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Register a new userPhysically motivated fit to mass surface density profiles observed in galaxies
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Polytropes have gained renewed interest because they account for several seemingly-disconnected observational properties of galaxies. Here we study if polytropes are also able to explain the stellar mass distribution within galaxies. We develop a code to fit surface density profiles using polytropes projected in the plane of the sky (propols). Sersic profiles are known to be good proxies for the global shapes of galaxies and we find that, ignoring central cores, propols and Sersic profiles are indistinguishable within observational errors (within 5 % over 5 orders of magnitude in surface density). The range of physically meaningful polytropes yields Sersic indexes between 0.4 and 6. The code has been systematically applied to ~750 galaxies with carefully measured mass density profiles and including all morphological types and stellar masses (7 < log (Mstar/Msun) < 12). The propol fits are systematically better than Sersic profiles when log(Mstar/Msun) < 9 and systematically worst when log(Mstar/Msun) > 10. Although with large scatter, the observed polytropic indexes increase with increasing mass and tend to cluster around m=5. For the most massive galaxies, propols are very good at reproducing their central parts, but they do not handle well cores and outskirts altogether. Polytropes are self-gravitating systems in thermal meta-equilibrium as defined by the Tsallis entropy. Thus, the above results are compatible with the principle of maximum Tsallis entropy dictating the internal structure in dwarf galaxies and in the central region of massive galaxies.
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Present-day multi-wavelength deep imaging surveys allow to characterise the outskirts of galaxies with unprecedented precision. Taking advantage of this situation, we define a new physically motivated measurement of size for galaxies based on the expected location of the gas density threshold for star formation. Employing both theoretical and observational arguments, we use the stellar mass density contour at 1 $M_{rm odot}$ pc$^{-2}$ as a proxy for this density threshold for star formation. This choice makes our size definition operative. With this new size measure, the intrinsic scatter of the global stellar mass ($M_{rm star}$) - size relation (explored over five orders of magnitude in stellar mass) decreases to $sim$0.06 dex. This value is 2.5 times smaller than the scatter measured using the effective radius ($sim$0.15 dex) and between 1.5 and 1.8 times smaller than those using other traditional size indicators such as $R_{rm 23.5,i}$ ($sim$0.09 dex), the Holmberg radius $R_{rm H}$ ($sim$0.09 dex) and the half-mass radius $R_{rm e,M_{star}}$ ($sim$0.11 dex). Moreover, galaxies with 10$^7$ $M_{rm odot} <$ $M_{star} < 10^{11}$ $M_{rm odot}$ increase monotonically in size following a power-law with a slope very close to 1/3, equivalent to an average stellar mass 3D density of $sim$4.5$times$10$^{-3}$ $M_{rm odot}$ pc$^{-3}$ for galaxies within this mass range. Galaxies with $M_{rm star}$$>$10$^{11}$ $M_{rm odot}$ show a different slope with stellar mass, which is suggestive of a larger gas density threshold for star formation at the epoch when their star formation peaks.
The Einasto profile has been successful in describing the density profiles of dark matter haloes in $Lambda$CDM N-body simulations. It has also been able to describe multiple components in the surface brightness profiles of galaxies. However, analytically projecting it to calculate quantities under projection is challenging. In this paper, we will see the development of a highly accurate analytical approximation for the mass (or counts) enclosed in an infinitely long cylindrical column for Einasto profiles--also known as the projected mass (or counts)--using a novel methodology. We will then develop a self-consistent high-accuracy model for the surface density from the expression for the projected mass. Both models are quite accurate for a broad family of functions, with a shape parameter $alpha$ varying by a factor of 100 in the range $0.05 lesssim alpha lesssim 5.0$, with fractional errors $sim 10^{-6}$ for $alpha lesssim 0.4$. Profiles with $alpha lesssim 0.4$ have been shown to fit the density profiles of dark matter haloes in N-body simulations as well as the luminosity profiles of the outer components of massive galaxies. Since the projected mass and the surface density are used in gravitational lensing, I will illustrate how these models facilitate (for the first time) analytical computation of several quantities of interest in lensing due to Einasto profiles. The models, however, are not limited to lensing and apply to similar quantities under projection, such as the projected luminosity, the projected (columnar) number counts and the projected density or the surface brightness.
The nuclear obscurer of Active Galactic Nuclei (AGN) is poorly understood in terms of its origin, geometry and dynamics. We investigate whether physically motivated geometries emerging from hydro-radiative simulations can be differentiated with X-ray reflection spectroscopy. For two new geometries, the radiative fountain model of Wada (2012) and a warped disk, we release spectral models produced with the ray tracing code XARS. We contrast these models with spectra of three nearby AGN taken by NuSTAR and Swift/BAT. Along heavily obscured sight-lines, the models present different 4-20keV continuum spectra. These can be differentiated by current observations. Spectral fits of the Circinus Galaxy favor the warped disk model over the radiative fountain, and clumpy or smooth torus models. The necessary reflector (NH>10^25/cm^2) suggests a hidden population of heavily Compton-thick AGN amongst local galaxies. X-ray reflection spectroscopy is a promising pathway to understand the nuclear obscurer in AGN.
The simplest scheme for predicting real galaxy properties after performing a dark matter simulation is to rank order the real systems by stellar mass and the simulated systems by halo mass and then simply assume monotonicity - that the more massive halos host the more massive galaxies. This has had some success, but we study here if a better motivated and more accurate matching scheme is easily constructed by looking carefully at how well one could predict the simulated IllustrisTNG galaxy sample from its dark matter computations. We find that using the dark matter rotation curve peak velocity, $v_{max}$, for normal galaxies reduces the error of the prediction by 30% (18% for central galaxies and 60% for satellite systems) - following expectations from Faber-Jackson and the physics of monolithic collapse. For massive systems with halo mass $>$ 10$^{12.5}$ M$_{odot}$ hierarchical merger driven formation is the better model and dark matter halo mass remains the best single metric. Using a new single variable that combines these effects, $phi$ $=$ $v_{max}$/$v_{max,12.7}$ + M$_{peak}$/(10$^{12.7}$ M$_{odot}$) allows further improvement and reduces the error, as compared to ranking by dark matter mass at $z=0$ by another 6% from $v_{max}$ ranking. Two parameter fits -- including environmental effects produce only minimal further impact.
We present a novel halo painting network that learns to map approximate 3D dark matter fields to realistic halo distributions. This map is provided via a physically motivated network with which we can learn the non-trivial local relation between dark matter density field and halo distributions without relying on a physical model. Unlike other generative or regressive models, a well motivated prior and simple physical principles allow us to train the mapping network quickly and with relatively little data. In learning to paint halo distributions from computationally cheap, analytical and non-linear density fields, we bypass the need for full particle mesh simulations and halo finding algorithms. Furthermore, by design, our halo painting network needs only local patches of dark matter density to predict the halos, and as such, it can predict the 3D halo distribution for any arbitrary simulation box size. Our neural network can be trained using small simulations and used to predict large halo distributions, as long as the resolutions are equivalent. We evaluate our models ability to generate 3D halo count distributions which reproduce, to a high degree, summary statistics such as the power spectrum and bispectrum, of the input or reference realizations.
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