Non-Gaussianity from extragalactic point-sources

The population of compact extragalactic sources contribute to the non-Gaussianity at Cosmic Microwave Background frequencies. We study their non-Gaussianity using publicly available full-sky simulations. We introduce a parametrisation to visualise efficiently the bispectrum and we describe the scale and frequency dependences of the bispectrum of radio and IR point-sources. We show that the bispectrum is well fitted by an analytical prescription. We find that the clustering of IR sources enhances their non-Gaussianity by several orders of magnitude, and that their bispectrum peaks in the squeezed triangles. Examining the impact of these sources on primordial non-Gaussianity estimation, we find that radio sources yield an important positive bias to local fNL at low frequencies but this bias is efficiently reduced by masking detectable sources. IR sources produce a negative bias at high frequencies, which is not dimmed by the masking, as their clustering is dominated by faint sources.Comment: 4pages, 2 figures, 2 tables. Contribution to the proceedings of the International Conference on Gravitation and Cosmology, Goa, December 201

Characterization of the non-Gaussianity of radio and IR point sources at CMB frequencies

This study, using publicly available simulations, focuses on the characterization of the non-Gaussianity produced by radio point sources and by infrared (IR) sources in the frequency range of the cosmic microwave background from 30 to 350 GHz. We propose a simple prescription to infer the angular bispectrum from the power spectrum of point sources considering independent populations of sources, with or without clustering. We test the accuracy of our prediction using publicly available all-sky simulations of radio and IR sources and find very good agreement. We further characterize the configuration dependence and the frequency behaviour of the IR and radio bispectra. We show that the IR angular bispectrum peaks for squeezed triangles and that the clustering of IR sources enhances the bispectrum values by several orders of magnitude at scales ℓ∼ 100. At 150 GHz the bispectrum of IR sources starts to dominate that of radio sources on large angular scales, and it dominates the whole multipole range at 350 GHz. Finally, we compute the bias on fNL induced by radio and IR sources. We show that the positive bias induced by radio sources is significantly reduced by masking the sources. We also show, for the first time, that the form of the IR bispectrum mimics a primordial ‘local' bispectrum fNL. The IR sources produce a negative bias which becomes important for Planck-like resolution and at high frequencies (ΔfNL∼−6 at 277 GHz and ΔfNL∼−60-70 at 350 GHz). Most of the signal being due to the clustering of faint IR sources, the bias is not reduced by masking sources above a flux limit and may, in some cases, even be increased due to the reduction of the shot-noise ter

Feigenbaum graphs: a complex network perspective of chaos

The recently formulated theory of horizontal visibility graphs transforms time series into graphs and allows the possibility of studying dynamical systems through the characterization of their associated networks. This method leads to a natural graph-theoretical description of nonlinear systems with qualities in the spirit of symbolic dynamics. We support our claim via the case study of the period-doubling and band-splitting attractor cascades that characterize unimodal maps. We provide a universal analytical description of this classic scenario in terms of the horizontal visibility graphs associated with the dynamics within the attractors, that we call Feigenbaum graphs, independent of map nonlinearity or other particulars. We derive exact results for their degree distribution and related quantities, recast them in the context of the renormalization group and find that its fixed points coincide with those of network entropy optimization. Furthermore, we show that the network entropy mimics the Lyapunov exponent of the map independently of its sign, hinting at a Pesin-like relation equally valid out of chaos.Comment: Published in PLoS ONE (Sep 2011

Description of stochastic and chaotic series using visibility graphs

Nonlinear time series analysis is an active field of research that studies the structure of complex signals in order to derive information of the process that generated those series, for understanding, modeling and forecasting purposes. In the last years, some methods mapping time series to network representations have been proposed. The purpose is to investigate on the properties of the series through graph theoretical tools recently developed in the core of the celebrated complex network theory. Among some other methods, the so-called visibility algorithm has received much attention, since it has been shown that series correlations are captured by the algorithm and translated in the associated graph, opening the possibility of building fruitful connections between time series analysis, nonlinear dynamics, and graph theory. Here we use the horizontal visibility algorithm to characterize and distinguish between correlated stochastic, uncorrelated and chaotic processes. We show that in every case the series maps into a graph with exponential degree distribution P (k) ~ exp(-{\lambda}k), where the value of {\lambda} characterizes the specific process. The frontier between chaotic and correlated stochastic processes, {\lambda} = ln(3/2), can be calculated exactly, and some other analytical developments confirm the results provided by extensive numerical simulations and (short) experimental time series

Impact of survey geometry and super-sample covariance on future photometric galaxy surveys

Photometric galaxy surveys probe the late-time Universe where the density field is highly non-Gaussian. A consequence is the emergence of the super-sample covariance (SSC), a non-Gaussian covariance term that is sensitive to fluctuations on scales larger than the survey window. In this work, we study the impact of the survey geometry on the SSC and, subsequently, on cosmological parameter inference. We devise a fast SSC approximation that accounts for the survey geometry and compare its performance to the common approximation of rescaling the results by the fraction of the sky covered by the survey, fSKY, dubbed ‘full-sky approximation’. To gauge the impact of our new SSC recipe, that we call ‘partial-sky’, we perform Fisher forecasts on the parameters of the (w0, wa)-CDM model in a 3 × 2 point analysis, varying the survey area, the geometry of the mask, and the galaxy distribution inside our redshift bins. The differences in the marginalised forecast errors –with the full-sky approximation performing poorly for small survey areas but excellently for stage-IV-like areas– are found to be absorbed by the marginalisation on galaxy bias nuisance parameters. For large survey areas, the unmarginalised errors are underestimated by about 10% for all probes considered. This is a hint that, even for stage-IV-like surveys, the partial-sky method introduced in this work will be necessary if tight priors are applied on these nuisance parameters. We make the partial-sky method public with a new release of the public code PySSC

Quasiperiodic graphs: structural design, scaling and entropic properties

A novel class of graphs, here named quasiperiodic, are constructed via application of the Horizontal Visibility algorithm to the time series generated along the quasiperiodic route to chaos. We show how the hierarchy of mode-locked regions represented by the Farey tree is inherited by their associated graphs. We are able to establish, via Renormalization Group (RG) theory, the architecture of the quasiperiodic graphs produced by irrational winding numbers with pure periodic continued fraction. And finally, we demonstrate that the RG fixed-point degree distributions are recovered via optimization of a suitably defined graph entropy

Development of a tomato spotted wilt virus (TSWV) risk evaluation methology for a processing tomato region

A risk map for the Tomato spotted wilt virus (TSWV) was elaborated for the main Portuguese processing tomato producing region, the “Ribatejo e Península de Setúbal” region, where periodically this virus causes severe losses. Forty nine tomato fields were monitored. Risk factors for TSWV infection were identified and quantified according to their relative importance in TSWV incidence. The risk factors considered for each field were: (1) presence of TSWV in tomato plants; (2) presence of TSWV in weeds which are hosts of TSWV vectors; (3) presence of TSWV vector thrips; (4) presence of TSWV host crops previously (in the two years before), namely, tomato, potato and sweet pepper; and (5) presence of greenhouses, urban areas or TSWV host crops next to the field (up to about 100m from its borders). A risk estimator was calculated for each field. Among the thrips (Thysanoptera) identified, belonging to 11 genera, four vector thrips species were detected: Frankliniella occidentalis (Pergande) and Thrips tabaci Lindman, the two most abundant ones, and F. intonsa (Trybom) and F. schultzei (Trybom). Blue sticky traps placed up to about 75 cm above the crop canopy caught F. occidentalis and T. tabaci more efficiently than the beating technique. The weeds Datura stramonium L., Arctotheca calendula (L.), and Conyza bonariensis (L.) were identified as TSWV winter repositories. This study proposes a methodology to be used by field technicians for the annual evaluation of TSWV risk at a regional level, for an improved planning of processing tomato crop in the following season