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Register a new userRecovering $lambda_R$ and $V/sigma$ from seeing-dominated IFS data
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Observers experience a series of limitations when measuring galaxy kinematics, such as variable seeing conditions and aperture size. These effects can be reduced using empirical corrections, but these equations are usually applicable within a restrictive set of boundary conditions (e.g. Sersic indices within a given range) which can lead to biases when trying to compare measurements made across a full kinematic survey. In this work, we present new corrections for two widely used kinematic parameters, $lambda_R$ and $V/sigma$, that are applicable across a broad range of galaxy shapes, measurement radii and ellipticities. We take a series of mock observations of N-body galaxy models and use these to quantify the relationship between the observed kinematic parameters, structural properties and different seeing conditions. Derived corrections are then tested using the full catalogue of galaxies, including hydro-dynamic models from the EAGLE simulation. Our correction is most effective for regularly-rotating systems, yet the kinematic parameters of all galaxies -- fast, slow and irregularly rotating systems -- are recovered successfully. We find that $lambda_R$ is more easily corrected than $V/sigma$, with relative deviations of 0.02 and 0.06 dex respectively. The relationship between $lambda_R$ and $V/sigma$, as described by the parameter $kappa$, also has a minor dependence on seeing conditions. These corrections will be particularly useful for stellar kinematic measurements in current and future integral field spectroscopic (IFS) surveys of galaxies.
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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.
The MaNGA Survey (Mapping Nearby Galaxies at Apache Point Observatory) is one of three core programs in the Sloan Digital Sky Survey IV. It is obtaining integral field spectroscopy (IFS) for 10K nearby galaxies at a spectral resolution of R~2000 from 3,622-10,354A. The design of the survey is driven by a set of science requirements on the precision of estimates of the following properties: star formation rate surface density, gas metallicity, stellar population age, metallicity, and abundance ratio, and their gradients; stellar and gas kinematics; and enclosed gravitational mass as a function of radius. We describe how these science requirements set the depth of the observations and dictate sample selection. The majority of targeted galaxies are selected to ensure uniform spatial coverage in units of effective radius (Re) while maximizing spatial resolution. About 2/3 of the sample is covered out to 1.5Re (Primary sample), and 1/3 of the sample is covered to 2.5Re (Secondary sample). We describe the survey execution with details that would be useful in the design of similar future surveys. We also present statistics on the achieved data quality, specifically, the point spread function, sampling uniformity, spectral resolution, sky subtraction, and flux calibration. For our Primary sample, the median r-band signal-to-noise ratio is ~73 per 1.4A pixel for spectra stacked between 1-1.5 Re. Measurements of various galaxy properties from the first year data show that we are meeting or exceeding the defined requirements for the majority of our science goals.
In this article, we consider the problem of recovering the underlying trajectory when the longitudinal data are sparsely and irregularly observed and noise-contaminated. Such data are popularly analyzed with functional principal component analysis via the Principal Analysis by Conditional Estimation (PACE) method. The PACE method may sometimes be numerically unstable because it involves the inverse of the covariance matrix. We propose a sparse orthonormal approximation (SOAP) method as an alternative. It estimates the optimal empirical basis functions in the best approximation framework rather than eigen-decomposing the covariance function. The SOAP method avoids estimating the mean and covariance function, which is challenging when the assembled time points with observations for all subjects are not sufficiently dense. The SOAP method avoids the inverse of the covariance matrix, hence the computation is more stable. It does not require the functional principal component scores to follow the Gaussian distribution. We show that the SOAP estimate for the optimal empirical basis function is asymptotically consistent. The finite sample performance of the SOAP method is investigated in simulation studies in comparison with the PACE method. Our method is demonstrated by recovering the CD4 percentage curves from sparse and irregular data in the Multi-center AIDS Cohort Study.
A primary goal of integral field spectroscopic (IFS) surveys is to provide a statistical census of galaxies classified by their internal kinematics. As a result, the observational spin parameter, $lambda_R$, has become one of the most popular methods of quantifying the relative importance of velocity dispersion and rotation in supporting a galaxys inner structure. The goal of this paper is to examine the relationship between the observationally deduced $lambda_R$ and one of the most commonly used theoretical spin parameters in the literature, the Bullock et al. (2001) $lambda$. Using a set of $N$-body realisations of galaxies from which we construct mock IFS observations, we measure $lambda_R$ as an observer would, incorporating the effects of beam smearing and seeing conditions. Assuming parameters typical of current IFS surveys, we confirm that there are strong positive correlations between $lambda_R$ and measurement radius, and strong negative correlations between $lambda_R$ and size of the PSF, for late-type galaxies; these biases can be reduced using a recently proposed empirical correction. Once observational biases are corrected for, we find that $lambda_R$ provides a good approximation to $sim sqrt{3}/2 ; lambda(rm R_{rm eff})$, where $lambda$ is evaluated for the galactic stellar component within 1 R$_{rm eff}$.
We study, using simulated experiments inspired by thin film magnetic domain patterns, the feasibility of phase retrieval in X-ray diffractive imaging in the presence of intrinsic charge scattering given only photon-shot-noise limited diffraction data. We detail a reconstruction algorithm to recover the samples magnetization distribution under such conditions, and compare its performance with that of Fourier transform holography. Concerning the design of future experiments, we also chart out the reconstruction limits of diffractive imaging when photon- shot-noise and the intensity of charge scattering noise are independently varied. This work is directly relevant to the time-resolved imaging of magnetic dynamics using coherent and ultrafast radiation from X-ray free electron lasers and also to broader classes of diffractive imaging experiments which suffer noisy data, missing data or both.
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