The Dual Origin of Stellar Halos II: Chemical Abundances as Tracers of Formation History

Fully cosmological, high resolution N-Body + SPH simulations are used to investigate the chemical abundance trends of stars in simulated stellar halos as a function of their origin. These simulations employ a physically motivated supernova feedback recipe, as well as metal enrichment, metal cooling and metal diffusion. As presented in an earlier paper, the simulated galaxies in this study are surrounded by stellar halos whose inner regions contain both stars accreted from satellite galaxies and stars formed in situ in the central regions of the main galaxies and later displaced by mergers into their inner halos. The abundance patterns ([Fe/H] and [O/Fe]) of halo stars located within 10 kpc of a solar-like observer are analyzed. We find that for galaxies which have not experienced a recent major merger, in situ stars at the high [Fe/H] end of the metallicity distribution function are more [alpha/Fe]-rich than accreted stars at similar [Fe/H]. This dichotomy in the [O/Fe] of halo stars at a given [Fe/H] results from the different potential wells within which in situ and accreted halo stars form. These results qualitatively match recent observations of local Milky Way halo stars. It may thus be possible for observers to uncover the relative contribution of different physical processes to the formation of stellar halos by observing such trends in the halo populations of the Milky Way, and other local L* galaxies.Comment: Version accepted for publication in ApJ Part 1. This version of the paper has been extended to include a detailed discussion of numerical issue

Cosmic variance of the local Hubble flow in large-scale cosmological simulations

The increasing precision in the determination of the Hubble parameter has reached a per cent level at which large-scale cosmic flows induced by inhomogeneities of the matter distribution become non-negligible. Here, we use large-scale cosmological N-body simulations to study statistical properties of the local Hubble parameter as measured by local observers. We show that the distribution of the local Hubble parameter depends not only on the scale of inhomogeneities, but also on how one defines the positions of observers in the cosmic web and what reference frame is used. Observers located in random dark matter haloes measure on average lower expansion rates than those at random positions in space or in the centres of cosmic voids, and this effect is stronger from the halo rest frames compared to the cosmic microwave background (CMB) rest frame. We compare the predictions for the local Hubble parameter with observational constraints based on Type Ia supernova (SNIa) and CMB observations. Due to cosmic variance, for observers located in random haloes we show that the Hubble constant determined from nearby SNIa may differ from that measured from the CMB by ±0.8 per cent at 1σ statistical significance. This scatter is too small to significantly alleviate a recently claimed discrepancy between current measurements assuming a flat Λ cold dark matter (ΛCDM) model. However, for observers located in the centres of the largest voids permitted by the standard ΛCDM model, we find that Hubble constant measurements from SNIa would be biased high by 5 per cent, rendering this tension non-existent in this extreme case

The MUSIC of Galaxy Clusters I: Baryon properties and Scaling Relations of the thermal Sunyaev-Zel'dovich Effect

We introduce the Marenostrum-MultiDark SImulations of galaxy Clusters (MUSIC) Dataset, one of the largest sample of hydrodynamically simulated galaxy clusters with more than 500 clusters and 2000 groups. The objects have been selected from two large N-body simulations and have been resimulated at high resolution using SPH together with relevant physical processes (cooling, UV photoionization, star formation and different feedback processes). We focus on the analysis of the baryon content (gas and star) of clusters in the MUSIC dataset both as a function of aperture radius and redshift. The results from our simulations are compared with the most recent observational estimates of the gas fraction in galaxy clusters at different overdensity radii. When the effects of cooling and stellar feedbacks are included, the MUSIC clusters show a good agreement with the most recent observed gas fractions quoted in the literature. A clear dependence of the gas fractions with the total cluster mass is also evident. The impact of the aperture radius choice, when comparing integrated quantities at different redshifts, is tested: the standard definition of radius at a fixed overdensity with respect to critical density is compared with a definition based on the redshift dependent overdensity with respect to background density. We also present a detailed analysis of the scaling relations of the thermal SZ (Sunyaev Zel'dovich) Effect derived from MUSIC clusters. The integrated SZ brightness, Y, is related to the cluster total mass, M, as well as, the M-Y counterpart, more suitable for observational applications. Both laws are consistent with predictions from the self-similar model, showing a very low scatter. The effects of the gas fraction on the Y-M scaling and the presence of a possible redshift dependence on the Y-M scaling relation are also explored.Comment: 22 pages, 25 figures, accepted for pubblication by MNRA

The quijote simulations

The Quijote simulations are a set of 44,100 full N-body simulations spanning more than 7000 cosmological models in the hyperplane. At a single redshift, the simulations contain more than 8.5 trillion particles over a combined volume of 44,100 each simulation follows the evolution of 2563, 5123, or 10243 particles in a box of 1 h -1 Gpc length. Billions of dark matter halos and cosmic voids have been identified in the simulations, whose runs required more than 35 million core hours. The Quijote simulations have been designed for two main purposes: (1) to quantify the information content on cosmological observables and (2) to provide enough data to train machine-learning algorithms. In this paper, we describe the simulations and show a few of their applications. We also release the petabyte of data generated, comprising hundreds of thousands of simulation snapshots at multiple redshifts; halo and void catalogs; and millions of summary statistics, such as power spectra, bispectra, correlation functions, marked power spectra, and estimated probability density functions

CT Volumetry and Basic Texture Analysis as Surrogate Markers in Advanced Non-small-cell Lung Cancer.

IntroductionWe evaluated volumetric tumor measurements and computed tomography texture analysis as prognostic indicators in patients with advanced non-small-cell lung cancer when compared with the unidimensional tumor size measurements used in Response Evaluation Criteria in Solid Tumors (RECIST).Patients and methodsIn a retrospective review, computed tomography examinations in 77 patients with advanced non-small-cell lung cancer were evaluated before and after 2 cycles of chemotherapy. Baseline and changes in tumor diameter, volume, and texture were analyzed. Survival was analyzed with Cox regression analysis and Kaplan-Meier survival statistics.ResultsCox regression analysis demonstrated that only change in tumor volume (exp(B) = 1.006; P = .02) and the initial sum of the largest target lesion diameters predicted survival (exp(B) = 1.013; P = .02). Kaplan-Meier statistics demonstrated that patients with an initial sum of the largest target lesion diameters less than 88 mm had median survival time of 587 days (95% confidence interval [CI], 269-905 days), compared with the survival of those with larger tumor burden of 407 days (95% CI, 235-579 days). Patients in whom tumor volume decreased by more than 29% had a median survival time of 622 days (95% CI, 448-796 days), compared with 305 days for those with less decrease (95% CI, 34-240 days).ConclusionThis study demonstrates that change in lung tumor volume is a better marker of patient survival than change of unidimensional diameter measurements in our cohort. If confirmed in larger studies, this suggests that volumetry might improve clinical decision-making for individual patients and allow for faster assessment of new treatments