21 research outputs found
Large-scale periodicity in the distribution of QSO absorption-line systems
The spatial-temporal distribution of absorption-line systems (ALSs) observed
in QSO spectra within the cosmological redshift interval z = 0.0--4.3 is
investigated on the base of our updated catalog of absorption systems. We
consider so called metallic systems including basically lines of heavy
elements. The sample of the data displays regular variations (with amplitudes ~
15 -- 20%) in the z-distribution of ALSs as well as in the eta-distribution,
where eta is a dimensionless line-of-sight comoving distance, relatively to
smoother dependences. The eta-distribution reveals the periodicity with period
Delta eta = 0.036 +/- 0.002, which corresponds to a spatial characteristic
scale (108 +/- 6) h(-1) Mpc or (alternatively) a temporal interval (350 +/- 20)
h(-1) Myr for the LambdaCDM cosmological model. We discuss a possibility of a
spatial interpretation of the results treating the pattern obtained as a trace
of an order imprinted on the galaxy clustering in the early Universe.Comment: AASTeX, 13 pages, with 9 figures, Accepted for publication in
Astrophysics & Space Scienc
Cosmological constraints from galaxy clustering
In this manuscript I review the mathematics and physics that underpins recent
work using the clustering of galaxies to derive cosmological model constraints.
I start by describing the basic concepts, and gradually move on to some of the
complexities involved in analysing galaxy redshift surveys, focusing on the 2dF
Galaxy Redshift Survey (2dFGRS) and the Sloan Digital Sky survey (SDSS).
Difficulties within such an analysis, particularly dealing with redshift space
distortions and galaxy bias are highlighted. I then describe current
observations of the CMB fluctuation power spectrum, and consider the importance
of measurements of the clustering of galaxies in light of recent experiments.
Finally, I provide an example joint analysis of the latest CMB and large-scale
structure data, leading to a set of parameter constraints.Comment: 30 pages, 13 figures. Lecture given at Third Aegean Summer School,
The invisible universe: Dark matter and Dark energ
The Large Scale Structure in the Universe: From Power-Laws to Acoustic Peaks
The most popular tools for analysing the large scale distribution of galaxies
are second-order spatial statistics such as the two-point correlation function
or its Fourier transform, the power spectrum. In this review, we explain how
our knowledge of cosmic structures, encapsulated by these statistical
descriptors, has evolved since their first use when applied on the early galaxy
catalogues to the present generation of wide and deep redshift surveys,
incorporating the most challenging discovery in the study of the galaxy
distribution: the detection of Baryon Acoustic Oscillations.Comment: 20 pages, 12 figures, to appear in "Data Analysis in Cosmology",
Lecture Notes in Physics, 2008, eds. V. J. Martinez, E. Saar, E.
Martinez-Gonzalez, and M.J. Pons-Borderia, Springer-Verla
Support in Managerial Work Groups: Psychological Climate, Role, Feedback, Behaviour and Satisfaction
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Suboptimal Criterion Learning in Static and Dynamic Environments
Humans often make decisions based on uncertain sensory information. Signal detection theory (SDT) describes detection and discrimination decisions as a comparison of stimulus "strength" to a fixed decision criterion. However, recent research suggests that current responses depend on the recent history of stimuli and previous responses, suggesting that the decision criterion is updated trial-by-trial. The mechanisms underpinning criterion setting remain unknown. Here, we examine how observers learn to set a decision criterion in an orientation-discrimination task under both static and dynamic conditions. To investigate mechanisms underlying trial-by-trial criterion placement, we introduce a novel task in which participants explicitly set the criterion, and compare it to a more traditional discrimination task, allowing us to model this explicit indication of criterion dynamics. In each task, stimuli were ellipses with principal orientations drawn from two categories: Gaussian distributions with different means and equal variance. In the covert-criterion task, observers categorized a displayed ellipse. In the overt-criterion task, observers adjusted the orientation of a line that served as the discrimination criterion for a subsequently presented ellipse. We compared performance to the ideal Bayesian learner and several suboptimal models that varied in both computational and memory demands. Under static and dynamic conditions, we found that, in both tasks, observers used suboptimal learning rules. In most conditions, a model in which the recent history of past samples determines a belief about category means fit the data best for most observers and on average. Our results reveal dynamic adjustment of discrimination criterion, even after prolonged training, and indicate how decision criteria are updated over time