Showing papers by "Ravi K. Sheth published in 2022"
TL;DR: In this paper , the authors show that a gradient which has larger magnitude in the center increases the estimated total stellar mass (M ∗ ) and reduces the scale which contains half this mass (R e, ∗ ), compared to when the gradient is ignored.
Abstract: Gradients in the stellar populations (SP) of galaxies – e.g., in age, metallicity, stellar Initial Mass Function (IMF) – can result in gradients in the stellar mass to light ratio, M ∗ /L . Such gradients imply that the distribution of the stellar mass and light are different. For old SPs, e.g., in early-type galaxies at z ∼ 0, the M ∗ /L gradients are weak if driven by variations in age and metallicity, but significantly larger if driven by the IMF. A gradient which has larger M ∗ /L in the center increases the estimated total stellar mass ( M ∗ ) and reduces the scale which contains half this mass ( R e, ∗ ), compared to when the gradient is ignored. For the IMF gradients inferred from fitting MILES simple SP models to the H β , (cid:104) Fe (cid:105) , [MgFe] and TiO 2SDSS absorption lines measured in spatially resolved spectra of early-type galaxies in the MaNGA survey, the fractional change in R e, ∗ can be significantly larger than that in M ∗ , especially when the light is more centrally concentrated. The R e, ∗ − M ∗ correlation which results is offset by 0.3 dex to smaller sizes compared to when these gradients are ignored. Comparisons with ‘quiescent’ galaxies at higher- z must account for evolution in SP gradients (especially age and IMF) and the light profile before drawing conclusions about how R e, ∗ and M ∗ evolve. The implied merging between higher- z and the present is less contrived if R e, ∗ /R e at z ∼ 0 is closer to our IMF-driven gradient calibration than to unity.
8 citations
TL;DR: In this article , a weighted, semidiscrete, fast optimal transport (OT) algorithm for reconstructing the Lagrangian positions of protohalos from their evolved Eulerian positions is presented.
Abstract: A weighted, semidiscrete, fast optimal transport (OT) algorithm for reconstructing the Lagrangian positions of protohalos from their evolved Eulerian positions is presented. The algorithm makes use of a mass estimate of the biased tracers and of the distribution of the remaining mass (the "dust") but is robust to errors in the mass estimates. Tests with state-of-art cosmological simulations show that if the dust is assumed to have a uniform spatial distribution, then the shape of the OT-reconstructed pair correlation function of the tracers is very close to linear theory, enabling subpercent precision in the baryon acoustic oscillation distance scale that depends weakly, if at all, on a cosmological model. With a more sophisticated model for the dust, OT returns an estimate of the displacement field which yields superb reconstruction of the protohalo positions and, hence, of the shape and amplitude of the initial pair correlation function of the tracers. This enables direct and independent determinations of the bias factor b and the smearing scale Σ, potentially providing new methods for breaking the degeneracy between b and σ_{8}.
TL;DR: This work describes a Bayesian framework for determining how many basis functions to use and comparing one basis set with another and provides intuition into how one’s degree of belief in different basis sets together determine the derived constraints.
Abstract: Constraints on cosmological parameters are often distilled from sky surveys by fitting templates to summary statistics of the data that are motivated by a fiducial cosmological model. However, recent work has shown how to estimate the distance scale using templates that are more generic: the basis functions used are not explicitly tied to any one cosmological model. We describe a Bayesian framework for (i) determining how many basis functions to use and (ii) comparing one basis set with another. Our formulation provides intuition into how (a) one’s degree of belief in different basis sets, (b) the fact that the choice of priors depends on basis set, and (c) the data set itself, together determine the derived constraints. We illustrate our framework using measurements in simulated datasets before applying it to real data.