A study of digital holographic filters generation. Phase 2: Digital data communication system, volume 1

An empirical study of the performance of the Viterbi decoders in bursty channels was carried out and an improved algebraic decoder for nonsystematic codes was developed. The hybrid algorithm was simulated for the (2,1), k = 7 code on a computer using 20 channels having various error statistics, ranging from pure random error to pure bursty channels. The hybrid system outperformed both the algebraic and the Viterbi decoders in every case, except the 1% random error channel where the Viterbi decoder had one bit less decoding error

Observational evidence for stochastic biasing

We show that the galaxy density in the Las Campanas Redshift Survey (LCRS) cannot be perfectly correlated with the underlying mass distribution since various galaxy subpopulations are not perfectly correlated with each other, even taking shot noise into account. This rules out the hypothesis of simple linear biasing, and suggests that the recently proposed stochastic biasing framework is necessary for modeling actual data.Comment: 4 pages, with 2 figures included. Minor revisions to match accepted ApJL version. Links and color fig at http://www.sns.ias.edu/~max/r_frames.html or from [email protected]

Detecting the Earliest Galaxies Through Two New Sources of 21cm Fluctuations

The first galaxies that formed at a redshift ~20-30 emitted continuum photons with energies between the Lyman-alpha and Lyman limit wavelengths of hydrogen, to which the neutral universe was transparent except at the Lyman-series resonances. As these photons redshifted or scattered into the Lyman-alpha resonance they coupled the spin temperature of the 21cm transition of hydrogen to the gas temperature, allowing it to deviate from the microwave background temperature. We show that the fluctuations in the radiation emitted by the first galaxies produced strong fluctuations in the 21cm flux before the Lyman-alpha coupling became saturated. The fluctuations were caused by biased inhomogeneities in the density of galaxies, along with Poisson fluctuations in the number of galaxies. Observing the power-spectra of these two sources would probe the number density of the earliest galaxies and the typical mass of their host dark matter halos. The enhanced amplitude of the 21cm fluctuations from the era of Lyman-alpha coupling improves considerably the practical prospects for their detection.Comment: 11 pages, 7 figures, ApJ, published. Normalization fixed in top panels of Figures 4-

Star Formation and Stellar Mass Assembly in Dark Matter Halos: From Giants to Dwarfs

The empirical model of Lu et al. 2014 is updated with recent data and used to study galaxy star formation and assembly histories. At $z > 2$, the predicted galaxy stellar mass functions are steep, and a significant amount of star formation is hosted by low-mass haloes that may be missed in current observations. Most of the stars in cluster centrals formed earlier than $z\approx 2$ but have been assembled much later. Milky Way mass galaxies have had on-going star formation without significant mergers since $z\approx 2$, and are thus free of significant (classic) bulges produced by major mergers. In massive clusters, stars bound in galaxies and scattered in the halo form a homogeneous population that is old and with solar metallicity. In contrast, in Milky Way mass systems the two components form two distinct populations, with halo stars being older and poorer in metals by a factor of $\approx 3$. Dwarf galaxies in haloes with $M_{\rm h} < 10^{11}h^{-1}M_{\odot}$ have experienced a star formation burst accompanied by major mergers at $z > 2$, followed by a nearly constant star formation rate after $z = 1$. The early burst leaves a significant old stellar population that is distributed in spheroids.Comment: 17 pages, 17 figure

The clustering of SDSS galaxy groups: mass and color dependence

We use a sample of galaxy groups selected from the SDSS DR 4 with an adaptive halo-based group finder to probe how the clustering strength of groups depends on their masses and colors. In particular, we determine the relative biases of groups of different masses, as well as that of groups with the same mass but with different colors. In agreement with previous studies, we find that more massive groups are more strongly clustered, and the inferred mass dependence of the halo bias is in good agreement with predictions for the $\Lambda$CDM cosmology. Regarding the color dependence, we find that groups with red centrals are more strongly clustered than groups of the same mass but with blue centrals. Similar results are obtained when the color of a group is defined to be the total color of its member galaxies. The color dependence is more prominent in less massive groups and becomes insignificant in groups with masses \gta 10^{14}\msunh. We construct a mock galaxy redshift survey constructed from the large Millenium simulation that is populated with galaxies according to the semi-analytical model of Croton et al. Applying our group finder to this mock survey, and analyzing the mock data in exactly the same way as the true data, we are able to accurately recover the intrinsic mass and color dependencies of the halo bias in the model. This suggests that our group finding algorithm and our method of assigning group masses do not induce spurious mass and/or color dependencies in the group-galaxy correlation function. The semi-analytical model reveals the same color dependence of the halo bias as we find in our group catalogue. In halos with M\sim 10^{12}\msunh, though, the strength of the color dependence is much stronger in the model than in the data.Comment: 16 pages, 14 figures, Accepted for publication in ApJ. In the new version, we add the bias of the shuffled galaxy sample. The errors are estimated according to the covariance matrix of the GGCCF, which is then diagonalize

Multisensory causal inference in the brain

At any given moment, our brain processes multiple inputs from its different sensory modalities (vision, hearing, touch, etc.). In deciphering this array of sensory information, the brain has to solve two problems: (1) which of the inputs originate from the same object and should be integrated and (2) for the sensations originating from the same object, how best to integrate them. Recent behavioural studies suggest that the human brain solves these problems using optimal probabilistic inference, known as Bayesian causal inference. However, how and where the underlying computations are carried out in the brain have remained unknown. By combining neuroimaging-based decoding techniques and computational modelling of behavioural data, a new study now sheds light on how multisensory causal inference maps onto specific brain areas. The results suggest that the complexity of neural computations increases along the visual hierarchy and link specific components of the causal inference process with specific visual and parietal regions