Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAn Improved and Physically-Motivated Scheme for Matching Galaxies with Dark Matter Halos
79
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
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.
rate research
Read More
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.
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.
In our modern understanding of galaxy formation, every galaxy forms within a dark matter halo. The formation and growth of galaxies over time is connected to the growth of the halos in which they form. The advent of large galaxy surveys as well as high-resolution cosmological simulations has provided a new window into the statistical relationship between galaxies and halos and its evolution. Here we define this galaxy-halo connection as the multi-variate distribution of galaxy and halo properties that can be derived from observations and simulations. This connection provides a key test of physical galaxy formation models; it also plays an essential role in constraints of cosmological models using galaxy surveys and in elucidating the properties of dark matter using galaxies. We review techniques for inferring the galaxy-halo connection and the insights that have arisen from these approaches. Some things we have learned are that galaxy formation efficiency is a strong function of halo mass; at its peak in halos around a pivot halo mass of 10^12 Msun, less than 20% of the available baryons have turned into stars by the present day; the intrinsic scatter in galaxy stellar mass is small, less than 0.2 dex at a given halo mass above this pivot mass; below this pivot mass galaxy stellar mass is a strong function of halo mass; the majority of stars over cosmic time were formed in a narrow region around this pivot mass. We also highlight key open questions about how galaxies and halos are connected, including understanding the correlations with secondary properties and the connection of these properties to galaxy clustering.
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.
A new family of nonrelativistic, Newtonian, non-quantum equilibrium configurations describing galactic halos is introduced, by considering strange quark matter conglomerates with masses larger than about 8 GeV as new possible components of the dark matter. Originally introduced to explain the state of matter in neutron stars, such conglomerates may also form in the high-density and temperature conditions of the primordial Universe and then decouple from ordinary baryonic matter, providing the fundamental components of dark matter for the formation of pristine gravitational potential wells and the subsequent evolution of cosmic structures. The obtained results for halo mass and radius are consistent with the rotational velocity curve observed in the Galaxy. Additionally, the average density of such dark matter halos is similar to that derived for halos of dwarf spheroidal galaxies, which can therefore be interpreted as downscal
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
