Clocking the formation of today's largest galaxies: Wide field integral spectroscopy of Brightest Cluster Galaxies and their surroundings

The formation and evolution of local brightest cluster galaxies (BCGs) is investigated by determining the stellar populations and dynamics from the galaxy core, though the outskirts and into the intracluster light (ICL). Integral spectroscopy of 23 BCGs observed out to 4 r_e is collected and high signal-to-noise regions are identified. Stellar population synthesis codes are used to determine the age, metallicity, velocity, and velocity dispersion of stars within each region. The intracluster light (ICL) spectra are best modeled with populations that are younger and less metal-rich than those of the BCG cores. The average BCG core age of the sample is 13.3$\pm$ 2.8 Gyr and the average metallicity is [Fe/H] = 0.30 $\pm$ 0.09, whereas for the ICL the average age is 9.2$\pm$3.5 Gyr and the average metallicity is [Fe/H] = 0.18$\pm$0.16. The velocity dispersion profile is seen to be rising or flat in most of the sample (17/23), and those with rising values reach the value of the host cluster's velocity dispersion in several cases. The most extended BCGs are closest to the peak of the cluster's X-ray luminosity. The results are consistent with the idea that the BCG cores and inner regions formed quickly and long ago, with the outer regions and ICL forming more recently, and continuing to assemble through minor merging. Any recent star formation in the BCGs is a minor component, and is associated with the cluster cool core status.Comment: 22 pages, 21 figures, MNRAS, accepte

Prediction of sulfur content in diesel fuel using fluorescence spectroscopy and a hybrid ant colony - Tabu Search algorithm with polynomial bases expansion

It is widely accepted that feature selection is an essential step in predictive modeling. There are several approaches to feature selection, from filter techniques to meta-heuristics wrapper methods. In this paper, we propose a compilation of tools to optimize the fitting of black-box linear models. The proposed AnTSbe algorithm combines Ant Colony Optimization and Tabu Search memory list for the selection of features and uses l1 and l2 regularization norms to fit the linear models. In addition, a polynomial combination of input features was introduced to further explore the information contained in the original data. As a case study, excitation-emission matrix fluorescence data were used as the primary measurements to predict total sulfur concentration in diesel fuel samples. The sample dataset was divided into S10 (less than 10 ppm of total sulfur), and S100 (mean sulfur content of 100 ppm) groups and local linear models were fit with AnTSbe. For the Diesel S100 local models, using only 5 out of the original 1467 fluorescence pairs, combined with bases expansion, we were able to satisfactorily predict total sulfur content in samples with MAPE of less than 4% and RMSE of 4.68 ppm, for the test subset. For the Diesel S10 local models, the use of 4 Ex/Em pairs was sufficient to predict sulfur content with MAPE 0.24%, and RMSE of 0.015 ppm, for the test subset. Our experimental results demonstrate that the proposed methodology was able to satisfactorily optimize the fitting of linear models to predict sulfur content in diesel fuel samples without need of chemical of physical pre-treatment, and was superior to classic PLS regression methods and also to our previous results with ant colony optimization studies in the same dataset. The proposed AnTSbe can be directly applied to data from other sources without need for adaptations