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Register a new userRecent Advances in Large-Scale Structure and Galaxy Formation Studies
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L. Guzzo
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I review at the non-specialist level recent progress in the study of the large-scale structure of the Universe, covering the following areas: (1) Results from recently completed or ongoing redshift surveys of galaxies and X-ray clusters; (2) Measurements of the power spectrum of fluctuations approaching Gpc scales; (3) Redshift-space distortions and their cosmological use; (4) Structure at high redshifts and its connection to galaxy formation.
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There is a storied scientific history in the role of mechanical instruments for the measurement of fundamental physical interactions. Among these include the detection of magnetic torques via a displacement of a compliant mechanical sensor as a result of angular momentum transfer. Modern nanofabrication methods have enabled the coupling of mechanical structures to single, miniature magnetic specimens. This has allowed for strikingly sensitive detection of magnetic hysteresis and other quasi-static effects, as well as spin resonances, in materials confined to nanoscale geometries. The extraordinary sensitivities achieved in mechanical transduction through recent breakthroughs in cavity optomechanics, where a high-finesse optical cavity is used for readout of motion, are now being harnessed for torque magnetometry. In this article, we review the recent progress in mechanical detection of magnetic torques, highlight current applications, and speculate on possible future developments in the technology and science. Guidelines for designing and implementing the measurements are also included.
This paper serves as a survey of recent advances in large margin training and its theoretical foundations, mostly for (nonlinear) deep neural networks (DNNs) that are probably the most prominent machine learning models for large-scale data in the community over the past decade. We generalize the formulation of classification margins from classical research to latest DNNs, summarize theoretical connections between the margin, network generalization, and robustness, and introduce recent efforts in enlarging the margins for DNNs comprehensively. Since the viewpoint of different methods is discrepant, we categorize them into groups for ease of comparison and discussion in the paper. Hopefully, our discussions and overview inspire new research work in the community that aim to improve the performance of DNNs, and we also point to directions where the large margin principle can be verified to provide theoretical evidence why certain regularizations for DNNs function well in practice. We managed to shorten the paper such that the crucial spirit of large margin learning and related methods are better emphasized.
I review the status of large-scale structure studies based on redshift surveys of galaxies and clusters of galaxies. In particular, I compare recent results on the power spectrum and two-point correlation correlation function from the 2dF and REFLEX surveys, highlighting the advantage of X-ray clusters in the comparison to cosmological models, given their easy-to-understand mass selection function. Unlike for galaxies, this allows the overall normalization of the power spectrum to be measured directly from the data, providing an extra constraint on the models. In the context of CDM models, both the shape and amplitude of the REFLEX P(k) require, consistently, a low value for the mean matter density $Omega_M$. This shape is virtually indistinguishable from that of the galaxy power spectrum measured by the 2dF survey, simply multiplied by a constant cluster-galaxy bias factor. This consistency is remarkable for data sets which use different tracers and are very different in terms of selection function and observational biases. Similarly, the knowledge of the power spectrum normalization yields naturally a value $bsimeq 1$ for the bias parameter of $b_J$-selected (as in 2dF) galaxies, also in agreement with independent estimates using higher-order clustering and CMB data. In the final part, I briefly describe the measurements of the matter density parameter from redshift space distortions in galaxy surveys, and show evidence for similar streaming motions of clusters in the REFLEX redshift-space correlation function $xi(r_p,pi)$. With no exception, this wealth of independent clustering measurements point in a remarkably consistent way towards a low-density CDM Universe with $Omega_Msimeq 0.3$.
The Large Scale Structure (LSS) in the galaxy distribution is investigated using the Sloan Digital Sky Survey Early Data Release (SDSS EDR). Using the Minimal Spanning Tree technique we have extracted sets of filaments, of wall-like structures, of galaxy groups, and of rich clusters from this unique sample. The physical properties of these structures were then measured and compared with the expectations from Zeldovich theory. The measured characteristics of galaxy walls were found to be consistent with those for a spatially flat $Lambda$CDM cosmological model with $Omega_mapprox$ 0.3 and $Omega_Lambda approx$ 0.7, and for Gaussian initial perturbations with a Harrison -- Zeldovich power spectrum. Furthermore, we found that the mass functions of groups and of unrelaxed structure elements generally fit well with the expectations from Zeldovich theory, although there was some discrepancy for lower mass groups which may be due to incompleteness in the selected sample of groups. We also note that both groups and rich clusters tend to prefer the environments of walls, which tend to be of higher density, rather than the environments of filaments, which tend to be of lower density. Finally, we note evidence of systematic differences in the properties of the LSS between the Northern Galactic Cap stripe and the Southern Galactic Cap stripe -- in particular, in the physical properties of the walls, their spatial distribution, and the relative numbers of clusters embedded in walls. Because the mean separation of walls is $approx$ 60 -- 70$h^{-1}$ Mpc, each stripe only intersects a few tens of walls. Thus, small number statistics and cosmic variance are the likely drivers of these systematic differences.
With the commissioning of the LHC expected in 2009, and the LHC upgrades expected in 2012, ATLAS and CMS are planning for detector upgrades for their innermost layers requiring radiation hard technologies. Chemical Vapor Deposition (CVD) diamond has been used extensively in beam conditions monitors as the innermost detectors in the highest radiation areas of BaBar, Belle and CDF and is now planned for all LHC experiments. This material is now being considered as an alternate sensor for use very close to the interaction region of the super LHC where the most extreme radiation conditions will exist. Recently the RD42 collaboration constructed, irradiated and tested polycrystalline and single-crystal chemical vapor deposition diamond sensors to the highest fluences available. We present beam test results of chemical vapor deposition diamond up to fluences of 1.8 x 10^16 protons/cm^2 showing that both polycrystalline and single-crystal chemical vapor deposition diamonds follow a single damage curve allowing one to extrapolate their performance as a function of dose.
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