Hubble Space Telescope/Advanced Camera for Surveys Confirmation of the Dark Substructure in A520

We present the results from a weak gravitational lensing study of the merging cluster A520 based on the analysis of Hubble Space Telescope/Advanced Camera for Surveys (ACS) data. The excellent data quality allows us to reach a mean number density of source galaxies of ~109 per sq. arcmin, which improves both resolution and significance of the mass reconstruction compared to a previous study based on Wide Field Planetary Camera 2 (WFPC2) images. We take care in removing instrumental effects such as the trailing of charge due to radiation damage of the ACS detector and the position-dependent point spread function (PSF). This new ACS analysis confirms the previous claims that a substantial amount of dark mass is present between two luminous subclusters. We examine the distribution of cluster galaxies and observe very little light at this location. We find that the centroid of the dark peak in the current ACS analysis is offset to the southwest by ~1 arcmin with respect to the centroid from the WFPC2 analysis. Interestingly, this new centroid is in better spatial agreement with the location where the X-ray emission is strongest, and the mass-to-light ratio estimated with this centroid is much higher 813+-78 M_sun/L_Rsun than the previous value; the aperture mass based on the WFPC2 centroid provides a slightly lower, but consistent mass. Although we cannot provide a definite explanation for the presence of the dark peak, we discuss a revised scenario, wherein dark matter with a more conventional range sigma_DM/m_DM < 1 cm^2/g of self-interacting cross-section can lead to the detection of this dark substructure. If supported by detailed numerical simulations, this hypothesis opens up the possibility that the A520 system can be used to establish a lower limit of the self-interacting cross-section of dark matter.Comment: Accepted to The Astrophysical Journa

Comparison of predictive models for the early diagnosis of diabetes

Objectives: This study develops neural network models to improve the prediction of diabetes using clinical and lifestyle characteristics. Prediction models were developed using a combination of approaches and concepts. Methods: We used memetic algorithms to update weights and to improve prediction accuracy of models. In the first step, the optimum amount for neural network parameters such as momentum rate, transfer function, and error function were obtained through trial and error and based on the results of previous studies. In the second step, optimum parameters were applied to memetic algorithms in order to improve the accuracy of prediction. This preliminary analysis showed that the accuracy of neural networks is 88%. In the third step, the accuracy of neural network models was improved using a memetic algorithm and resulted model was compared with a logistic regression model using a confusion matrix and receiver operating characteristic curve (ROC). Results: The memetic algorithm improved the accuracy from 88.0% to 93.2%. We also found that memetic algorithm had a higher accuracy than the model from the genetic algorithm and a regression model. Among models, the regression model has the least accuracy. For the memetic algorithm model the amount of sensitivity, specificity, positive predictive value, negative predictive value, and ROC are 96.2, 95.3, 93.8, 92.4, and 0.958 respectively. Conclusions: The results of this study provide a basis to design a Decision Support System for risk management and planning of care for individuals at risk of diabetes