Improving reconstruction of the baryon acoustic peak : the effect of local environment

Precise measurements of the baryon acoustic oscillation (BAO) scale as a standard ruler in the clustering pattern of large-scale structure is a central goal of current and future galaxy surveys. The BAO peak may be sharpened using the technique of density-field reconstruction, in which the bulk displacements of galaxies are estimated using a Zel'dovitch approximation. We use numerical simulations to demonstrate how the accuracy of this approximation depends strongly on local environment, and how this information may be used to construct an improved BAO measurement through environmental re-weighting and using higher-order perturbation theory. We outline further applications of the displacement field for testing cosmological models.Comment: 13 pages, 14 figure

Testing spherical evolution for modelling void abundances

We compare analytical predictions of void volume functions to those measured from N-body simulations, detecting voids with the zobov void finder. We push to very small, non-linear voids, below few Mpc radius, by considering the unsampled dark matter density field. We also study the case where voids are identified using haloes. We develop analytical formula for the void abundance of both the excursion set approach and the peaks formalism. These formulas are valid for random walks smoothed with a top-hat filter in real space, with a large class of realistic barrier models. We test the extent to which the spherical evolution approximation, which forms the basis of the analytical predictions, models the highly aspherical voids that occur in the cosmic web, and are found by a watershed-based algorithm such as zobov. We show that the volume function returned by zobov is quite sensitive to the choice of treatment of subvoids, a fact that has not been appreciated previously. For reasonable choices of subvoid exclusion, we find that the Lagrangian density δv of the zobov voids - which is predicted to be a constant δv≈−2.7 in the spherical evolution model - is different from the predicted value, showing substantial scatter and scale dependence. This result applies to voids identified at z=0 with effective radius between 1 and 10 h−1 Mpc. Our analytical approximations are flexible enough to give a good description of the resulting volume function; however, this happens for choices of parameter values that are different from those suggested by the spherical evolution assumption. We conclude that analytical models for voids must move away from the spherical approximation in order to be applied successfully to observations, and we discuss some possible ways forwar

Natural Language Processing for Financial Regulation

This article provides an understanding of Natural Language Processing techniques in the framework of financial regulation, more specifically in order to perform semantic matching search between rules and policy when no dataset is available for supervised learning. We outline how to outperform simple pre-trained sentences-transformer models using freely available resources and explain the mathematical concepts behind the key building blocks of Natural Language Processing.Comment: 20 pages, 3 figure

Modeling the environmental dependence of the growth rate of cosmic structure

International audienceThe growth rate of cosmic structure is a powerful cosmological probe for extracting information on the gravitational interactions and dark energy. In the late-time Universe, the growth rate becomes nonlinear and is usually probed by measuring the two-point statistics of galaxy clustering in redshift space up to a limited scale, retaining the constraint on the linear growth rate f. In this paper, we present an alternative method to analyze the growth of structure in terms of local densities, i.e., f(Δ). Using N-body simulations, we measure the function of f(Δ) and show that structure grows faster in high-density regions and slower in low-density regions. We demonstrate that f(Δ) can be modeled using a log-normal Monte Carlo random walk approach, which provides a means to extract cosmological information from f(Δ). We discuss prospects for applying this approach to galaxy surveys

New constraints on the linear growth rate using cosmic voids in the SDSS DR12 datasets

International audienceWe present a new analysis of the inferred growth rate of cosmic structure measured around voids, using the LOWZ and the CMASS samples in the 12th data release (DR12) of SDSS. Using a simple multipole analysis we recover a value consistent with ΛCDM for the inferred linear growth rate normalized by the linear bias: the β parameter. We find β=0.33±0.11 for the LOWZ sample and β=0.36±0.05 for the CMASS sample. This work demonstrates that we can expect redshift-space distortions around voids to provide unbiased and accurate constraints on the growth rate, complementary to galaxy clustering, using simple linear modeling

A Unified Graph Clustering Framework for Complex Systems Modeling

Networks are pervasive for complex systems modeling, from biology tolanguage or social sciences, ecosystems or computer science. Detecting com-munities in networks is among the main methods to reveal meaningful struc-tural patterns for the understanding of those systems. Although dozens ofclustering methods have been proposed so far, sometimes including parame-ters such as resolution or scaling, there is no unified framework for selectingthe method best suited to a research objective. After more than 20 years ofresearch, scientists still justify their methodological choice based on ad-hoccomparisons with ‘ground-truth’ or synthetic networks, making it challengingto perform comparative study between those methods. This paper proposesa unified framework, based on easy-to-understand measures, that enables theselection of appropriate clustering methods according to the situation. If re-quired, it can also be used to fine-tune their parameters by interpreting themas description scale parameters. We demonstrate that a new family of algo-rithms inspired by our approach outperforms a set of state-of-the-art com-munity detection algorithms, by comparing them on a benchmark dataset.We believe our approach has the potential to provide a fresh start and a solidfoundation for the development and evaluation of clustering methods acrossa wide range of disciplines