No Arabic abstract
Oscillations in the baryon-photon fluid prior to recombination imprint different signatures on the power spectrum and correlation function of matter fluctuations. The measurement of these features using galaxy surveys has been proposed as means to determine the equation of state of the dark energy. The accuracy required to achieve competitive constraints demands an extremely good understanding of systematic effects which change the baryonic acoustic oscillation (BAO) imprint. We use 50 very large volume N-body simulations to investigate the BAO signature in the two-point correlation function. The location of the BAO bump does not correspond to the sound horizon scale at the level of accuracy required by future measurements, even before any dynamical or statistical effects are considered. Careful modelling of the correlation function is therefore required to extract the cosmological information encoded on large scales. We find that the correlation function is less affected by scale dependent effects than the power spectrum. We show that a model for the correlation function proposed by Crocce & Scoccimarro (2008), based on renormalised perturbation theory, gives an essentially unbiased measurement of the dark energy equation of state. This means that information from the large scale shape of the correlation function, in addition to the form of the BAO peak, can be used to provide robust constraints on cosmological parameters. The correlation function therefore provides a better constraint on the distance scale (~50% smaller errors with no systematic bias) than the more conservative approach required when using the power spectrum (i.e. which requires amplitude and long wavelength shape information to be discarded).
In this article we study the problem of document image representation based on visual features. We propose a comprehensive experimental study that compares three types of visual document image representations: (1) traditional so-called shallow features, such as the RunLength and the Fisher-Vector descriptors, (2) deep features based on Convolutional Neural Networks, and (3) features extracted from hybrid architectures that take inspiration from the two previous ones. We evaluate these features in several tasks (i.e. classification, clustering, and retrieval) and in different setups (e.g. domain transfer) using several public and in-house datasets. Our results show that deep features generally outperform other types of features when there is no domain shift and the new task is closely related to the one used to train the model. However, when a large domain or task shift is present, the Fisher-Vector shallow features generalize better and often obtain the best results.
We assess the detectability of baryonic acoustic oscillations (BAO) in the power spectrum of galaxies using ultra large volume N-body simulations of the hierarchical clustering of dark matter and semi-analytical modelling of galaxy formation. A step-by-step illustration is given of the various effects (nonlinear fluctuation growth, peculiar motions, nonlinear and scale dependent bias) which systematically change the form of the galaxy power spectrum on large scales from the simple prediction of linear perturbation theory. Using a new method to extract the scale of the oscillations, we nevertheless find that the BAO approach gives an unbiased estimate of the sound horizon scale. Sampling variance remains the dominant source of error despite the huge volume of our simulation box ($=2.41 h^{-3}{rm Gpc}^{3}$). We use our results to forecast the accuracy with which forthcoming surveys will be able to measure the sound horizon scale, $s$, and, hence constrain the dark energy equation of state parameter, $w$ (with simplifying assumptions and without marginalizing over the other cosmological parameters). Pan-STARRS could potentially yield a measurement with an accuracy of $Delta s/s = 0.5-0.7 % $ (corresponding to $Delta w approx 2-3% $), which is competitive with the proposed WFMOS survey ($Delta s/s = 1% $ $Delta w approx 4 % $). Achieving $Delta w le 1% $ using BAO alone is beyond any currently commissioned project and will require an all-sky spectroscopic survey, such as would be undertaken by the SPACE mission concept under proposal to ESA.
Baryonic acoustic oscillations (BAOs) modulate the density ratio of baryons to dark matter across large regions of the Universe. We show that the associated variation in the mass-to-light ratio of galaxies should generate an oscillatory, scale-dependent bias of galaxies relative to the underlying distribution of dark matter. A measurement of this effect would calibrate the dependence of the characteristic mass-to-light ratio of galaxies on the baryon mass fraction in their large scale environment. This bias, though, is unlikely to significantly affect measurements of BAO peak positions.
We show that it is possible to build effective matter density power spectra in tomographic cosmic shear observations that exhibit the Baryonic Acoustic Oscillations (BAO) features once a nulling transformation has been applied to the data. The precision with which the amplitude and position of these features can be reconstructed is quantified in terms of sky coverage, intrinsic shape noise, median source redshift and number density of sources. BAO detection in Euclid or LSST like wide surveys will be possible with a modest signal-to-noise ratio. It would improve dramatically for slightly deeper surveys.
(This paper was written in November 2011 and never published. It is posted on arXiv.org in its original form in June 2016). Many recent object recognition systems have proposed using a two phase training procedure to learn sparse convolutional feature hierarchies: unsupervised pre-training followed by supervised fine-tuning. Recent results suggest that these methods provide little improvement over purely supervised systems when the appropriate nonlinearities are included. This paper presents an empirical exploration of the space of learning procedures for sparse convolutional networks to assess which method produces the best performance. In our study, we introduce an augmentation of the Predictive Sparse Decomposition method that includes a discriminative term (DPSD). We also introduce a new single phase supervised learning procedure that places an L1 penalty on the output state of each layer of the network. This forces the network to produce sparse codes without the expensive pre-training phase. Using DPSD with a new, complex predictor that incorporates lateral inhibition, combined with multi-scale feature pooling, and supervised refinement, the system achieves a 70.6% recognition rate on Caltech-101. With the addition of convolutional training, a 77% recognition was obtained on the CIfAR-10 dataset.