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Stellar spectral template libraries play an important role in the automated analysis of stellar spectra. Synthetic template libraries cover a very large parameter space but suffer from poor matching with observed spectra. In this study, we propose a synthetic-to-observed spectral translation (SOST) method based on generative adversarial networks. The SOST method is able to calibrate synthetic spectra by converting them to the corresponding observed spectra. We applied this method to Kurucz synthetic spectra and observed spectra data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). After that, we constructed a stellar spectral library with uniform and broad parameter distributions using the SOST-corrected Kurucz synthetic spectra. Our stellar spectral template library contains 2431 spectra spanning a parameter space of 3500–8000 K for effective temperature (Teff), 0.0–5.0 dex for surface gravity (log g), and −2.0–0.5 dex for metallicity ([Fe/H]). The spectra in the library have a resolution of R ∼ 1800 and cover the wavelength range 3900–8700 Å. In order to verify the accuracy of this template library, we used the template library and the template-matching algorithm to derive the parameters of the PASTEL database. Compared to measurements using the original synthetic template library, the accuracies of the three parameters, Teff, log g, and [Fe/H], are improved, from 140 K, 0.31 dex, and 0.21 dex to 121 K, 0.26 dex, and 0.13 dex, respectively. In addition, we re-parameterised more than six million stellar spectra released by LAMOST DR8. [ABSTRACT FROM AUTHOR]

We evaluate the effectiveness of early dark energy (EDE) in addressing the Hubble tension using the luminous red galaxy (LRG), quasar (QSO), and emission line galaxy (ELG) samples from the completed eBOSS survey. We perform cosmological parameter measurements based on full shape analysis of the power spectrum employing the effective field theory of large-scale structure (EFTofLSS). EDE is known to strongly suffer from volume projection effects, complicating cosmological constraints' interpretation. To quantify the volume projection effects within an EDE full shape analysis, we explore the impact of different prior choices on the nuisance parameters of EFTofLSS through an extensive mock study. We compare classical Gaussian priors to the non-informative Jeffreys prior, known to mitigate volume projection effects in ΛCDM. Our full shape analysis combines eBOSS and BOSS data with Planck , external Baryon Acoustic Oscillation (BAO), PantheonPlus, and SH0ES supernova data. EDE reduces the tension from 5.2σ to 3σ compared to ΛCDM, yielding |$H_0=71.73_{-0.86}^{+0.82}$| km s−1 Mpc−1 with |$f_\mathrm{EDE} = 0.1179_{-0.022}^{+0.025}$| (Gaussian priors) and |$H_0=72.03_{-0.87}^{+0.82}$| km s−1 Mpc−1 with |$f_\mathrm{EDE} = 0.1399_{-0.022}^{+0.023}$| (Jeffreys prior). Although the Hubble tension is mitigated compared to ΛCDM, the inclusion of eBOSS data amplifies the tension within EDE from 2σ to 3σ, in contrast to the full shape analysis of BOSS data with Planck , external BAO, PantheonPlus, and SH0ES. This highlights the significance of incorporating additional large-scale structure data in discussions concerning models aiming to resolve the Hubble tension. [ABSTRACT FROM AUTHOR]

Field-level inference is emerging as a promising technique for optimally extracting information from cosmological data sets. Previous analyses have shown field-based inference produces tighter parameter constraints than power spectrum analyses. However, estimates of the detailed quantitative gain in constraining power differ. Here, we demonstrate the gain in constraining power depends on the parameter space being constrained. As a specific example, we find that lognormal field-based analysis of an LSST Y1-like mock data set only marginally improves constraints relative to a 2-point function analysis in Lambda cold dark matter (ΛCDM), yet it more than doubles the constraining power of the data in the context of w CDM models. This effect reconciles some, but not all, of the discrepant results found in the literature. Our results suggest the importance of using a full systematics model when quantifying the information gain for realistic field-level analyses of future data sets. [ABSTRACT FROM AUTHOR]

Understanding the temporal characteristics of data from low-frequency radio telescopes is of importance in devising suitable calibration strategies. Application of time-series analysis techniques to data from radio telescopes can reveal a wealth of information that can aid in calibration. In this paper, we investigate singular spectrum analysis (SSA) as an analysis tool for radio data. We show the intimate connection between SSA and Fourier techniques. We develop the relevant mathematics starting with an idealized periodic dataset and proceeding to include various non-ideal behaviours. We propose a novel technique to obtain long-term gain changes in data, leveraging the periodicity arising from sky drift through the antenna beams. We also simulate several plausible scenarios and apply the techniques to a 30-day time series data collected during 2021 June from SITARA – a short-spacing two element interferometer for global 21-cm detection. Applying the techniques to real data, we find that the first reconstructed component – the trend – has a strong anti-correlation with the local temperature suggesting temperature fluctuations as the most likely origin for the observed variations in the data. We also study the limitations of the calibration in the presence of diurnal gain variations and find that such variations are the likely impediment to calibrating SITARA data with SSA. [ABSTRACT FROM AUTHOR]

Abstract—The article provides a brief description of the software package DECH for processing and analysis of astronomical spectra. DECH supports all stages of processing and analysis of spectral data, including image preprocessing, spectra extraction (including those with a variable tilted slit), wavelength calibration by a two-dimensional polynomial, continuum normalization (manual or automatic), measurement of equivalent widths and radial velocities in various ways, cross-correlation analysis, etc. The DECH software package is actively used by astronomers from different countries and continues to be improved. In particular, utilities for processing and analysis of data from the high-resolution fiber-feed echelle spectrograph installed at 6-m telescope of Special Astrophysical Observatory of Russian Academy of Sciences were added in the latest version. Software provides high-precision measurements of radial velocities, including those for detection of extraterrestrial planets. [ABSTRACT FROM AUTHOR]

We report results on the joint-fit of the NuSTAR and HXMT data for the black hole X-ray binary candidate MAXI J1535-571. The observations were obtained in 2017 when the source evolved through the hard, hard-intermediate, and soft-intermediate states over the rising phase of the outburst. After subtracting continuum components, X-ray reflection signatures are clearly showed in those observations. By modelling the relativistic reflection in detail, we find that the inner radius R in is relatively stable with R in ≲ 1.55 R g during the three states, which implies that the inner radius likely extends to the innermost stable circular orbit even in the bright hard state. When adopting R in = R ISCO, the spin parameter is constrained to be |$0.985_{-0.004}^{+0.002}$| at 90 per cent confidence (statistical only). The best-fitting results reveal that the inclination of the inner accretion disc is ∼70–74 deg, which notably conflicts with the apparent orientation of the ballistic jet (≤45 deg). In addition, both the photon index and the electron temperature increase during the transition from hard to soft state. It seems that the corona evolves from dense low-temperature in the low/hard state to tenuous high-temperature after the state transition, which indicates that the state transition is accompanied by the evolution of the coronal properties. [ABSTRACT FROM AUTHOR]

Empirical estimates of the band power covariance matrix are commonly used in cosmic microwave background (CMB) power spectrum analyses. While this approach easily captures correlations in the data, noise in the resulting covariance estimate can systematically bias the parameter fitting. Conditioning the estimated covariance matrix, by applying prior information on the shape of the eigenvectors, can reduce these biases and ensure the recovery of robust parameter constraints. In this work, we use simulations to benchmark the performance of four different conditioning schemes, motivated by contemporary CMB analyses. The simulated surveys measure the TT ,  TE , and EE power spectra over the angular multipole range 300 ≤ ℓ ≤ 3500 in Δℓ = 50 wide bins, for temperature map-noise levels of 10, 6.4, and |$2\, \mu$| K arcmin. We divide the survey data into N real = 30, 50, or 100 uniform subsets. We show the results of different conditioning schemes on the errors in the covariance estimate, and how these uncertainties on the covariance matrix propagate to the best-fitting parameters and parameter uncertainties. The most significant effect we find is an additional scatter in the best-fitting point, beyond what is expected from the data likelihood. For a minimal conditioning strategy, N real = 30, and a temperature map-noise level of 10 |$\, \mu$| K arcmin, we find the uncertainty on the recovered best-fitting parameter to be ×1.3 larger than the apparent posterior width from the likelihood (×1.2 larger than the uncertainty when the true covariance is used). Stronger priors on the covariance matrix reduce the misestimation of parameter uncertainties to |$\lt 1{{\ \rm per\ cent}}$|⁠. As expected, empirical estimates perform better with higher N real, ameliorating the adverse effects on parameter constraints. [ABSTRACT FROM AUTHOR]