CMB Power Spectrum Estimation via Hierarchical Decomposition

We have developed a fast, accurate and generally applicable method for inferring the power spectrum and its uncertainties from maps of the cosmic microwave background (CMB) in the presence of inhomogeneous and correlated noise. For maps with 10 to 100 thousand pixels, we apply an exact power spectrum estimation algorithm to submaps of the data at various resolutions, and then combine the results in an optimal manner. To analyze larger maps efficiently one must resort to sub-optimal combinations in which cross-map power spectrum error correlations are only calculated approximately. We expect such approximations to work well in general, and in particular for the megapixel maps to come from the next generation of satellite missions.Comment: 10 pages, 5 figures, to be submitted to Phys. Rev.

Modeling the Radio Background from the First Black Holes at Cosmic Dawn: Implications for the 21 cm Absorption Amplitude

We estimate the 21 cm Radio Background from accretion onto the first intermediate-mass Black Holes between $z\approx 30$ and $z\approx 16$. Combining potentially optimistic, but plausible, scenarios for black hole formation and growth with empirical correlations between luminosity and radio-emission observed in low-redshift active galactic nuclei, we find that a model of black holes forming in molecular cooling halos is able to produce a 21 cm background that exceeds the Cosmic Microwave Background (CMB) at $z \approx 17$ though models involving larger halo masses are not entirely excluded. Such a background could explain the surprisingly large amplitude of the 21 cm absorption feature recently reported by the EDGES collaboration. Such black holes would also produce significant X-ray emission and contribute to the $0.5-2$ keV soft X-ray background at the level of $\approx 10^{-13}-10^{-12}$ erg sec$^{-1}$ cm$^{-2}$ deg$^{-2}$, consistent with existing constraints. In order to avoid heating the IGM over the EDGES trough, these black holes would need to be obscured by Hydrogen column depths of $N_\text{H} \sim 5 \times 10^{23} \text{cm}^{-2}$. Such black holes would avoid violating contraints on the CMB optical depth from Planck if their UV photon escape fractions were below $f_{\text{esc}} \lesssim 0.1$, which would be a natural result of $N_\text{H} \sim 5 \times 10^{23} \text{cm}^{-2}$ imposed by an unheated IGM.Comment: 10 pages, 7 figures, accepted to ApJ, replacement to match submitted versio

The imprints of primordial non-gaussianities on large-scale structure: scale dependent bias and abundance of virialized objects

We study the effect of primordial nongaussianity on large-scale structure, focusing upon the most massive virialized objects. Using analytic arguments and N-body simulations, we calculate the mass function and clustering of dark matter halos across a range of redshifts and levels of nongaussianity. We propose a simple fitting function for the mass function valid across the entire range of our simulations. We find pronounced effects of nongaussianity on the clustering of dark matter halos, leading to strongly scale-dependent bias. This suggests that the large-scale clustering of rare objects may provide a sensitive probe of primordial nongaussianity. We very roughly estimate that upcoming surveys can constrain nongaussianity at the level |fNL| <~ 10, competitive with forecasted constraints from the microwave background.Comment: 16 pages, color figures, revtex4. v2: added references and an equation. submitted to PRD. v3: simplified derivation, additional reference

Limitation of energy deposition in classical N body dynamics

Energy transfers in collisions between classical clusters are studied with Classical N Body Dynamics calculations for different entrance channels. It is shown that the energy per particle transferred to thermalised classical clusters does not exceed the energy of the least bound particle in the cluster in its ``ground state''. This limitation is observed during the whole time of the collision, except for the heaviest system.Comment: 13 pages, 15 figures, 1 tabl

Detection of Gravitational Lensing in the Cosmic Microwave Background

Gravitational lensing of the cosmic microwave background (CMB), a long-standing prediction of the standard cosmolgical model, is ultimately expected to be an important source of cosmological information, but first detection has not been achieved to date. We report a 3.4 sigma detection, by applying quadratic estimator techniques to all sky maps from the Wilkinson Microwave Anisotropy Probe (WMAP) satellite, and correlating the result with radio galaxy counts from the NRAO VLA Sky Survey (NVSS). We present our methodology including a detailed discussion of potential contaminants. Our error estimates include systematic uncertainties from density gradients in NVSS, beam effects in WMAP, Galactic microwave foregrounds, resolved and unresolved CMB point sources, and the thermal Sunyaev-Zeldovich effect.Comment: 27 pages, 20 figure

Infantile de novo primary antiphospholipid syndrome revealed by neonatal stroke

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Agroecology as a science, a movement and a practice. A review

Agroecology involves various approaches to solve actual challenges of agricultural production. Though agroecology initially dealt primarily with crop production and protection aspects, in recent decades new dimensions such as environmental, social, economic, ethical and development issues are becoming relevant. Today, the term ‘agroecology’ means either a scientific discipline, agricultural practice, or political or social movement. Here we study the different meanings of agroecology. For that we analyse the historical development of agroecology. We present examples from USA, Brazil, Germany, and France. We study and discuss the evolution of different meanings agroecology. The use of the term agroecology can be traced back to the 1930s. Until the 1960s agroecology referred only as a purely scientific discipline. Then, different branches of agroecology developed. Following environmental movements in the 1960s that went against industrial agriculture, agroecology evolved and fostered agroecological movements in the 1990s. Agroecology as an agricultural practice emerged in the 1980s, and was often intertwined with movements. Further, the scales and dimensions of agroecological investigations changed over the past 80 years from the plot and field scales to the farm and agroecosystem scales. Actually three approaches persist: (1) investigations at plot and field scales, (2) investigations at the agroecosystem and farm scales, and (3) investigations covering the whole food system. These different approaches of agroecological science can be explained by the history of nations. In France, agroecology was mainly understood as a farming practice and to certain extent as a movement, whereas the corresponding scientific discipline was agronomy. In Germany, agroecology has a long tradition as a scientific discipline. In the USA and in Brazil all three interpretations of agroecology occur, albeit with a predominance of agroecology as a science in the USA and a stronger emphasis on movement and agricultural practice in Brazil. These varied meanings of the term agroecology cause confusion among scientists and the public, and we recommend that those who publish using this term be explicit in their interpretation

Agroecology as a science, a movement and a practice. A review

Agroecology involves various approaches to solve actual challenges of agricultural production. Though agroecology initially dealt primarily with crop production and protection aspects, in recent decades new dimensions such as environmental, social, economic, ethical and development issues are becoming relevant. Today, the term ‘agroecology’ means either a scientific discipline, agricultural practice, or political or social movement. Here we study the different meanings of agroecology. For that we analyse the historical development of agroecology. We present examples from USA, Brazil, Germany, and France. We study and discuss the evolution of different meanings agroecology. The use of the term agroecology can be traced back to the 1930s. Until the 1960s agroecology referred only as a purely scientific discipline. Then, different branches of agroecology developed. Following environmental movements in the 1960s that went against industrial agriculture, agroecology evolved and fostered agroecological movements in the 1990s. Agroecology as an agricultural practice emerged in the 1980s, and was often intertwined with movements. Further, the scales and dimensions of agroecological investigations changed over the past 80 years from the plot and field scales to the farm and agroecosystem scales. Actually three approaches persist: (1) investigations at plot and field scales, (2) investigations at the agroecosystem and farm scales, and (3) investigations covering the whole food system. These different approaches of agroecological science can be explained by the history of nations. In France, agroecology was mainly understood as a farming practice and to certain extent as a movement, whereas the corresponding scientific discipline was agronomy. In Germany, agroecology has a long tradition as a scientific discipline. In the USA and in Brazil all three interpretations of agroecology occur, albeit with a predominance of agroecology as a science in the USA and a stronger emphasis on movement and agricultural practice in Brazil. These varied meanings of the term agroecology cause confusion among scientists and the public, and we recommend that those who publish using this term be explicit in their interpretation

Ophthalmic statistics note 8: missing data - exploring the unknown

Medical research is conducted to answer uncertainties and to identify effective treatments for patients. Different questions are best addressed by different types of study design—but the randomised, controlled clinical trial is typically viewed as the gold standard, providing a very high level of evidence, when examining efficacy. While clinical trial methodology has advanced considerably with clear guidance provided as to how to avoid sources of bias, even the most robustly designed study can succumb to missing data. In this statistics note, we discuss strategies for dealing with missing data but what we hope emerges is a very clear message that there is no ideal solution to missing data and prevention is the best strategy