Simulating the formation of a proto-cluster at z~2

We present results from two high-resolution hydrodynamical simulations of proto-cluster regions at z~2.1. The simulations have been compared to observational results for the socalled Spiderweb galaxy system, the core of a putative proto-cluster region at z = 2.16, found around a radio galaxy. The simulated regions have been chosen so as to form a poor cluster with M200~10^14 h-1 Msun (C1) and a rich cluster with M200~2x10^15 h-1 Msun (C2) at z = 0. The simulated proto-clusters show evidence of ongoing assembly of a dominating central galaxy. The stellar mass of the brightest cluster galaxy (BCG) of the C2 system is in excess with respect to observational estimates for the Spiderweb galaxy, with a total star formation rate which is also larger than indicated by observations. We find that the projected velocities of galaxies in the C2 cluster are consistent with observations, while those measured for the poorer cluster C1 are too low compared to the observed velocities. We argue that the Spiderweb complex resemble the high-redshift progenitor of a rich galaxy cluster. Our results indicate that the included supernovae feedback is not enough to suppress star formation in these systems, supporting the need of introducing AGN feedback. According to our simulations, a diffuse atmosphere of hot gas in hydrostatic equilibrium should already be present at this redshift, and enriched at a level comparable to that of nearby galaxy clusters. The presence of this gas should be detectable with future deep X-ray observations.Comment: 6 pages, 4 figures, accepted for publication in MNRAS (Letters

Lyman Alpha Emitter Evolution in the Reionization Epoch

Combining cosmological SPH simulations with a previously developed Lyman Alpha production/transmission model and the Early Reionization Model (ERM, reionization ends at redshift z~7), we obtain Lyman Alpha and UV Luminosity Functions (LFs) for Lyman Alpha Emitters (LAEs) for redshifts between 5.7 and 7.6. Matching model results to observations at z~5.7 requires escape fractions of Lyman Alpha, f_alpha=0.3, and UV (non-ionizing) continuum photons, f_c=0.22, corresponding to a color excess, E(B-V)=0.15. We find that (i) f_c increases towards higher redshifts, due the decreasing mean dust content of galaxies, (ii) the evolution of f_alpha/f_c hints at the dust content of the ISM becoming progressively inhomogeneous/clumped with decreasing redshift. The clustering photoionization boost is important during the initial reionization phases but has little effect on the Lyman Alpha LF for a highly ionized IGM. Halo (stellar) masses are in the range 10.0 < \log M_h < 11.8 (8.1 < \log M_* < 10.4) with M_h \propto M_*^{0.64}. The star formation rates are between 3-120 solar masses per year, mass-weighted mean ages are greater than 20 Myr at all redshifts, while the mean stellar metallicity increases from Z=0.12 to 0.22 solar metallicity from z~7.6 to z~5.7; both age and metallicity positively correlate with stellar mass. The brightest LAEs are all characterized by large star formation rates and intermediate ages (~200 Myr), while objects in the faint end of the Lyman Alpha LF show large age and star formation rate spreads. With no more free parameters, the Spectral Energy Distributions of three LAE at z~5.7 observed by Lai et al. (2007) are well reproduced by an intermediate age (182-220 Myr) stellar population and the above E(B-V) value.Comment: 13 pages, 9 figures, accepted to MNRA

Properties of the galaxy population in hydrodynamical simulations of clusters

We present a study of the galaxy population predicted by hydrodynamical simulations for a set of 19 galaxy clusters based on the GADGET-2 Tree+SPH code. These simulations include gas cooling, star formation, a detailed treatment of stellar evolution and chemical enrichment, as well as SN energy feedback in the form of galactic winds. We compute the spectro-photometric properties of the simulated galaxies. All simulations have been performed for two choices of the stellar initial mass function: a standard Salpeter IMF, and a top-heavier IMF. Several of the observational properties of the galaxy population in nearby clusters are reproduced fairly well by simulations. A Salpeter IMF is successful in accounting for the slope and the normalization of the color-magnitude relation for the bulk of the galaxy population. Simulated clusters have a relation between mass and optical luminosity which generally agrees with observations, both in normalization and slope. We find that galaxies are generally bluer, younger and more star forming in the cluster outskirts, thus reproducing the observational trends. However, simulated clusters have a total number of galaxies which is significantly smaller than the observed one, falling short by about a factor 2-3. Finally, the brightest cluster galaxies are always predicted to be too massive and too blue, when compared to observations, due to gas overcooling in the core cluster regions, even in the presence of a rather efficient SN feedback.Comment: 15 pages, 17 figures, to appear in MNRA

Evolution of the metal content of the intra-cluster medium with hydrodynamical simulations

We present a comparison between simulation results and X-ray observational data on the evolution of the metallicity of the intra-cluster medium (ICM). The simulations of galaxy clusters were performed with the Tree-SPH Gadget2 code that includes a detailed model of chemical evolution, by assuming three different shapes for the stellar initial mass function (IMF), namely the Salpeter (1955), Kroupa (2001) and Arimoto-Yoshii (1987) IMF. Our simulations predict significant radial gradients of the Iron abundance, which extend over the whole cluster virialized region. At larger radii, we do not detect any flattening of the metallicity profiles. As for the evolution of the ICM metal (Iron) abundance out to z=1, we find that it is determined by the combined action of (i) the sinking of already enriched gas, (ii) the ongoing metal production in galaxies and (iii) the locking of ICM metals in newborn stars. As a result, rather than suppressing the metallicity evolution, stopping star formation at z=1 has the effect of producing an even too fast evolution of the emission-weighted ICM metallicity with too high values at low redshift. Finally, we compare simulations with the observed rate of type-Ia supernovae per unit B-band luminosity (SnU_B). We find that our simulated clusters do not reproduce the decreasing trend of SnU_B at low redshift, unless star formation is truncated at z=1.Comment: 9 pages, 7 figures, to appear in MNRA

Utilización de enzimas fibrolíticas para mejorar la digestión de forrajes tropicales. I. Influencia del método de aplciacion en la producción de gas in vitro y la composición química

Los forrajes tropicales presentan, en general, un menor valor nutritivo que los forrajes de zonas templadas. Sin embargo, su disponibilidad suele ser elevada y en numerosas ocasiones son el único recurso alimenticio disponible para los animales rumiantes. Esta situación limita la productividad de estos animales y por ello se han investigado diferentes estrategias para aumentar el valor nutritivo de los forrajes tropicales. Una de las metodologías propuestas para incrementar la utilización digestiva de los forrajes es el tratamiento de los mismos con enzimas fibrolíticas (Carro y Ranilla, 2001), pero todavía son escasos los estudios realizados con forrajes tropicales. El objetivo de este trabajo fue evaluar el efecto de tres preparados enzimáticos en la fermentación ruminal in vitro y la degradabilidad de tres forrajes tropicales

Utilización de enzimas fibrolíticas para mejorar la digestión de forrajes tropicales. II. Efectos en la fermetación ruminal in vitro y la degradabilidad

En muchos países tropicales los sistemas productivos de animales rumiantes se basan en una amplia utilización de recursos forrajeros. Sin embargo, estos recursos suelen tener una baja calidad, por lo que cualquier mejora de su valor nutritivo tendrá una repercusión positiva en la productividad de los animales. En los últimos años se han realizado numerosos estudios para evaluar diferentes enzimas fibrolíticas como aditivos para mejorar el valor nutritivo de forrajes, pero la mayoría de ellos han utilizado forrajes de elevada calidad y apenas existen estudios con forrajes de baja calidad. Por otra parte, los resultados han sido muy variables, ya que la efectividad de las enzimas se ve afectada por numerosos factores, siendo el tipo de forraje y el método de aplicación de las enzimas dos de los más importantes (Giraldo et al., 2008). El objetivo del presente estudio fue evaluar el efecto de tres enzimas fibrolíticas exógenas en la fermentación ruminal in vitro de tres forrajes tropicales cuando las enzimas se aplicaron 24 h antes o en el momento de la incubación

High Frequency Cluster Radio Galaxies: Luminosity Functions and Implications for SZE Selected Cluster Samples

We study the overdensity of point sources in the direction of X-ray-selected galaxy clusters from the Meta-Catalog of X-ray detected Clusters of galaxies (MCXC; $\langle z \rangle = 0.14$) at South Pole Telescope (SPT) and Sydney University Molonglo Sky Survey (SUMSS) frequencies. Flux densities at 95, 150 and 220 GHz are extracted from the 2500 deg$^2$ SPT-SZ survey maps at the locations of SUMSS sources, producing a multi-frequency catalog of radio galaxies. In the direction of massive galaxy clusters, the radio galaxy flux densities at 95 and 150 GHz are biased low by the cluster Sunyaev-Zel'dovich Effect (SZE) signal, which is negative at these frequencies. We employ a cluster SZE model to remove the expected flux bias and then study these corrected source catalogs. We find that the high frequency radio galaxies are centrally concentrated within the clusters and that their luminosity functions (LFs) exhibit amplitudes that are characteristically an order of magnitude lower than the cluster LF at 843 MHz. We use the 150 GHz LF to estimate the impact of cluster radio galaxies on an SPT-SZ like survey. The radio galaxy flux typically produces a small bias on the SZE signal and has negligible impact on the observed scatter in the SZE mass-observable relation. If we assume there is no redshift evolution in the radio galaxy LF then $1.8\pm0.7$ percent of the clusters would be lost from the sample. Allowing for redshift evolution of the form $(1+z)^{2.5}$ increases the incompleteness to $5.6\pm1.0$ percent. Improved constraints on the evolution of the cluster radio galaxy LF require a larger cluster sample extending to higher redshift.Comment: Submitted to MNRA

The influence of diet on the effectiveness of garlic oil and cinnamaldehyde to manipulate in vitro ruminal fermentation and methane production.

The objective of this study was to evaluate the effects of increasing doses [0 (control: CON), 20, 60, 180 and 540 mg/L incubation medium] of garlic oil (GO) and cinnamaldehyde (CIN) on in vitro ruminal fermentation of two diets. Batch cultures of mixed ruminal microorganisms were inoculated with ruminal fluid from four sheep fed a medium-concentrate diet (MC; 50 : 50 alfalfa hay : concentrate) or four sheep fed a high-concentrate diet (HC; 15 : 85 barley straw : concentrate). Diets MC and HC were representative of those fed to dairy and fattening ruminants, respectively. Samples of each diet were used as incubation substrates for the corresponding inoculum, and the incubation was repeated on 4 different days (four replicates per experimental treatment). There were GO × diet-type and CIN × diet-type interactions (P 0.05) total volatile fatty acid (VFA) production at any dose. For MC diet, GO at 60, 180 and 540 mg/L decreased (P 0.05) on butyrate proportion were detected. Methane/VFA ratio was reduced (P < 0.05) by GO at 60, 180 and 540 mg/L for MC diet (0.23, 0.16 and 0.10 mol/mol, respectively), and by GO at 20, 60, 180 and 540 mg/L for HC diet (0.19, 0.19, 0.16 and 0.08 mol/mol, respectively), compared with CON (0.26 and 0.21 mol/mol for MC and HC diets, respectively). No effects (P = 0.16–0.85) of GO on final pH and concentrations of NH3-N and lactate were detected. For both diet types, the highest CIN dose decreased (P < 0.05) production of total VFA, gas and methane, which would indicate an inhibition of fermentation. Compared with CON, CIN at 180 mg/L increased (P < 0.05) acetate proportion for the MC (629 and 644 mmol/mol total VFA for CON and CIN, respectively) and HC (525 and 540 mmol/mol total VFA, respectively) diets, without affecting the proportions of any other VFA or total VFA production. Whereas for MC diet CIN at 60 and 180 mg/L decreased (P < 0.05) NH3-N concentrations compared with CON, only a trend (P < 0.10) was observed for CIN at 180 mg/L with the HC diet. Supplementation of CIN up to 180 mg/L did not affect (P = 0.18–0.99) lactate concentrations and production of gas and methane for any diet. The results show that effectiveness of GO and CIN to modify ruminal fermentation may depend on diet type, which would have practical implications if they are confirmed in vivo

Machine learning to identify ICL and BCG in simulated galaxy clusters

Nowadays, Machine Learning techniques offer fast and efficient solutions for classification problems that would require intensive computational resources via traditional methods. We examine the use of a supervised Random Forest to classify stars in simulated galaxy clusters after subtracting the member galaxies. These dynamically different components are interpreted as the individual properties of the stars in the Brightest Cluster Galaxy (BCG) and IntraCluster Light (ICL). We employ matched stellar catalogues (built from the different dynamical properties of BCG and ICL) of 29 simulated clusters from the DIANOGA set to train and test the classifier. The input features are cluster mass, normalized particle cluster-centric distance, and rest-frame velocity. The model is found to correctly identify most of the stars, while the larger errors are exhibited at the BCG outskirts, where the differences between the physical properties of the two components are less obvious. We investigate the robustness of the classifier to numerical resolution, redshift dependence (up to z = 1), and included astrophysical models. We claim that our classifier provides consistent results in simulations for z 0.1 R-200) is significantly affected by uncertainties in the classification process. In conclusion, this work suggests the importance of employing Machine Learning to speed up a computationally expensive classification in simulations