Jet properties from di-hadron correlations in p+p collisions at s**(1/2) = 200-GeV

An analysis of high pT hadron spectra associated with high pT $\pi^0$ particles in p+p collisions at s**(1/2) = 200-GeV is presented. The shape of the azimuthal angular correlation is used to determine the value of partonic intrinsic momentum \sqrt{\left} = 2.68 \pm 0.07(\rm stat) \pm 0.34(\rm sys) GeV/c. The effect of kT-smearing of inclusive $\pi^0$ cross section is discussed.Comment: To appear in the proceedings of 2nd International Conference on Hard and Electromagnetic Probes of High-Energy Nuclear Collisions (Hard Probes 2006), Asilomar, Pacific Grove, California, 9-16 Jun 200

Materialisierte views in verteilten key-value stores

Distributed key-value stores have become the solution of choice for warehousing large volumes of data. However, their architecture is not suitable for real-time analytics. To achieve the required velocity, materialized views can be used to provide summarized data for fast access. The main challenge then, is the incremental, consistent maintenance of views at large scale. Thus, we introduce our View Maintenance System (VMS) to maintain SQL queries in a data-intensive real-time scenario.Verteilte key-value stores sind ein Typ moderner Datenbanken um große Mengen an Daten zu verarbeiten. Trotzdem erlaubt ihre Architektur keine analytischen Abfragen in Echtzeit. Materialisierte Views können diesen Nachteil ausgleichen, indem sie schnellen Zuriff auf Ergebnisse ermöglichen. Die Herausforderung ist dann, das inkrementelle und konsistente Aktualisieren der Views. Daher präsentieren wir unser View Maintenance System (VMS), das datenintensive SQL Abfragen in Echtzeit berechnet

Digital soil mapping of copper in Sweden: Using the prediction and uncertainty as decision support in crop micronutrient management

Digital soil mapping (DSM) of topsoil copper (Cu) concentrations and prediction intervals covering 90% of agricultural land in Sweden was performed, in order to identify areas at risk of Cu deficiency. A total of 12,527 soil samples were used to calibrate the DSM model, using airborne gamma radiation data, climate data, topographical data and soil texture class data. Among the samples included, 11,093 had no laboratory-analysed Cu concentrations, so their Cu concentrations were predicted using portable X-ray fluorescence (PXRF) measurements. Cross-validation of the PXRF model resulted in Nash-Sutcliffe model efficiency coefficient (E) of 0.66 and mean absolute error (MAE) of 3.3 mg kg−1. Cross-validation of the DSM model showed somewhat lower performance (E = 0.57, MAE = 4.1 mg kg−1). Based on the lower bound of the prediction interval (5th percentile), 48% of agricultural soils in Sweden are most likely not at risk of Cu deficiency (>7 mg kg−1). The Cu map was also validated against concentrations in soil samples from five fields (25–47 ha in size; four samples per ha). The field means were predicted with a MAE of 1.0 mg kg−1 and within-field variation was reproduced with a field-wise squared Pearson correlation coefficient (r2) of 0–0.36. The classification metric ‘recall’ showed that the map of soil Cu concentrations might not predict all possible areas at risk of being Cu deficient, as observational data indicates that about 22% of soils in the mapped area should have Cu concentrations below the risk limit. However, the metric ‘precision’ showed that when the soil map predicted a concentration at or below 7 mg kg−1, it was generally correct. Increasing the limit resulted in the recall and precision increasing rapidly. The remaining 52% of agricultural soils at risk of being below the Cu concentration limit can be targeted by laboratory analysis or monitoring

Collisional energy loss and the suppression of high pTp_T hadrons

We calculate nuclear suppression factor ($R_{AA}$) for light hadrons by taking only the elastic processes and argue that in the measured $p_T$ domain of RHIC, collisional rather than the radiative processes is the dominant mechanism for partonic energy loss.Comment: Presented at the international conference on strong and electroweak matter 2006, May 10-13, Brookhaven National Laborator

Probing collectivity in ultra-relativistic heavy ion collision by leptons and photons

It has been shown that the evolution of collectivity in ultra-relativistic heavy ion collision is manifested in the variation of various HBT radii with invariant mass ($M$) extracted from the correlation functions of two lepton pairs. The value of the radial velocity ($v_r$) can be estimated from the ratio of the $p_T$ distributions of single photons to lepton pairs for various $M$ windows. It has been argued that the variation of radial flow with appropriate kinematic variables can be used as an indicator of a phase transition from initially produced partons to hadrons. We also consider the elliptic flow ($v_2^{HF}$) of the matter as probed by the single electron spectra originating from the semileptonic decays of heavy mesons. The measured values of $v_2^{HF}$ and the nuclear suppression factor ($R_{\mathrm AA}$) at RHIC energy have been reproduced simultaneously by including both the collisional and radiative processes within the scope of perturbative quantum chromodynamics. The $R_{\mathrm AA}$ and $v_2^{HF}$ have been predicted for LHC energy.Comment: Plenary talk at ICPAQGP 2010, December 6-10, 2011, Goa, Indi

STAR inner tracking upgrade - A performance study

Anisotropic flow measurements have demonstrated development of partonic collectivity in $200\mathrm{GeV}$ Au+Au collisions at RHIC. To understand the partonic EOS, thermalization must be addressed. Collective motion of heavy-flavor (c,b) quarks can be used to indicate the degree of thermalization of the light-flavor quarks (u,d,s). Measurement of heavy-flavor quark collectivity requires direct reconstruction of heavy-flavor hadrons in the low \pt region. Measurement of open charm spectra to high \pt can be used to investigate heavy-quark energy loss and medium properties. The Heavy Flavor Tracker (HFT), a proposed upgrade to the STAR experiment at midrapidity, will measure $v_{2}$ of open-charm hadrons to very low \pt by reconstructing their displaced decay vertices. The innermost part of the HFT is the PIXEL detector (made of two low mass monolithic active pixel sensor layers), which delivers a high precision position measurement close to the collision vertex. The Intermediate Silicon Tracker (IST), a 1-layer strip detector, is essential to improve hit identification in the PIXEL detector when running at full RHIC-II luminosity. Using a full GEANT simulation, open charm measurement capabilities of STAR with the HFT will be shown. Its performance in a broad \pt range will be demonstrated on $v_{2}$ (\pt > 0.5\mathrm{GeV}/c) and $R_\mathrm{CP}$ (\pt < 10\mathrm{GeV}/c) measurements of \D meson. Results of reconstruction of \Lc baryon in heavy-ion collisions are presented.Comment: to appear in EPJ C (Hot Quarks 2008 conference volume

Probing Cosmology with Weak Lensing Minkowski Functionals

In this paper, we show that Minkowski Functionals (MFs) of weak gravitational lensing (WL) convergence maps contain significant non-Gaussian, cosmology-dependent information. To do this, we use a large suite of cosmological ray-tracing N-body simulations to create mock WL convergence maps, and study the cosmological information content of MFs derived from these maps. Our suite consists of 80 independent 512^3 N-body runs, covering seven different cosmologies, varying three cosmological parameters Omega_m, w, and sigma_8 one at a time, around a fiducial LambdaCDM model. In each cosmology, we use ray-tracing to create a thousand pseudo-independent 12 deg^2 convergence maps, and use these in a Monte Carlo procedure to estimate the joint confidence contours on the above three parameters. We include redshift tomography at three different source redshifts z_s=1, 1.5, 2, explore five different smoothing scales theta_G=1, 2, 3, 5, 10 arcmin, and explicitly compare and combine the MFs with the WL power spectrum. We find that the MFs capture a substantial amount of information from non-Gaussian features of convergence maps, i.e. beyond the power spectrum. The MFs are particularly well suited to break degeneracies and to constrain the dark energy equation of state parameter w (by a factor of ~ three better than from the power spectrum alone). The non-Gaussian information derives partly from the one-point function of the convergence (through V_0, the "area" MF), and partly through non-linear spatial information (through combining different smoothing scales for V_0, and through V_1 and V_2, the boundary length and genus MFs, respectively). In contrast to the power spectrum, the best constraints from the MFs are obtained only when multiple smoothing scales are combined.Comment: 19 pages, 9 figures, 5 table

EDS tomographic reconstruction regularized by total nuclear variation joined with HAADF-STEM tomography

Energy-dispersive X-ray spectroscopy (EDS) tomography is an advanced technique to characterize compositional information for nanostructures in three dimensions (3D). However, the application is hindered by the poor image quality caused by the low signal-to-noise ratios and the limited number of tilts, which are fundamentally limited by the insufficient number of X-ray counts. In this paper, we explore how to make accurate EDS reconstructions from such data. We propose to augment EDS tomography by joining with it a more accurate high-angle annular dark-field STEM (HAADF-STEM) tomographic reconstruction, for which usually a larger number of tilt images are feasible. This augmentation is realized through total nuclear variation (TNV) regularization, which encourages the joint EDS and HAADF reconstructions to have not only sparse gradients but also common edges and parallel (or antiparallel) gradients. Our experiments show that reconstruction images are more accurate compared to the non-regularized and the total variation regularized reconstructions, even when the number of tilts is small or the X-ray counts are low

Improving ICD-based semantic similarity by accounting for varying degrees of comorbidity

Finding similar patients is a common objective in precision medicine, facilitating treatment outcome assessment and clinical decision support. Choosing widely-available patient features and appropriate mathematical methods for similarity calculations is crucial. International Statistical Classification of Diseases and Related Health Problems (ICD) codes are used worldwide to encode diseases and are available for nearly all patients. Aggregated as sets consisting of primary and secondary diagnoses they can display a degree of comorbidity and reveal comorbidity patterns. It is possible to compute the similarity of patients based on their ICD codes by using semantic similarity algorithms. These algorithms have been traditionally evaluated using a single-term expert rated data set. However, real-word patient data often display varying degrees of documented comorbidities that might impair algorithm performance. To account for this, we present a scale term that considers documented comorbidity-variance. In this work, we compared the performance of 80 combinations of established algorithms in terms of semantic similarity based on ICD-code sets. The sets have been extracted from patients with a C25.X (pancreatic cancer) primary diagnosis and provide a variety of different combinations of ICD-codes. Using our scale term we yielded the best results with a combination of level-based information content, Leacock & Chodorow concept similarity and bipartite graph matching for the set similarities reaching a correlation of 0.75 with our expert's ground truth. Our results highlight the importance of accounting for comorbidity variance while demonstrating how well current semantic similarity algorithms perform.Comment: 11 pages, 6 figures, 1 tabl