Mapping and simulating systematics due to spatially-varying observing conditions in DES Science Verification data

Spatially-varying depth and characteristics of observing conditions, such as seeing, airmass, or sky background, are major sources of systematic uncertainties in modern galaxy survey analyses, in particular in deep multi-epoch surveys. We present a framework to extract and project these sources of systematics onto the sky, and apply it to the Dark Energy Survey (DES) to map the observing conditions of the Science Verification (SV) data. The resulting distributions and maps of sources of systematics are used in several analyses of DES SV to perform detailed null tests with the data, and also to incorporate systematics in survey simulations. We illustrate the complementarity of these two approaches by comparing the SV data with the BCC-UFig, a synthetic sky catalogue generated by forward-modelling of the DES SV images. We analyse the BCC-UFig simulation to construct galaxy samples mimicking those used in SV galaxy clustering studies. We show that the spatially-varying survey depth imprinted in the observed galaxy densities and the redshift distributions of the SV data are successfully reproduced by the simulation and well-captured by the maps of observing conditions. The combined use of the maps, the SV data and the BCC-UFig simulation allows us to quantify the impact of spatial systematics on $N(z)$, the redshift distributions inferred using photometric redshifts. We conclude that spatial systematics in the SV data are mainly due to seeing fluctuations and are under control in current clustering and weak lensing analyses. The framework presented here is relevant to all multi-epoch surveys, and will be essential for exploiting future surveys such as the Large Synoptic Survey Telescope (LSST), which will require detailed null-tests and realistic end-to-end image simulations to correctly interpret the deep, high-cadence observations of the sky

Mathematical Modeling and Simulation of Ventricular Activation Sequences: Implications for Cardiac Resynchronization Therapy

Next to clinical and experimental research, mathematical modeling plays a crucial role in medicine. Biomedical research takes place on many different levels, from molecules to the whole organism. Due to the complexity of biological systems, the interactions between components are often difficult or impossible to understand without the help of mathematical models. Mathematical models of cardiac electrophysiology have made a tremendous progress since the first numerical ECG simulations in the 1960s. This paper briefly reviews the development of this field and discusses some example cases where models have helped us forward, emphasizing applications that are relevant for the study of heart failure and cardiac resynchronization therapy

CMB lensing tomography with the DES science verification galaxies

We measure the cross-correlation between the galaxy density in the Dark Energy Survey (DES) Science Verification data and the lensing of the cosmic microwave background (CMB) as reconstructed with the Planck satellite and the South Pole Telescope (SPT). When using the DES main galaxy sample over the full redshift range 0.2 2σ) detections in all bins. Comparing to the fiducial Planck cosmology, we find the redshift evolution of the signal matches expectations, although the amplitude is consistently lower than predicted across redshift bins. We test for possible systematics that could affect our result and find no evidence for significant contamination. Finally, we demonstrate how these measurements can be used to constrain the growth of structure across cosmic time. We find the data are fit by a model in which the amplitude of structure in the z < 1.2 universe is 0.73 ± 0.16 times as large as predicted in the Λ cold dark matter Planck cosmology, a 1.7σ deviation

Mechanism of generation of body surface electrocardiographic P-waves in normal, middle, and lower sinus rhythms.

We used comprehensive electrophysiological/anatomical digital computer models of atrial excitation and the human torso to study the mechanisms of generation of body surface P-waves in normal sinus rhythm, and in middle and lower sinus rhythm. Simulated atrial surface isochrone maps for normal sinus rhythm support the validity of the atrial excitation model. The results suggest that the presence of specialized internodal tracts containing fast-conducting fibers is not essential to account for propagation of excitation in apparent preferential directions from the sinoatrial (SA) node to the atrioventricular node. However, in the absence of fast conducting fibers, a slowly conducting segment in the intercaval region is necessary to achieve proper excitation of the interatrial septum. P-wave notches occur in the absence of specialized fast conducting atrial tracts and anisotropies due to fiber orientation. These notches are due to the atrial geometry and the separate contributions of the right atrium, left atrium, and interatrial septum to the P-waves, and become more pronounced as the pacemaker site shifts downward in the SA node. Thus, slight changes in the origin of excitation, which result in subtle changes in the atrial excitation isochrones, produce significant and complex changes in the simulated body surface P-waves.</jats:p

Microfluidic mixer designed for performing single-molecule kinetics with confocal detection on timescales from milliseconds to minutes

Microfluidic mixing in combination with single-molecule spectroscopy allows the investigation of complex biomolecular processes under non-equilibrium conditions. Here we present a protocol for building, installing and operating microfluidic mixing devices optimized for this purpose. The mixer is fabricated by replica molding with polydimethylsiloxane (PDMS), which allows the production of large numbers of devices at a low cost using a single microfabricated silicon mold. The design is based on hydrodynamic focusing combined with diffusive mixing and allows single-molecule kinetics to be recorded over five orders of magnitude in time, from 1 ms to ∼100 s. Owing to microfabricated particle filters incorporated in the inlet channels, the devices provide stable flow for many hours to days without channel blockage, which allows reliable collection of high-quality data. Modular design enables rapid exchange of samples and mixing devices, which are mounted in a specifically designed holder for use with a confocal microscopy detection system. Integrated Peltier elements provide temperature control from 4 to 37 °C. The protocol includes the fabrication of a silicon master, production of the microfluidic devices, instrumentation setup and data acquisition. Once a silicon master is available, devices can be produced and experiments started within ∼1 d of preparation. We demonstrate the performance of the system with single-molecule Förster resonance energy transfer (FRET) measurements of kinetics of protein folding and conformational changes. The dead time of 1 ms, as predicted from finite element calculations, was confirmed by the measurements

BAO from Angular Clustering: Optimization and Mitigation of Theoretical Systematics

International audienceWe study the methodology and potential theoretical systematics of measuring baryon acoustic oscillations (BAO) using the angular correlation functions in tomographic bins. We calibrate and optimize the pipeline for the Dark Energy Survey Year 1 data set using 1800 mocks. We compare the BAO fitting results obtained with three estimators: the Maximum Likelihood Estimator (MLE), Profile Likelihood, and Markov Chain Monte Carlo. The fit results from the MLE are the least biased and their derived 1σ error bar are closest to the Gaussian distribution value after removing the extreme mocks with non-detected BAO signal. We show that incorrect assumptions in constructing the template, such as mismatches from the cosmology of the mocks or the underlying photo-|$z$| errors, can lead to BAO angular shifts. We find that MLE is the method that best traces this systematic biases, allowing to recover the true angular distance values. In a real survey analysis, it may happen that the final data sample properties are slightly different from those of the mock catalogue. We show that the effect on the mock covariance due to the sample differences can be corrected with the help of the Gaussian covariance matrix or more effectively using the eigenmode expansion of the mock covariance. In the eigenmode expansion, the eigenmodes are provided by some proxy covariance matrix. The eigenmode expansion is significantly less susceptible to statistical fluctuations relative to the direct measurements of the covariance matrix because of the number of free parameters is substantially reduced