Sazuna/UAT_keywords huggingface dataset:
Improving Cosmological Constraints by Inferring the Formation Channel of Extreme-mass-ratio Inspirals. Extreme-mass-ratio inspirals (EMRIs) could be detected by space-borne gravitational-wave (GW) detectors, such as the Laser Interferometer Space Antenna (LISA), TianQin, and Taiji. Localizing EMRIs by GW detectors can help us select candidate host galaxies, which can be used to infer the cosmic expansion history. In this paper, we demonstrate that the localization information can also be used to infer the formation channel of EMRIs, and can hence allow us to extract more precisely the redshift probability distributions. By conducting mock observations of the EMRIs that can be detected by TianQin and LISA, as well as the galaxies that can be provided by the future Chinese Space Station Telescope, we find that TianQin can constrain the Hubble–Lemaître constant H 0 to a precision of ∼3%–8% and the dark energy equation-of-state parameter w 0 to ∼10%–40%. The TianQin+LISA network, by increasing the localization accuracy, can improve the precisions of H 0 and w 0 to ∼0.4%–7% and ∼4%–20%, respectively. Then, considering an illustrative case in which all EMRIs originate in active galactic nuclei (AGNs), and combining the mock EMRI observation with a mock AGN catalog, we show that TianQin can recognize the EMRI–AGN correlation with ∼1300 detections. The TianQin+LISA network can reduce this required number to ∼30. Additionally, we propose a statistical method to directly estimate the fraction of EMRIs produced in AGNs, f agn, and show that observationally deriving this value could significantly improve the constraints on the cosmological parameters. These results demonstrate the potentials of using EMRIs as well as galaxy and AGN surveys to improve the constraints on cosmological parameters and the formation channel of EMRIs.

Keywords: Bayesian statistics, Sky surveys, Black holes, Hubble constant, Poisson distribution, Gravitational waves
Observations of Five Major Uranian Satellites during 2013–2020 Based on Gaia DR3. A total of 10,339 positions of the five major Uranian satellites (Ariel, Umbriel, Titania, Oberon and Miranda) were collected at the Yunnan Observatories from 2013 to 2020 with the 1.0 m astrometric reflector. A median filtering method was applied to reduce the influence of the bright Uranus halo. All CCD observational data was analyzed by the newly developed CCD image-processing software with Gaia DR3 as the reference catalog. The impact of different exposure times on satellite positioning accuracy was evaluated. The observational positions of satellites were compared with theoretical positions from Institut de Mécanique Céleste et de Calcul des Éphémérides (DE441 and Lainey et al. 2015) and the mean offsets are from ‑0.″115 to 0.″103 in both R.A. and decl. for the five satellites. The standard deviation of the offsets are from 0.″008 to 0.″086 for the four greatest Uranian satellites and from 0.″037 to 0.″132 for Miranda in each direction. These precise observations of the five major Uranian satellites over an 8 yr period will be very useful for improving the orbital parameters of both Uranus and its five major satellites.

Keywords: Uranian satellites
Cosmology Large Angular Scale Surveyor (CLASS): 90 GHz Telescope Pointing, Beam Profile, Window Function, and Polarization Performance. The Cosmology Large Angular Scale Surveyor (CLASS) is a telescope array that observes the cosmic microwave background (CMB) over ∼75% of the sky from the Atacama Desert, Chile, at frequency bands centered near 40, 90, 150, and 220 GHz. CLASS measures the large angular scale CMB polarization to constrain the tensor-to-scalar ratio and the optical depth to last scattering. This paper presents the optical characterization of the 90 GHz telescope. Observations of the Moon establish the pointing while dedicated observations of Jupiter are used for beam calibration. The standard deviations of the pointing error in azimuth, elevation, and boresight angle are 1.'3, 2.'1, and 2.'0, respectively, over the first 3 yr of observations. This corresponds to a pointing uncertainty ∼7% of the beam's full width at half-maximum (FWHM). The effective azimuthally symmetrized instrument 1D beam estimated at 90 GHz has an FWHM of 0.°620 ± 0.°003 and a solid angle of 138.7 ± 0.6(stats.) ± 1.1(sys.) μsr integrated to a radius of 4°. The corresponding beam window function drops to at ℓ = 30, 100, 300, respectively. Far-sidelobes are studied using detector-centered intensity maps of the Moon and measured to be at a level of 10‑3 or below relative to the peak. The polarization angle of Tau A estimated from preliminary survey maps is 149°.6 ± 0°.2(stats.) in equatorial coordinates. The instrumental temperature-to-polarization (T → P) leakage fraction, inferred from per-detector demodulated Jupiter scan data, has a monopole component at the level of 1.7 × 10‑3, a dipole component with an amplitude of 4.3 × 10‑3, with no evidence of quadrupolar leakage.

Keywords: Observational cosmology, Cosmic microwave background radiation, Astronomical instrumentation, Polarimeters, Telescopes
LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring Its Applications. We investigate the prospect of reconstructing the "cosmic distance ladder" of the Universe using a novel deep learning framework called LADDER—Learning Algorithm for Deep Distance Estimation and Reconstruction. LADDER is trained on the apparent magnitude data from the Pantheon Type Ia supernova compilation, incorporating the full covariance information among data points, to produce predictions along with corresponding errors. After employing several validation tests with a number of deep learning models, we pick LADDER as the best-performing one. We then demonstrate applications of our method in the cosmological context, including serving as a model-independent tool for consistency checks for other data sets like baryon acoustic oscillations, calibration of high-redshift data sets such as gamma-ray bursts, and use as a model-independent mock-catalog generator for future probes. Our analysis advocates for careful consideration of machine learning techniques applied to cosmological contexts.

Keywords: Cosmology, Neural networks, Cosmological parameters, Calibration, Type Ia supernovae, Stellar distance, Baryon acoustic oscillations
Experimental Scattering Matrices of Martian Dust Aerosols with Narrow Particle-size Distributions. We present experimental scattering matrices of the JSC Mars-1, MMS-2, and MGS-1 simulants at 488 and 640 nm. The analogs were processed so that narrow size distributions representative of Martian dust aerosols during different dust cycles were obtained. We find that the forward peak of the phase function depends on particle size as it becomes narrower with increasing size, whereas the side- and backscattering directions depend on both composition and size so that increasing size and decreasing absorption produce a flatter curve. The position and maximum of the degree of linear polarization varies based on particle size and composition, and the negative polarization branch is more prominent for wavelength-scale particles diminishing with increasing size. The linear depolarization is strongly affected by size and composition. Finally, we compare sky-brightness curves measured by the Navcam and Hazcam engineering cameras on board the Mars Science Laboratory rover to the measured phase functions. The observations show a narrower peak at the forward direction and a flatter curve toward the side- and backscattering directions with an increasing dust load in the atmosphere, similar to what can be seen for the measured phase functions of the analogs with increasing particle size. In the case of the analogs, the flattening of the curve can be caused by an increase in multiple scattering within a particle by wavelength-scale surface roughness and/or internal inclusions. For the observed sky brightnesses, particle aggregation and multiple scattering among particles in denser dust conditions play a major role.

Keywords: Mars, Polarimetry, Experimental data
SMA 200–400 GHz Survey for Dust Properties in the Icy Class II Disks in the Taurus Molecular Cloud. We present a new Submillimeter Array survey of 47 Class II sources in the Taurus–Auriga region. Our observations made 12 independent samples of flux densities over the 200–400 GHz frequency range. We tightly constrained the spectral indices of most sources to a narrow range of 2.0 ± 0.2; only a handful of spatially resolved (e.g., diameter &gt;250 au) disks present larger spectral indices. The simplest interpretation for this result is that the (sub)millimeter luminosities of all of the observed target sources are dominated by very optically thick (e.g., τ ≳ 5) dust thermal emission. Some previous works that were based on the optically thin assumption thus might have underestimated optical depths by at least 1 order of magnitude. Assuming DSHARP dust opacities, this corresponds to underestimates of dust masses by a similar factor. For our specific selected sample, the lower limits of dust masses implied by the optically thick interpretation are 1–3 times higher than those previous estimates that were made based on the optically thin assumption. Moreover, some population synthesis models show that, to explain the observed, narrowly distributed spectral indices, the disks in our selected sample need to have very similar dust temperatures (T dust). Given a specific assumption of median T dust, the maximum grain sizes ( ) can also be constrained, which is a few times smaller than 0.1 mm for T dust ∼ 100 K and a few millimeters for T dust ∼ 24 K. The results may indicate that dust grain growth outside the water snow line is limited by the bouncing/fragmentation barriers. This is consistent with the recent laboratory experiments, which indicated that the coagulation of water-ice-coated dust is not efficient, and the water-ice-free dust is stickier and thus can coagulate more efficiently. In the Class II disks, the dust mass budget outside of the water snow line may be largely retained instead of being mostly consumed by planet formation. While Class II disks still possess sufficient dust masses to feed planet formation at a later time, it is unknown whether or not dust coagulation and planet formation can be efficient or natural outside of the water snow line.

Keywords: Circumstellar dust, Protoplanetary disks, Planet formation, Pre-main sequence
Discovery of Giant Radio Sources from TGSS Alternative Data Release 1: Radio, Optical, and Infrared Properties. Giant radio sources (GRSs) are the single largest astrophysical objects known in the Universe that have grown to megaparsec scales (≥0.7 Mpc). GRSs are much rarer compared with normal-sized radio galaxies. Still, the reason for the formation of their gigantic sizes is under debate. We systematically search for GRSs from the TIFR Giant Metrewave Radio Telescope Sky Survey Alternative Data Release 1 at 150 MHz. We have newly identified 34 GRSs from this study. We have also studied the multiwavelength properties (radio, optical, and infrared) of these GRSs. We have used the likelihood ratio method to identify highly reliable multiwavelength counterparts of GRSs from Pan-STARRS (optical) and Wide-field Infrared Survey Explorer (mid-IR) data. We have classified GRSs based on their accretion mode of the central black holes using optical and mid-IR data. For all sources, we also discuss the principal characteristic parameters (redshift distribution, angular and projected linear size, total integrated radio flux density, spectral index, and radio power). We show the radio evolution track and the location of the GRSs in the P–D diagram. Using a radio–optical luminosity diagram, we identify GRSs in the Fanaroff–Riley classification. Only two GRGs in our sample reside close to the centers of galaxy clusters.

Keywords: Quasars, Active galactic nuclei, Giant radio galaxies, Radio active galactic nuclei, Radio sources
A Method of Rapidly Deriving Late-type Contact Binary Parameters and Its Application in the Catalina Sky Survey. With the continuous development of large optical surveys, a large number of light curves of late-type contact binary systems (CBs) have been released. Deriving parameters for CBs using the the Wilson–Devinney program and the PHOEBE program poses a challenge. Therefore, this study developed a method for rapidly deriving light curves based on the Neural Networks model combined with the Hamiltonian Monte Carlo (HMC) algorithm (NNHMC). The neural network was employed to establish the mapping relationship between the parameters and the pregenerated light curves by the PHOEBE program, and the HMC algorithm was used to obtain the posterior distribution of the parameters. The NNHMC method was applied to a large contact binary sample from the Catalina Sky Survey, and a total of 19,104 late-type contact binary parameters were derived. Among them, 5172 have an inclination greater than 70° and a temperature difference less than 400 K. The obtained results were compared with the previous studies for 30 CBs, and there was an essentially consistent goodness-of-fit (R 2) distribution between them. The NNHMC method possesses the capability to simultaneously derive parameters for a vast number of targets. Furthermore, it can provide an extremely efficient tool for the rapid derivation of parameters in future sky surveys involving large samples of CBs.

Keywords: Eclipsing binary stars, Astronomy data analysis, Contact binary stars
On the Interpretation of Mid-infrared Absorption Lines of Gas-phase H<SUB>2</SUB>O as Observed by JWST/MIRI. Rovibrational absorption lines of H2O in the 5–8 μm wavelength range selectively probe gas against the mid-infrared continuum-emitting background of the inner regions of young stellar objects and active galactic nuclei and deliver important information about these warm, dust-obscured environments. JWST/Mid-Infrared Instrument (MIRI) detects these lines in many lines of sight at a moderate spectral resolving power of R ∼ 3500 (full width at half-maximum of 85 km s‑1). Based on our analysis of high-resolution SOFIA/EXES observations, we find that the interpretation of JWST/MIRI absorption spectra can be severely hampered by the blending of individual transitions and the lost information on the intrinsic line width or the partial coverage of the background continuum source. In this paper, we point out problems such as degeneracy that arise in deriving physical properties from an insufficiently resolved spectrum. This can lead to differences in the column density by 2 orders of magnitude. We emphasize the importance of weighting optically thin and weak lines in spectral analyses and provide recipes for breaking down the coupled parameters. We also provide an online tool to generate the H2O absorption line spectra that can be compared to observations.

Keywords: Interstellar molecules, Infrared spectroscopy
Enhancing Morphological Measurements of the Cosmic Web with Delaunay Tessellation Field Estimation. The density fields constructed by traditional mass assignment methods are susceptible to irritating discreteness, which hinders morphological measurements of cosmic large-scale structure (LSS) through Minkowski functionals (MFs). To alleviate this issue, fixed-kernel smoothing methods are commonly used in the literature, at the expense of losing substantial structural information. In this work, we propose to measure MFs with the Delaunay tessellation field estimation (DTFE) technique, with the goal of maximizing the extraction of morphological information from sparse tracers. We perform our analyses starting from matter fields and progressively extending to halo fields. At the matter-field level, we elucidate how discreteness affects morphological measurements of LSS. Then, by comparing with the traditional Gaussian smoothing scheme, we preliminarily showcase the advantages of DTFE for enhancing measurements of MFs from sparse tracers. At the halo-field level, we first numerically investigate various systematic effects on MFs of DTFE fields, which are induced by finite voxel sizes, halo number densities, halo weightings, and redshift space distortions (RSDs), respectively. Then, we explore the statistical power of MFs measured with DTFE for extracting the cosmological information encoded in RSDs. We find that MFs measured with DTFE exhibit improvements by ∼2 orders of magnitude in discriminative power for RSD effects and by a factor of ∼3–5 in constraining power on the structure growth rate over the MFs measured with Gaussian smoothing. These findings demonstrate the remarkable enhancements in statistical power of MFs achieved by DTFE, showing enormous application potentials for our method in extracting various key cosmological information from galaxy surveys.

Keywords: Cosmology, Cosmic web, Large-scale structure of the universe
A Robust Young Stellar Object Identification Method Based on Deep Learning. Young stellar objects (YSOs) represent the earliest stage in the process of star formation, offering insights that contribute to the development of models elucidating star formation and evolution. Recent advancements in deep-learning techniques have enabled significant strides in identifying special objects within vast data sets. In this paper, we present a YSO identification method based on deep-learning principles and spectra from the LAMOST. We designed a structure based on a long short-term memory network and a convolutional neural network and trained different models in two steps to identify YSO candidates. Initially, we trained a model to detect stellar spectra featuring the Hα emission line, achieving an accuracy of 98.67%. Leveraging this model, we classified 10,495,781 stellar spectra from LAMOST, yielding 76,867 candidates displaying a Hα emission line. Subsequently, we developed a YSO identification model, which achieved a recall rate of 95.81% for YSOs. Utilizing this model, we further identified 35,021 YSO candidates from the Hα emission-line candidates. Following cross validation, 3204 samples were identified as previously reported YSO candidates. We eliminated samples with low signal-to-noise ratios and M dwarfs by using the equivalent widths of the N II and He I emission lines and visual inspection, resulting in a catalog of 20,530 YSO candidates. To facilitate future research endeavors, we provide the obtained catalogs of Hα emission-line star candidates and YSO candidates along with the code used for training the model.

Keywords: Neural networks, Astronomy data analysis, Stellar classification, Young stellar objects, Stellar spectral types
Light-curve Recovery with Rubin-LSST. II. Unveiling the Darkness of the Galactic Bulge (VESTALE) with RR Lyrae. This work is part of VESTALE, a project initiated within the Legacy Survey of Space and Time (LSST) Cadence Strategy Optimization Process. Its goal is to explore the potential of Rubin-LSST observations aimed at the Galactic bulge (henceforth just "Bulge") for studying RR Lyrae (RRL) stars. Observation and analysis of RRL stars in the Bulge are crucial for tracing the old population of the central part of our Galaxy and reconstructing its formation. Based on observations conducted with CTIO/DECam by Saha et al. toward Baade's window, our simulations demonstrate that early Rubin-LSST observations will enable the recovery of RRL light curves (LCs) at Galactic center distances with sufficient precision. This will allow us to utilize theoretical relations from Marconi et al. to determine their distances and/or metallicity, following the REDIME algorithm introduced in Bono et al. We show how reddening and crowding affect our simulations and highlight the importance of considering these effects when deriving pulsation parameters (luminosity amplitudes, mean magnitudes) based on the LCs, especially if the goal is to explore the opposite side of the Bulge through the observation of its RRL. The simulations discussed in this investigation were conducted to support the Survey Cadence Optimization Committee's decision to observe this important sky region since it has only recently been decided to include part of the Bulge as a target within the LSST main survey.

Keywords: RR Lyrae variable stars, Sky surveys, Galactic bulge, Time series analysis
Modeling the Magnetic Vectors of Interplanetary Coronal Mass Ejections at Different Heliocentric Distances with INFROS. The INterplanetary Flux ROpe Simulator (INFROS) is an observationally constrained analytical model dedicated to forecasting the strength of the southward component (Bz) of the magnetic field embedded in interplanetary coronal mass ejections (ICMEs). In this work, we validate the model for six ICMEs sequentially observed by two radially aligned spacecraft positioned at different heliocentric distances. The six selected ICMEs in this study comprise cases associated with isolated coronal mass ejection (CME) evolution as well as those interacting with high-speed streams (HSSs) and high-density streams (HDSs). For the isolated CMEs, our results show that the model outputs at both spacecraft are in good agreement with in situ observations. However, for most of the interacting events, the model correctly captures the CME evolution only at the inner spacecraft. Due to the interaction with HSSs and HDSs, which in most cases occurred at heliocentric distances beyond the inner spacecraft, the ICME evolution no longer remains self-similar. Consequently, the model underestimates the field strength at the outer spacecraft. Our findings indicate that constraining the INFROS model with inner-spacecraft observations significantly enhances the prediction accuracy at the outer spacecraft for the three events undergoing self-similar expansion, achieving a 90% correlation between observed and predicted Bz profiles. This work also presents a quantitative estimation of the ICME magnetic field enhancement due to interaction which may lead to severe space weather. We conclude that the assumption of self-similar expansion provides a lower limit to the magnetic field strength estimated at any heliocentric distance, based on the remote-sensing observations.

Keywords: Space weather, Solar wind, Interplanetary magnetic fields, Solar coronal mass ejections, Corotating streams
P ADÉ: A Code for Protoplanetary Disk Turbulence Based on Padé Differencing. The PADÉ code has been developed to treat hydrodynamic turbulence in protoplanetary disks. It solves the compressible equations of motion in cylindrical coordinates. Derivatives are computed using nondiffusive and conservative fourth-order Padé differencing, which has higher resolving power compared to both dissipative shock-capturing schemes used in most astrophysics codes, as well as nondiffusive central finite-difference schemes of the same order. The fourth-order Runge–Kutta method is used for time stepping. A previously reported error-corrected Fargo approach is used to reduce the time step constraint imposed by rapid Keplerian advection. Artificial bulk viscosity is used when shock capturing is required. Tests for correctness and scaling with respect to the number of processors are presented. Finally, efforts to improve efficiency and accuracy are suggested.

Keywords: Computational methods, Protoplanetary disks