Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userData-Driven Stellar Models
131
0
0.0
(
0
)
Authors
Gregory M. Green
Ask ChatGPT about the research
No Arabic abstract
We develop a data-driven model to map stellar parameters (effective temperature, surface gravity and metallicity) accurately and precisely to broad-band stellar photometry. This model must, and does, simultaneously constrain the passband-specific dust reddening vector in the Milky Way. The model uses a neural network to learn the (de-reddened) absolute magnitude in one band and colors across many bands, given stellar parameters from spectroscopic surveys and parallax constraints from Gaia. To demonstrate the effectiveness of this approach, we train our model on a dataset with spectroscopic parameters from LAMOST, APOGEE and GALAH, Gaia parallaxes, and optical and near-infrared photometry from Gaia, Pan-STARRS~1, 2MASS and WISE. Testing the model on these datasets leads to an excellent fit and a precise - and by construction accurate - prediction of the color-magnitude diagrams in many bands. This flexible approach rigorously links spectroscopic and photometric surveys, and also results in an improved, stellar-temperature-dependent reddening vector. As such, it provides a simple and accurate method for predicting photometry in stellar evolutionary models. Our model will form a basis to infer stellar properties, distances and dust extinction from photometric data, which should be of great use in 3D mapping of the Milky Way. Our trained model may be obtained at https://doi.org/10.5281/zenodo.3902382.
rate research
Read More
The validity of the unified active galactic nuclei (AGN) model has been challenged in the last decade, especially when different types of AGNs are considered to only differ in the viewing angle to the torus. We aim to assess the importance of the viewing angle in classifying different types of Seyfert galaxies in spectral energy distribution (SED) modelling. We retrieve photometric data from publicly available astronomical databases: CDS and NED, to model SEDs with X-CIGALE in a sample of 13 173 Seyfert galaxies located at redshift range from $z=0$ to $z=3.5$, with a median redshift of $zapprox0.2$. We assess whether the estimated viewing angle from the SED models reflects different Seyfert classifications. Two AGN models with either a smooth or clumpy torus structure are adopted in this paper. We find that the viewing angle in Type-1 AGNs is better constrained than in Type-2 AGNs. Limiting the viewing angles representing these two types of AGNs do not affect the physical parameter estimates such as star-formation rate (SFR) or AGN fractional contribution ($f_{rm{AGN}}$). In addition, the viewing angle is not the most discriminating physical parameter to differentiate Seyfert types. We suggest that the observed and intrinsic AGN disc luminosity can: i) be used in $z<0.5$ studies to distinguish between Type-1 and Type-2 AGNs, and ii) explain the probable evolutionary path between these AGN types. Finally, we propose the use of X-CIGALE for AGN galaxy classification tasks. All data from the 13 173 SED fits are available at https://doi.org/10.5281/zenodo.5221764
We present a performance test of the Point Spread Function deconvolution algorithm applied to astronomical Integral Field Unit (IFU) Spectroscopy data for restoration of galaxy kinematics. We deconvolve the IFU data by applying the Lucy-Richardson algorithm to the 2D image slice at each wavelength. We demonstrate that the algorithm can effectively recover the true stellar kinematics of the galaxy, by using mock IFU data with diverse combination of surface brightness profile, S/N, line-of-sight geometry and Line-Of-Sight Velocity Distribution (LOSVD). In addition, we show that the proxy of the spin parameter $lambda_{R_{e}}$ can be accurately measured from the deconvolved IFU data. We apply the deconvolution algorithm to the actual SDSS-IV MaNGA IFU survey data. The 2D LOSVD, geometry and $lambda_{R_{e}}$ measured from the deconvolved MaNGA IFU data exhibit noticeable difference compared to the ones measured from the original IFU data. The method can be applied to any other regular-grid IFU data to extract the PSF-deconvolved spatial information.
The primary challenge in the study of explosive astrophysical transients is their detection and characterisation using multiple messengers. For this purpose, we have developed a new data-driven discovery framework, based on deep learning. We demonstrate its use for searches involving neutrinos, optical supernovae, and gamma rays. We show that we can match or substantially improve upon the performance of state-of-the-art techniques, while significantly minimising the dependence on modelling and on instrument characterisation. Particularly, our approach is intended for near- and real-time analyses, which are essential for effective follow-up of detections. Our algorithm is designed to combine a range of instruments and types of input data, representing different messengers, physical regimes, and temporal scales. The methodology is optimised for agnostic searches of unexpected phenomena, and has the potential to substantially enhance their discovery prospects.
Upcoming million-star spectroscopic surveys have the potential to revolutionize our view of the formation and chemical evolution of the Milky Way. Realizing this potential requires automated approaches to optimize estimates of stellar properties, such as chemical element abundances, from the spectra. The volume and quality of the observations strongly motivate that these approaches should be data-driven. With this in mind, we introduce SSSpaNG: a data-driven non-Gaussian Process model of stellar spectra. We demonstrate the capabilities of SSSpaNG using a sample of APOGEE red clump stars, whose model parameters we infer via Gibbs sampling. Pooling information between stars to infer their covariance, we permit clear identification of the correlations between spectral pixels. Harnessing these correlations, we infer the true spectrum of each star, inpainting missing regions and denoising by a factor of at least 2 for stars with signal-to-noise of ~20. As we marginalize over the covariance matrix of the spectra, the effective prior on these true spectra is non-Gaussian and sparsifying, favouring typically small but occasionally large excursions from the mean. The high-fidelity inferred spectra produced will enable improved elemental abundance measurements for individual stars. Our model also allows us to quantify the information gained by observing portions of a stars spectrum, and thereby define the most mutually informative spectral regions. Using 25 windows centred on elemental absorption lines, we demonstrate that the iron-peak and alpha-process elements are particularly mutually informative for these spectra, and that the majority of information about a target window is contained in the 10-or-so most informative windows. Such mutual-information estimates have the potential to inform models of nucleosynthetic yields and the design of future observations.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
