and the prediction is consistent with the observations. This agreement serves as both a test of our model and strong evidence of the gravitational nature of the spiral. Our model estimates a maximum grain size ${a}_{\max }\sim 196{(R/10\,\mathrm{au})}^{-2.45}\mu {\rm{m}}$ , which is consistent with grain growth being capped by a fragmentation barrier with a threshold velocity of ∼1 m s ^−1 . We further demonstrate that the observational properties of TMC1A are typical among the observed population of class 0/I disks, which hints that traditional methods of disk data analysis based on Gaussian fitting and the assumption of optically thin dust emission could have systematically underestimated the disk size and mass and overestimated the grain size.

Quasars show a remarkable degree of atomic emission-line broadening, an observational feature which, in conjunction with a radial distance estimate for this emission from the nucleus, is often used to infer the mass of the central supermassive black hole. The radius estimate depends on the structure and kinematics of this so-called broad-line region, which is often modeled as a set of discrete emitting clouds. Here, we test an alternative kinematic disk-wind model of optically thick line emission originating from a geometrically thin accretion disk under Keplerian rotation around a supermassive black hole. We use this model to calculate broad emission-line profiles and interferometric phases to compare to GRAVITY data and previously published cloud modeling results. While we show that such a model can provide a statistically satisfactory fit to GRAVITY data for quasar 3C 273, we disfavor it as it requires 3C 273 be observed at high inclination, which observations of the radio jet orientation do not support.

The progenitors for many types of supernovae (SNe) are still unknown, and an approach to diagnose their physical origins is to investigate the light-curve brightness and shape of a large set of SNe. However, it is often difficult to compare and contrast the existing sample studies due to differences in their approaches and assumptions, for example, in how to eliminate host galaxy extinction, and this might lead to systematic errors when comparing the results. We therefore introduce the Hybrid Analytic Flux FittEr for Transients ( HAFFET ), a Python-based software package that can be applied to download photometric and spectroscopic data for transients from open online sources, derive bolometric light curves, and fit them to semianalytical models for estimation of their physical parameters. In a companion study, we have investigated a large collection of SNe Ib and Ic observed with the Zwicky Transient Facility (ZTF) with HAFFET , and here we detail the methodology and the software package to encourage more users. As large-scale surveys such as ZTF and LSST continue to discover increasing numbers of transients, tools such as HAFFET will be critical for enabling rapid comparison of models against data in statistically consistent, comparable, and reproducible ways. Additionally, HAFFET is created with a graphical user interface mode, which we hope will boost the efficiency and make the usage much easier ( https://github.com/saberyoung/HAFFET ).

NASA’s Kepler primary mission observed about 116 deg ^2 in the sky for 3.5 consecutive years to discover Earth-like exoplanets. This mission recorded pixel cutouts, known as target pixel files (TPFs), of over 200,000 targets that were selected to maximize the scientific yield. The Kepler pipeline performed aperture photometry for these primary targets to create light curves. However, hundreds of thousands of background sources were recorded in the TPFs and have never been systematically analyzed. This work uses the linearized field deblending (LFD) method, a point-spread function (PSF) photometry algorithm, to extract light curves. We use Gaia Data Release 3 as the input catalog to extract 606,900 light curves from long-cadence TPFs; 406,548 are new light curves of background sources, while the rest are Kepler’s targets. These light curves have comparable quality to those computed by the Kepler pipeline, with combined differential photometric precision values

The known near-Earth object (NEO) population consists of over 32,000 objects, with a yearly discovery rate of over 3000 NEOs per year. An essential component of the next generation of NEO surveys is an understanding of the population of known objects, including an accounting of the discovery rate per year as a function of size. Using a near-Earth asteroid (NEA) reference model developed for NASA’s NEO Surveyor (NEOS) mission and a model of the major current and historical ground-based surveys, an estimate of the current NEA survey completeness as a function of size and absolute magnitude has been determined (termed the Known Object Model; KOM). This allows for understanding of the intersection of the known catalog of NEAs and the objects expected to be observed by NEOS. The current NEA population is found to be ∼38% complete for objects larger than 140 m, consistent with estimates by Harris & Chodas. NEOS is expected to catalog more than two-thirds of the NEAs larger than 140 m, resulting in ∼76% of NEAs cataloged at the end of its 5 yr nominal survey, making significant progress toward the US Congressional mandate. The KOM estimates that ∼77% of the currently cataloged objects will be detected by NEOS, with those not detected contributing ∼9% to the final completeness at the end of its 5 yr mission. This model allows for placing the NEOS mission in the context of current surveys to more completely assess the progress toward the goal of cataloging the population of hazardous asteroids.

Symbolic regression (SR) is the study of algorithms that automate the search for analytic expressions that fit data. While recent advances in deep learning have generated renewed interest in such approaches, the development of SR methods has not been focused on physics, where we have important additional constraints due to the units associated with our data. Here we present Φ-SO, a physical symbolic optimization framework for recovering analytical symbolic expressions from physics data using deep reinforcement learning techniques by learning units constraints. Our system is built, from the ground up, to propose solutions where the physical units are consistent by construction. This is useful not only in eliminating physically impossible solutions but also because the grammatical rules of dimensional analysis enormously restrict the freedom of the equation generator, thus vastly improving performance. The algorithm can be used to fit noiseless data, which can be useful, for instance, when attempting to derive an analytical property of a physical model, and it can also be used to obtain analytical approximations of noisy data. We test our machinery on a standard benchmark of equations from the Feynman Lectures on Physics and other physics textbooks, achieving state-of-the-art performance in the presence of noise (exceeding 0.1%) and show that it is robust even in the presence of substantial (10%) noise. We showcase its abilities on a panel of examples from astrophysics.