Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userCovariance-regularized reconstruction of data cubes in integral field spectroscopy and application to MaNGA data
59
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Integral field spectroscopy can map astronomical objects spatially and spectroscopically. Due to instrumental and atmospheric effects, it is common for integral field instruments to yield a sampling of the sky image that is both irregular and wavelength-dependent. Most subsequent analysis procedures require a regular, wavelength independent sampling (for example a fixed rectangular grid), and thus an initial step of fundamental importance is to resample the data onto a new grid. The best possible resampling would produce a well-sampled image, with a resolution equal to that imposed by the intrinsic spatial resolution of the instrument, telescope, and atmosphere, and with no statistical correlations between neighboring pixels. A standard method in the field to produce a regular set of samples from an irregular set of samples is Shepards method, but Shepards method typically yields images with a degraded resolution and large statistical correlations between pixels. Here we introduce a new method, which improves on Shepards method in both these respects. We apply this method to data from the Mapping Nearby Galaxies at Apache Point Observatory survey, part of Sloan Digital Sky Survey IV, demonstrating a full-width half maximum close to that of the intrinsic resolution (and ~ 16% better than Shepards method) and low statistical correlations between pixels. These results nearly achieve the ideal resampling. This method can have broader applications to other integral field data sets and to other astronomical data sets (such as dithered images) with irregular sampling.
rate research
Read More
Astronomical observations of extended sources, such as cubes of integral field spectroscopy (IFS), encode auto-correlated spatial structures that cannot be optimally exploited by standard methodologies. This work introduces a novel technique to model IFS datasets, which treats the observed galaxy properties as realizations of an unobserved Gaussian Markov random field. The method is computationally efficient, resilient to the presence of low-signal-to-noise regions, and uses an alternative to Markov Chain Monte Carlo for fast Bayesian inference, the Integrated Nested Laplace Approximation (INLA). As a case study, we analyse 721 IFS data cubes of nearby galaxies from the CALIFA and PISCO surveys, for which we retrieve the maps of the following physical properties: age, metallicity, mass and extinction. The proposed Bayesian approach, built on a generative representation of the galaxy properties, enables the creation of synthetic images, recovery of areas with bad pixels, and an increased power to detect structures in datasets subject to substantial noise and/or sparsity of sampling. A snippet code to reproduce the analysis of this paper is available in the COIN toolbox, together with the field reconstructions of the CALIFA and PISCO samples.
We present SimSpin, a new, public, software framework for generating integral field spectroscopy (IFS) data cubes from N-body/hydrodynamical simulations of galaxies, which can be compared directly with observational datasets. SimSpin provides a consistent method for studying a galaxys stellar component. It can be used to explore how observationally inferred measurements of kinematics, such as the spin parameter $lambda_R$, are impacted by the effects of, for example, inclination, seeing conditions, distance, etc. SimSpin is written in R and has been designed to be highly modular, flexible, and extensible. It is already being used by the astrophysics community to generate IFS-like cubes and FITS files for direct comparison of simulations to observations. In this paper, we explain the conceptual framework of SimSpin; how it is implemented in R; and we demonstrate SimSpins current capabilities, providing as an example a brief investigation of how numerical resolution affects how reliably we can recover the intrinsic stellar kinematics of a simulated galaxy.
We present an improved version of FIT3D, a fitting tool for the analysis of the spectroscopic properties of the stellar populations and the ionized gas derived from moderate resolution spectra of galaxies. FIT3D is a tool developed to analyze Integral Field Spectroscopy data and it is the basis of Pipe3D, a pipeline already used in the analysis of datasets like CALIFA, MaNGA, and SAMI. We describe the philosophy behind the fitting procedure, and in detail each of the different steps in the analysis. We present an extensive set of simulations in order to estimate the precision and accuracy of the derived parameters for the stellar populations. In summary, we find that using different stellar population templates we reproduce the mean properties of the stellar population (age, metallicity, and dust attenuation) within ~0.1 dex. A similar approach is adopted for the ionized gas, where a set of simulated emission- line systems was created. Finally, we compare the results of the analysis using FIT3D with those provided by other widely used packages for the analysis of the stellar population (Starlight, Steckmap, and analysis based on stellar indices) using real high S/N data. In general we find that the parameters for the stellar populations derived by FIT3D are fully compatible with those derived using these other tools.
We present the first observations of the Frontier Fields Cluster Abell S1063 taken with the newly commissioned Multi Unit Spectroscopic Explorer (MUSE) integral field spectrograph. Because of the relatively large field of view (1 arcmin^2), MUSE is ideal to simultaneously target multiple galaxies in blank and cluster fields over the full optical spectrum. We analysed the four hours of data obtained in the Science Verification phase on this cluster and measured redshifts for 53 galaxies. We confirm the redshift of five cluster galaxies, and determine the redshift of 29 other cluster members. Behind the cluster, we find 17 galaxies at higher redshift, including three previously unknown Lyman-alpha emitters at z>3, and five multiply-lensed galaxies. We report the detection of a new z=4.113 multiply lensed galaxy, with images that are consistent with lensing model predictions derived for the Frontier Fields. We detect C III], C IV, and He II emission in a multiply lensed galaxy at z=3.116, suggesting the likely presence of an active galactic nucleus. We also created narrow-band images from the MUSE datacube to automatically search for additional line emitters corresponding to high-redshift candidates, but we could not identify any significant detections other than those found by visual inspection. With the new redshifts, it will become possible to obtain an accurate mass reconstruction in the core of Abell S1063 through refined strong lensing modelling. Overall, our results illustrate the breadth of scientific topics that can be addressed with a single MUSE pointing. We conclude that MUSE is a very efficient instrument to observe galaxy clusters, enabling their mass modelling, and to perform a blind search for high-redshift galaxies.
We present a statistical framework to compare spectral-line data cubes of molecular clouds and use the framework to perform an analysis of various statistical tools developed from methods proposed in the literature. We test whether our methods are sensitive to changes in the underlying physical properties of the clouds or whether their behaviour is governed by random fluctuations. We perform a set of 32 self-gravitating magnetohydrodynamic simulations that test all combinations of five physical parameters -- Mach number, plasma parameter, virial parameter, driving scales, and solenoidal driving fraction -- each of which can be set to a low or high value. We create mock observational data sets of ${rm ^{13}CO}$(1-0) emission from each simulation. We compare these mock data to a those generated from a set of baseline simulations using pseudo-distance metrics based on 18 different statistical techniques that have previously been used to study molecular clouds. We analyze these results using methods from the statistical field of experimental design and find that several of the statistics can reliably track changes in the underlying physics. Our analysis shows that the interactions between parameters are often among the most significant effects. A small fraction of statistics are also sensitive to changes in magnetic field properties. We use this framework to compare the set of simulations to observations of three nearby star-forming regions: NGC 1333, Oph A, and IC 348. We find that no one simulation agrees significantly better with the observations, although there is evidence that the high Mach number simulations are more consistent with the observations.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
