Arnau Pujol

Galaxy bias from the Dark Energy Survey Science Verification data: Combining galaxy density maps and weak lensing maps

We measure the redshift evolution of galaxy bias for a magnitude-limited galaxy sample by combining the galaxy density maps and weak lensing shear maps for a ˜116 deg2 area of the Dark Energy Survey (DES) Science Verification (SV) data. This method was first developed in Amara et al. and later re-examined in a companion paper with rigorous simulation tests and analytical treatment of tomographic measurements. In this work we apply this method to the DES SV data and measure the galaxy bias for a i < 22.5 galaxy sample. We find the galaxy bias and 1sigma error bars in four photometric redshift bins to be 1.12 ± 0.19 (z = 0.2-0.4), 0.97 ± 0.15 (z = 0.4-0.6), 1.38 ± 0.39 (z = 0.6-0.8), and 1.45 ± 0.56 (z = 0.8-1.0). These measurements are consistent at the 2sigma level with measurements on the same data set using galaxy clustering and cross-correlation of galaxies with cosmic microwave background lensing, with most of the redshift bins consistent within the 1sigma error bars. In addition, our method provides the only sigma8 independent constraint among the three. We forward model the main observational effects using mock galaxy catalogues by including shape noise, photo-z errors, and masking effects. We show that our bias measurement from the data is consistent with that expected from simulations. With the forthcoming full DES data set, we expect this method to provide additional constraints on the galaxy bias measurement from more traditional methods. Furthermore, in the process of our measurement, we build up a 3D mass map that allows further exploration of the dark matter distribution and its relation to galaxy evolution.

Subhaloes gone Notts: the clustering properties of subhaloes

We present a study of the substructure finder dependence of subhalo clustering in the Aquarius Simulation. We run 11 different subhalo finders on the haloes of the Aquarius Simulation and study their differences in the density profile, mass fraction and two-point correlation function of subhaloes in haloes. We also study the mass and v(max) dependence of subhalo clustering. As the Aquarius Simulation has been run at different resolutions, we study the convergence with higher resolutions. We find that the agreement between finders is at around the 10 per cent level inside R-200 and at intermediate resolutions when a mass threshold is applied, and better than 5 per cent when v(max) is restricted instead of mass. However, some discrepancies appear in the highest resolution, underlined by an observed resolution dependence of subhalo clustering. This dependence is stronger for the smallest subhaloes, which are more clustered in the highest resolution, due to the detection of subhaloes within subhaloes (the sub-subhalo term). This effect modifies the mass dependence of clustering in the highest resolutions. We discuss implications of our results for models of subhalo clustering and their relation with galaxy clustering.

Are the halo occupation predictions consistent with large scale galaxy clustering

We study how well we can reconstruct the 2-point clustering of galaxies on linear scales, as a function of mass and luminosity, using the halo occupation distribution (HOD) in several semi-analytical models (SAMs) of galaxy formation from the Millennium Simulation. We find that HOD with Friends of Friends groups can reproduce galaxy clustering better than gravitationally bound haloes. This indicates that Friends of Friends groups are more directly related to the clustering of these regions than the bound particles of the overdensities. In general we find that the reconstruction works at best to 5% accuracy: it underestimates the bias for bright galaxies. This translates to an overestimation of 50% in the halo mass when we use clustering to calibrate mass. We also found a degeneracy on the mass prediction from the clustering amplitude that affects all the masses. This effect is due to the clustering dependence on the host halo substructure, an indication of assembly bias. We show that the clustering of haloes of a given mass increases with the number of subhaloes, a result that only depends on the underlying matter distribution. As the number of galaxies increases with the number of subhaloes in SAMs, this results in a low bias for the HOD reconstruction. We expect this effect to apply to other models of galaxy formation, including the real universe, as long as the number of galaxies incresases with the number of subhaloes. We have also found that the reconstructions of galaxy bias from the HOD model fails for low mass haloes with M = 3-5x10^11 Msun/h. We find that this is because galaxy clustering is more strongly affected by assembly bias for these low masses.

A new method to measure galaxy bias by combining the density and weak lensing fields

We present a new method to measure the redshift-dependent galaxy bias by combining information from the galaxy density field and the weak lensing field. This method is based on Amara et al. (2012), where they use the galaxy density field to construct a bias-weighted convergence field kg. The main difference between Amara et al. (2012) and our new implementation is that here we present another way to measure galaxy bias using tomography instead of bias parameterizations. The correlation between kg and the true lensing field k allows us to measure galaxy bias using different zero-lag correlations, such as / or / . Our method measures the linear bias factor on linear scales under the assumption of no stochasticity between galaxies and matter. We use the MICE simulation to measure the linear galaxy bias for a flux-limited sample (i

nIFTy Cosmology: the clustering consistency of galaxy formation models

We present a clustering comparison of 12 galaxy formation models [including semi-analytic models (SAMs) and halo occupation distribution (HOD) models] all run on halo catalogues and merger trees extracted from a single Λ cold dark matter N-body simulation. We compare the results of the measurements of the mean halo occupation numbers, the radial distribution of galaxies in haloes and the two-point correlation functions (2PCF). We also study the implications of the different treatments of orphan (galaxies not assigned to any dark matter subhalo) and non-orphan galaxies in these measurements. Our main result is that the galaxy formation models generally agree in their clustering predictions but they disagree significantly between HOD and SAMs for the orphan satellites. Although there is a very good agreement between the models on the 2PCF of central galaxies, the scatter between the models when orphan satellites are included can be larger than a factor of 2 for scales smaller than 1 h−1 Mpc. We also show that galaxy formation models that do not include orphan satellite galaxies have a significantly lower 2PCF on small scales, consistent with previous studies. Finally, we show that the 2PCF of orphan satellites is remarkably different between SAMs and HOD models. Orphan satellites in SAMs present a higher clustering than in HOD models because they tend to occupy more massive haloes. We conclude that orphan satellites have an important role on galaxy clustering and they are the main cause of the differences in the clustering between HOD models and SAMs.

What determines large scale galaxy clustering: halo mass or local density?

Using dark matter simulations we show how halo bias is determined by local density and not by halo mass. This is not totally surprising, as according to the peak-background split model, local density is the property that constraints bias at large scales. Massive haloes have a high clustering because they reside in high density regions. Small haloes can be found in a wide range of environments which determine their clustering amplitudes differently. This contradicts the assumption of standard Halo Occupation Distribution (HOD) models that the bias and occupation of haloes is determined solely by their mass. We show that the bias of central galaxies from semi-analytic models of galaxy formation as a function of luminosity and colour is not correctly predicted by the standard HOD model. Using local density instead of halo mass the HOD model correctly predicts galaxy bias. These results indicate the need to include information about local density and not only mass in order to correctly apply HOD analysis in these galaxy samples. This new model can be readily applied to observations and has the advantage that the galaxy density can be directly observed, in contrast with the dark matter halo mass.

Cosmic CARNage I: on the calibration of galaxy formation models

We present a comparison of nine galaxy formation models, eight semi-analytical, and one halo occupation distribution model, run on the same underlying cold dark matter simulation (cosmological box of comoving width 125h−1 Mpc, with a dark-matter particle mass of 1.24 × 109h−1M⊙) and the same merger trees. While their free parameters have been calibrated to the same observational data sets using two approaches, they nevertheless retain some ‘memory’ of any previous calibration that served as the starting point (especially for the manually tuned models). For the first calibration, models reproduce the observed z = 0 galaxy stellar mass function (SMF) within 3σ. The second calibration extended the observational data to include the z = 2 SMF alongside the z ∼ 0 star formation rate function, cold gas mass, and the black hole–bulge mass relation. Encapsulating the observed evolution of the SMF from z = 2 to 0 is found to be very hard within the context of the physics currently included in the models. We finally use our calibrated models to study the evolution of the stellar-to-halo mass (SHM) ratio. For all models, we find that the peak value of the SHM relation decreases with redshift. However, the trends seen for the evolution of the peak position as well as the mean scatter in the SHM relation are rather weak and strongly model dependent. Both the calibration data sets and model results are publicly available.

Measuring Linear and Non-linear Galaxy Bias Using Counts-in-Cells in the Dark Energy Survey Science Verification Data

Non-linear bias measurements require a great level of control of potential systematic effects in galaxy redshift surveys. Our goal is to demonstrate the viability of using Counts-in-Cells (CiC), a statistical measure of the galaxy distribution, as a competitive method to determine linear and higher-order galaxy bias and assess clustering systematics. We measure the galaxy bias by comparing the first four moments of the galaxy density distribution with those of the dark matter distribution. We use data from the MICE simulation to evaluate the performance of this method, and subsequently perform measurements on the public Science Verification (SV) data from the Dark Energy Survey (DES). We find that the linear bias obtained with CiC is consistent with measurements of the bias performed using galaxy-galaxy clustering, galaxy-galaxy lensing,CMB lensing, and shear+clustering measurements. Furthermore, we compute the projected (2D) non-linear bias using the expansion δ g =∑ 3 k=0 (b k /k!)δ k, finding a non-zero value for b 2 at the 3σ level. We also check a non-local bias model and show that the linear bias measurements are robust to the addition of new parameters. We compare our 2D results to the 3D prediction and find compatibility in the large scale regime (>30 Mpc h −1)

The effects of assembly bias on galaxy clustering predictions

The Halo Occupation Distribution (HOD) model is frequently used in surveys to predict the mass of the dark matter haloes from the clustering of galaxies. On the other hand, semi-analytical models (SAMs) of galaxy formation are often used to populate simulations according to some physical prescriptions and merger trees. We compare galaxy bi s measured in SAMs with the bias reconstructed from the halo bias and HOD measured in the sam simulations. We find that the reconstruction underestimates the bias by 5 − 10%, which translates in 50% overestimation of the halo mass. We attribute this failure to assembly bias. The clustering of haloes with M . 3−5×10hM⊙ depends strongly on environment (number of subhaloes) and t his also reflects in different clustering for galaxies with different lumino sity but equal halo mass. Thus we need to include properties other than halo mass to do a proper HOD r econstruction.