Weighing simulated galaxy clusters using lensing and X-ray

We aim at investigating potential biases in lensing and X-ray methods to measure the cluster mass profiles. We do so by performing realistic simulations of lensing and X-ray observations that are subsequently analyzed using observational techniques. The resulting mass estimates are compared among them and with the input models. Three clusters obtained from state-of-the-art hydrodynamical simulations, each of which has been projected along three independent lines-of-sight, are used for this analysis. We find that strong lensing models can be trusted over a limited region around the cluster core. Extrapolating the strong lensing mass models to outside the Einstein ring can lead to significant biases in the mass estimates, if the BCG is not modeled properly for example. Weak lensing mass measurements can be largely affected by substructures, depending on the method implemented to convert the shear into a mass estimate. Using non-parametric methods which combine weak and strong lensing data, the projected masses within R200 can be constrained with a precision of ~10%. De-projection of lensing masses increases the scatter around the true masses by more than a factor of two due to cluster triaxiality. X-ray mass measurements have much smaller scatter (about a factor of two smaller than the lensing masses) but they are generally biased low by 5-20%. This bias is ascribable to bulk motions in the gas of our simulated clusters. Using the lensing and the X-ray masses as proxies for the true and the hydrostatic equilibrium masses of the simulated clusters and averaging over the cluster sample we are able to measure the lack of hydrostatic equilibrium in the systems we have investigated.Comment: 27 pages, 21 figures, accepted for publication on A&A. Version with full resolution images can be found at http://pico.bo.astro.it/~massimo/Public/Papers/massComp.pd

Chrysotile detection in soils with proximal hyperspectral sensing and chemometrics

In this work the authors present an innovative methodology, based on proximal hyperspectral sensing and chemometric techniques, aimed at detecting asbestos containing soils. Short Wave InfraRed (SWIR) reflectance spectra of reference samples containing known chrysotile fractions were collected in laboratory. Since the identification of asbestos containing soils depends on the contaminant mass percentage (weight/weight), two supervised multivariate data projection methods were evaluated for asbestos concentration prediction. The first results are reported here, together with advantages and limits of the analytical methods. Orthogonal Partial Least Squares (PLS) regression showed the lowest error in prediction and the highest coefficient of determination in prediction. This technique would support screening activities frequently conducted during environmental assessment and remediation projects

Flexible ecoflex®/graphene nanoplatelet foams for highly sensitive low-pressure sensors

The high demand for multifunctional devices for smart clothing applications, human motion detection, soft robotics, and artificial electronic skins has encouraged researchers to develop new high-performance flexible sensors. In this work, we fabricated and tested new 3D squeezable Ecoflex® open cell foams loaded with different concentrations of graphene nanoplatelets (GNPs) in order to obtain lightweight, soft, and cost-effective piezoresistive sensors with high sensitivity in a low-pressure regime. We analyzed the morphology of the produced materials and characterized both the mechanical and piezoresistive response of samples through quasi-static cyclic compression tests. Results indicated that sensors infiltrated with 1 mg of ethanol/GNP solution with a GNP concentration of 3 mg/mL were more sensitive and stable compared to those infiltrated with the same amount of ethanol/GNP solution but with a lower GNP concentration. The electromechanical response of the sensors showed a negative piezoresistive behavior up to ~10 kPa and an opposite trend for the 10–40 kPa range. The sensors were particularly sensitive at very low deformations, thus obtaining a maximum sensitivity of 0.28 kPa−1 for pressures lower than 10 kPa

Asbestos detection in construction and demolition waste by different classification methods applied to short-wave infrared hyperspectral images

In this study, different multivariate classification methods were applied to hyperspectral images acquired, in the short-wave infrared range (SWIR: 1000-2500 nm), to define and evaluate quality control actions applied to construction and demolition waste (C&DW) flow streams, with particular reference to the detection of hazardous material as asbestos. Three asbestos fibers classes (i.e., amosite, chrysotile and crocidolite) inside asbestos-containing materials (ACM) were investigated. Samples were divided into two groups: calibration and validation datasets. The acquired hyperspectral images were first explored by Principal Component Analysis (PCA). The following multivariate classification methods were selected in order to verify and compare their efficiency and robustness: Hierarchical Partial Least Squares-Discriminant Analysis (Hi-PLSDA), Principal Component Analysis k-Nearest Neighbors (PCA-kNN) and Error Correcting Output Coding with Support Vector Machines (ECOC-SVM). The classification results obtained for the three models were evaluated by prediction maps and the values of performance parameters (Sensitivity and Specificity). Micro-X-ray fluorescence (micro-XRF) maps confirmed the correctness of classification results. The results demonstrate how SWIR-HSI technology, coupled with multivariate analysis modelling, is a promising approach to develop both "off-line" and "online" fast, reliable and robust quality control strategies, finalized to perform a quick assessment of ACM presence

Searching for galaxy clusters in the Kilo-Degree Survey

In this paper, we present the tools used to search for galaxy clusters in the Kilo Degree Survey (KiDS), and our first results. The cluster detection is based on an implementation of the optimal filtering technique that enables us to identify clusters as over-densities in the distribution of galaxies using their positions on the sky, magnitudes, and photometric redshifts. The contamination and completeness of the cluster catalog are derived using mock catalogs based on the data themselves. The optimal signal to noise threshold for the cluster detection is obtained by randomizing the galaxy positions and selecting the value that produces a contamination of less than 20%. Starting from a subset of clusters detected with high significance at low redshifts, we shift them to higher redshifts to estimate the completeness as a function of redshift: the average completeness is ~ 85%. An estimate of the mass of the clusters is derived using the richness as a proxy. We obtained 1858 candidate clusters with redshift 0 < z_c < 0.7 and mass 13.5 < log(M500/Msun) < 15 in an area of 114 sq. degrees (KiDS ESO-DR2). A comparison with publicly available Sloan Digital Sky Survey (SDSS)-based cluster catalogs shows that we match more than 50% of the clusters (77% in the case of the redMaPPer catalog). We also cross-matched our cluster catalog with the Abell clusters, and clusters found by XMM and in the Planck-SZ survey; however, only a small number of them lie inside the KiDS area currently available.Comment: 13 pages, 15 figures. Accepted for publication on Astronomy & Astrophysic

Intrinsic and Extrinsic Quality Attributes of Fresh and Semi-Hard Goat Cheese from Low- and High-Input Farming Systems

In this study, we investigated the lipid composition of fresh and semi-hard goat cheese produced in three Italian farms as well as the welfare assessment of goats reared in these farms. The fatty acid (FA) profile of cheese samples were found to be strictly related to the livestock system. Cheese collected from farms in which goats were allowed to graze and were fed diets with a higher forage/concentrate (F/C) ratio showed a FA profile represented by higher contents of health-promoting fatty acids. In the same samples, the health lipid indices showed the most favorable values. Conversely, cheese samples collected from a conventional-lowland farm, where goats were fed with higher amounts of concentrates and lower F/C ratio, presented a lower nutritional quality, characterized by the worst results for what concerns the health lipid indices. Then, we built a multivariate model able to discriminate samples coming from farms managed by a low-input system from those coming from farm managed by a high-input system. The comparison of animal welfare measurements and fatty acids data showed that a better intrinsic quality of low-input farms did not always correspond to better extrinsic quality, suggesting that the information on the livestock system is not always enough to provide consumers with complete awareness of the total product quality

Workers’ exposure assessment during the production of graphene nanoplatelets in r&d laboratory

Widespread production and use of engineered nanomaterials in industrial and research settings raise concerns about their health impact in the workplace. In the last years, graphene-based nanomaterials have gained particular interest in many application fields. Among them, graphene nanoplatelets (GNPs) showed superior electrical, optical and thermal properties, low-cost and availability. Few and conflicting results have been reported about toxicity and potential effects on workers’ health, during the production and handling of these nanostructures. Due to this lack of knowledge, systematic approaches are needed to assess risks and quantify workers’ exposure to GNPs. This work applies a multi-metric approach to assess workers’ exposure during the production of GNPs, based on the Organization for Economic Cooperation and Development (OECD) methodology by integrating real-time measurements and personal sampling. In particular, we analyzed the particle number concentration, the average diameter and the lung deposited surface area of airborne nanoparticles during the production process conducted by thermal exfoliation in two different ways, compared to the background. These results have been integrated by electron microscopic and spectroscopic analysis on the filters sampled by personal impactors. The study identifies the process phases potentially at risk for workers and reports quantitative information about the parameters that may influence the exposure in order to propose recommendations for a safer design of GNPs production process

A PCA-based automated finder for galaxy-scale strong lenses

We present an algorithm using Principal Component Analysis (PCA) to subtract galaxies from imaging data, and also two algorithms to find strong, galaxy-scale gravitational lenses in the resulting residual image. The combined method is optimized to find full or partial Einstein rings. Starting from a pre-selection of potential massive galaxies, we first perform a PCA to build a set of basis vectors. The galaxy images are reconstructed using the PCA basis and subtracted from the data. We then filter the residual image with two different methods. The first uses a curvelet (curved wavelets) filter of the residual images to enhance any curved/ring feature. The resulting image is transformed in polar coordinates, centered on the lens galaxy center. In these coordinates, a ring is turned into a line, allowing us to detect very faint rings by taking advantage of the integrated signal-to-noise in the ring (a line in polar coordinates). The second way of analysing the PCA-subtracted images identifies structures in the residual images and assesses whether they are lensed images according to their orientation, multiplicity and elongation. We apply the two methods to a sample of simulated Einstein rings, as they would be observed with the ESA Euclid satellite in the VIS band. The polar coordinates transform allows us to reach a completeness of 90% and a purity of 86%, as soon as the signal-to-noise integrated in the ring is higher than 30, and almost independent of the size of the Einstein ring. Finally, we show with real data that our PCA-based galaxy subtraction scheme performs better than traditional subtraction based on model fitting to the data. Our algorithm can be developed and improved further using machine learning and dictionary learning methods, which would extend the capabilities of the method to more complex and diverse galaxy shapes

Events with Isolated Charged Leptons and Large Missing Transverse Momentum at HERA

Striking events with isolated charged leptons, large missing transverse momentum and large transverse momentum of the hadronic final state were observed at the electron proton collider HERA in a data sample corresponding to a luminosity of about 130 pb-1. The H1 collaboration observed 11 events with isolated electrons or muons and with transverse momentum above 25 GeV. Only 3.4+-0.6 events were expected from Standard Model (SM) processes. Six of these events have a transverse momentum of greater than 40 GeV, while 1.3+-0.3 events were expected. The ZEUS collaboration observed good agreement with the SM. However, ZEUS found two events with a similar event topology, but tau leptons instead of electrons or muons in the final state. Only 0.2+-0.05 events were expected from SM processes. For various hypotheses the compatibility of the experimental results was investigated with respect to the SM and with respect to possible explanations beyond the SM. Prospects for the high-luminosity HERA-II data taking period are given

Comparison of Chemical Composition and Safety Issues in Fish Roe Products: Application of Chemometrics to Chemical Data

Processed fish roes are acquiring considerable importance in the modern food market, entering more and more often as an ingredient in food preparation and as caviar substitutes. In this study, we defined quality, traceability and safety issues related to processed fish roe products from different species. The results obtained allowed to distinguish eggs originated from different fish species and to discriminate between fish roes and caviar samples obtained from four different sturgeons species. We observed that roes showed a trend of grouping according to ecological and reproductive habits of fish species. We highlighted the differences between eggs originated by farmed and freshwater fish, enriched in n6 polyunsaturated fatty acids (PUFAs), and all the others, in which n3 PUFAs were prevalent. In addition, we evaluated processed fish roes under a food safety point of view, combining microbiological analysis with the determination of organic acids, used in some products as authorized preservatives. Microbiological characterization has proved a general good hygienic level for these products. Organic acids determination showed values in compliance with European Union (EU) regulations in almost of samples; in some cases, we found a mismatch between the organic acids detected and what was reported in labels. Processed fish roes could be considered a safe product that can provide to human nutrition a valuable content of essential fatty acids