SCORCH I: The Galaxy-Halo Connection in the First Billion Years

SCORCH (Simulations and Constructions of the Reionization of Cosmic Hydrogen) is a new project to study the Epoch of Reionization (EoR). In this first paper, we probe the connection between observed high-redshift galaxies and simulated dark matter halos to better understand the abundance and evolution of the primary source of ionizing radiation. High-resolution N-body simulations are run to quantify the abundance of dark matter halos as a function of mass $M$, accretion rate $dot{M}$, and redshift $z$. A new fit for the halo mass function $dn/dM$ is $approx 20%$ more accurate at the high-mass end where bright galaxies are expected to reside. A novel approach is used to fit the halo accretion rate function $dn/ddot{M}$ in terms of the halo mass function. Abundance matching against the observed galaxy luminosity function is used to estimate the luminosity-mass relation and the luminosity-accretion-rate relation. The inferred star formation efficiency is not monotonic with $M$ nor $dot{M}$, but reaches a maximum value at a characteristic mass $sim 2 times 10^{11} M_odot$ and a characteristic accretion rate $sim 6 times 10^2 M_odot/{rm yr}$ at $z approx 6$. We find a universal EoR luminosity-accretion-rate relation and construct a fiducial model for the galaxy luminosity function. The Schechter parameters evolve such that $phi_star$ decreases, $M_star$ is more positive (fainter), and $alpha$ is more negative (steeper) at higher redshifts. We forecast for the upcoming James Webb Space Telescope and show that with apparent magnitude limit $m_{rm AB} approx 31 (32)$, it can observe $gtrsim 11 (24)$ unlensed galaxies per square degree per unit redshift at least down to $M_star$ at $z lesssim 13 (14)$.

A Machine Learning Approach for Dynamical Mass Measurements of Galaxy Clusters

We present a modern machine learning approach for cluster dynamical mass measurements that is a factor of two improvement over using a conventional scaling relation. Different methods are tested against a mock cluster catalog constructed using halos with mass >= 10^14 Msolar/h from Multidarks publicly-available N-body MDPL halo catalog. In the conventional method, we use a standard M(sigma_v) power law scaling relation to infer cluster mass, M, from line-of-sight (LOS) galaxy velocity dispersion, sigma_v. The resulting fractional mass error distribution is broad, with width=0.87 (68% scatter), and has extended high-error tails. The standard scaling relation can be simply enhanced by including higher-order moments of the LOS velocity distribution. Applying the kurtosis as a correction term to log(sigma_v) reduces the width of the error distribution to 0.74 (16% improvement). Machine learning can be used to take full advantage of all the information in the velocity distribution. We employ the Support Distribution Machines (SDMs) algorithm that learns from distributions of data to predict single values. SDMs trained and tested on the distribution of LOS velocities yield width=0.46 (47% improvement). Furthermore, the problematic tails of the mass error distribution are effectively eliminated. Decreasing cluster mass errors will improve measurements of the growth of structure and lead to tighter constraints on cosmological parameters.

The Atacama Cosmology Telescope: Cosmology from Galaxy Clusters Detected via the Sunyaev-Zeldovich Effect

We present constraints on cosmological parameters based on a sample of Sunyaev-Zeldovich-selected galaxy clusters detected in a millimeter-wave survey by the Atacama Cosmology Telescope. The cluster sample used in this analysis consists of 9 optically-confirmed high-mass clusters comprising the high-significance end of the total cluster sample identified in 455 square degrees of sky surveyed during 2008 at 148 GHz. We focus on the most massive systems to reduce the degeneracy between unknown cluster astrophysics and cosmology derived from SZ surveys. We describe the scaling relation between cluster mass and SZ signal with a 4-parameter fit. Marginalizing over the values of the parameters in this fit with conservative priors gives sigma_8 = 0.851 +/- 0.115 and w = -1.14 +/- 0.35 for a spatially-flat wCDM cosmological model with WMAP 7-year priors on cosmological parameters. This gives a modest improvement in statistical uncertainty over WMAP 7-year constraints alone. Fixing the scaling relation between cluster mass and SZ signal to a fiducial relation obtained from numerical simulations and calibrated by X-ray observations, we find sigma_8 = 0.821 +/- 0.044 and w = -1.05 +/- 0.20. These results are consistent with constraints from WMAP 7 plus baryon acoustic oscillations plus type Ia supernoava which give sigma_8 = 0.802 +/- 0.038 and w = -0.98 +/- 0.053. A stacking analysis of the clusters in this sample compared to clusters simulated assuming the fiducial model also shows good agreement. These results suggest that, given the sample of clusters used here, both the astrophysics of massive clusters and the cosmological parameters derived from them are broadly consistent with current models.

Radiative Transfer Modeling of Lyman Alpha Emitters. II. New Effects in Galaxy Clustering

We study the clustering properties of z~5.7 Lyman-alpha emitters (LAEs) in a cosmological reionization simulation with a full Lya radiative transfer calculation. Lya radiative transfer substantially modifies the intrinsic Lya emission properties, compared to observed ones, depending on the density and velocity structure environment around the Lya emitting galaxy. This environment-dependent Lya selection introduces new features in LAE clustering, suppressing (enhancing) the line-of-sight (transverse) density fluctuations and giving rise to scale-dependent galaxy bias. In real space, the contours of the three-dimensional two-point correlation function of LAEs appear to be prominently elongated along the line of sight on large scales, an effect that is opposite to and much stronger than the linear redshift-space distortion effect. The projected two-point correlation function is greatly enhanced in amplitude by a factor of up to a few, compared to the case without the environment dependent selection effect. The new features in LAE clustering can be understood with a simple, physically motivated model, where Lya selection depends on matter density, velocity, and their gradients. We discuss the implications and consequences of the effects on galaxy clustering from Lya selection in interpreting clustering measurements and in constraining cosmology and reionization from LAEs.

Cosmological HII Bubble Growth During Reionization

We present general properties of ionized hydrogen (HII) bubbles and their growth based on a state-of-the-art large-scale (100 Mpc/h) cosmological radiative transfer simulation. The simulation resolves all halos with atomic cooling at the relevant redshifts and simultaneously performs radiative transfer and dynamical evolution of structure formation. Our major conclusions include: (1) for significant HII bubbles, the number distribution is peaked at a volume of $sim 0.6 {rm Mpc^{3}/h^{3}}$ at all redshifts. But, at $zle 10$, one large, connected network of bubbles dominates the entire HII volume. (2) HII bubbles are highly non-spherical. (3) The HII regions are highly biased with respect to the underlying matter distribution with the bias decreasing with time. (4) The non-gaussianity of the HII region is small when the universe becomes 50% ionized. The non-gaussianity reaches its maximal near the end of the reionization epoch $zsim 6$. But at all redshifts of interest there is a significant non-gaussianity in the HII field. (5) Population III galaxies may play a significant role in the reionization process. Small bubbles are initially largely produced by Pop III stars. At $zge 10$ even the largest HII bubbles have a balanced ionizing photon contribution from Pop II and Pop III stars, while at $zle 8$ Pop II stars start to dominate the overall ionizing photon production for large bubbles, although Pop III stars continue to make a non-negligible contribution. (6) The relationship between halo number density and bubble size is complicated but a strong correlation is found between halo number density and bubble size for large bubbles.