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Register a new userA new fitting concept for the robust determination of Sersic model parameters
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The Sersic law (SL) offers a versatile functional form for the structural characterization of galaxies near and far. Whereas applying it to galaxies with a genuine SL luminosity distribution yields a robust determination of the Sersic exponent eta and effective surface brightness $mu_{rm eff}$, this is not necessarily the case for galaxies whose surface brightness profiles (SBPs) appreciably deviate from the SL (eg, early-type galaxies with a depleted core and nucleated dwarf ellipticals, or most late-type galaxies-LTGs). In this general case of imperfect SL profiles, the best-fitting solution may significantly depend on the radius (or surface brightness) interval fit and corrections for point spread function (PSF) convolution effects. Such uncertainties may then affect, in a non-easily predictable manner, automated structural studies of galaxies. We present a fitting concept (iFIT) that permits a robust determination of the equivalent SL model for the general case of galaxies with imperfect SL profiles. iFIT has been extensively tested on synthetic data with a Sersic index 0.3<${eta}$<4.2 and an effective radius 1<$rm{R}_{eff}$ (arcs)<20. Applied to non PSF-convolved data, iFIT can infer the Sersic exponent eta with an absolute error of <0.2 even for shallow SBPs. As for PSF-degraded data, iFIT can recover the input SL model parameters with a satisfactorily accuracy almost over the entire considered parameter space as long as FWHM(PSF)<$rm{R}_{eff}$. Tests indicate that iFIT shows little sensitivity on PSF corrections and the SBP limiting surface brightness, and that subtraction of the best-fitting SL model in two different bands yields a good match to the observed radial color profile. The publicly available iFIT offers an efficient tool for the non-supervised structural characterization of large galaxy samples, as those expected to become available with Euclid and LSST.
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Predicting the merger timescale ($tau_{rm merge}$) of merging dark matter halos, based on their orbital parameters and the structural properties of their hosts, is a fundamental problem in gravitational dynamics that has important consequences for our understanding of cosmological structure formation and galaxy formation. Previous models predicting $tau_{rm merge}$ have shown varying degrees of success when compared to the results of cosmological $N$-body simulations. We build on this previous work and propose a new model for $tau_{rm merge}$ that draws on insights derived from these simulations. We find that published predictions can provide reasonable estimates for $tau_{rm merge}$ based on orbital properties at infall, but tend to underpredict $tau_{rm merge}$ inside the host virial radius ($R_{200}$) because tidal stripping is neglected, and overpredict it outside $R_{200}$ because the host mass is underestimated. Furthermore, we find that models that account for orbital angular momentum via the circular radius $R_{rm circ}$ underpredict (overpredict) $tau_{rm merge}$ for bound (unbound) systems. By fitting for the dependence of $tau_{rm merge}$ on various orbital and host halo properties,we derive an improved model for $tau_{rm merge}$ that can be applied to a merging halo at any point in its orbit. Finally, we discuss briefly the implications of our new model for $tau_{rm merge}$ for semi-analytical galaxy formation modelling.
We investigate the utility and robustness of a new statistic, $omega_{ell}left(r_{c}right)$, for analyzing Baryon Acoustic Oscillations (BAO). We apply $omega_{ell}left(r_{c}right)$, introduced in Xu et al. (2010), to mocks and data from the Sloan Digital Sky Survey (SDSS)-III Baryon Oscillation Spectroscopic Survey (BOSS) included in the SDSS Data Release Eleven (DR11). We fit the anisotropic clustering using the monopole and quadrupole of the $omega_{ell}left(r_{c}right)$ statistic in a manner similar to conventional multipole fitting methods using the correlation function as detailed in (Xu et al. 2012). To test the performance of the $omega_{ell}left(r_{c}right)$ statistic we compare our results to those obtained using the multipoles. The results are in agreement. We also conduct a brief investigation into some of the possible advantages of using the $omega_{ell}left(r_{c}right)$ statistic for BAO analysis. The $omega_{ell}left(r_{c}right)$ analysis matches the stability of the multipoles analysis in response to artificially introduced distortions in the data, without using extra nuisance parameters to improve the fit. When applied to data with systematics, the $omega_{ell}left(r_{c}right)$ statistic again matches the performance of fitting the multipoles without using nuisance parameters. In all the analyzed circumstances, we find that fitting the $omega_{ell}left(r_{c}right)$ statistic removes the requirement for extra nuisance parameters.
Elemental abundances of stars are the result of the complex enrichment history of their galaxy. Interpretation of observed abundances requires flexible modeling tools to explore and quantify the information about Galactic chemical evolution (GCE) stored in such data. Here we present Chempy, a newly developed code for GCE modeling, representing a parametrized open one-zone model within a Bayesian framework. A Chempy model is specified by a set of 5-10 parameters that describe the effective galaxy evolution along with the stellar and star-formation physics: e.g. the star-formation history, the feedback efficiency, the stellar initial mass function (IMF) and the incidence of supernova type Ia (SN Ia). Unlike established approaches, Chempy can sample the posterior probability distribution in the full model parameter space and test data-model matches for different nucleosynthetic yield sets. We extend Chempy to a multi-zone scheme. As an illustrative application, we show that interesting parameter constraints result from only the ages and elemental abundances of Sun, Arcturus and the present-day interstellar medium (ISM). For the first time, we use such information to infer IMF parameter via GCE modeling, where we properly marginalize over nuisance parameters and account for different yield sets. We find that of the IMF $11.6_{-1.6}^{+2.1}$ % explodes as core-collapse SN, compatible with Salpeter 1955. We also constrain the incidence of SN Ia per 10^3 Msun to 0.5-1.4. At the same time, this Chempy application shows persistent discrepancies between predicted and observed abundances for some elements, irrespective of the chosen yield set. These cannot be remedied by any variations of Chempys parameters and could be an indication for missing nucleosynthetic channels. Chempy should be a powerful tool to confront predictions from stellar nucleosynthesis with far more complex abundance data sets.
We present the analytical framework for converting projected light distributions with a Sersic profile into three-dimensional light distributions for stellar systems of arbitrary triaxial shape. The main practical result is the definition of a simple yet robust measure of intrinsic galaxy size: the median radius $r_mathrm{med}$, defined as the radius of a sphere that contains 50% of the total luminosity or mass, that is, the median distance of a star to the galaxy center. We examine how $r_mathrm{med}$ depends on projected size measurements as a function of Sersic index and intrinsic axis ratios, and demonstrate its relative independence of these parameters. As an application we show that the projected semi-major axis length of the ellipse enclosing 50% of the light is an unbiased proxy for $r_mathrm{med}$, with small galaxy-to-galaxy scatter of $sim$10% (1$sigma$), under the condition that the variation in triaxiality within the population is small. For galaxy populations with unknown or a large range in triaxiality an unbiased proxy for $r_mathrm{med}$ is $1.3times R_{e}$, where $R_{e}$ is the circularized half-light radius, with galaxy-to-galaxy scatter of 20-30% (1$sigma$). We also describe how inclinations can be estimated for individual galaxies based on the measured projected shape and prior knowledge of the intrinsic shape distribution of the corresponding galaxy population. We make the numerical implementation of our calculations available.
Ariel has been selected as the next ESA M4 science mission and it is expected to be launched in 2028. During its 4-year mission, Ariel will observe the atmospheres of a large and diversified population of transiting exoplanets. A key factor for the achievement of the scientific goal of Ariel is the selection strategy for the definition of the input target list. A meaningful choice of the targets requires an accurate knowledge of the planet hosting star properties and this is necessary to be obtained well before the launch. In this work, we present the results of a bench-marking analysis between three different spectroscopic techniques used to determine stellar parameters for a selected number of targets belonging to the Ariel reference sample. We aim to consolidate a method that will be used to homogeneously determine the stellar parameters of the complete Ariel reference sample. Homogeneous, accurate and precise derivation of stellar parameters is crucial for characterizing exoplanet-host stars and in turn is a key factor for the accuracy of the planet properties.
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