Virtual machines are a promising technology to overcome some of the problems found in current grid infrastructures, like heterogeneity, performance partitioning or application isolation. In this work, we present straightforward deployment of virtual machines in globus grids. This solution is based on standard services and does not require additional middleware to be installed. Also, we assess the suitability of this deployment in the execution of a high throughput scientific application, the XMM-Newton scientific analysis system.

The Latin American Giant Observatory (LAGO) is a ground-based extended cosmic rays observatory designed to study transient astrophysical events, the role of the atmosphere on the formation of secondary particles, and space-weather-related phenomena. With the use of a network of Water Cherenkov Detectors (WCDs), LAGO measures the secondary particle flux, a consequence of the interaction of astroparticles impinging on the atmosphere of Earth. This flux can be grouped into three distinct basic constituents: electromagnetic, muonic, and hadronic components. When a particle enters a WCD, it generates a measurable signal characterized by unique features correlating to the particle’s type and the detector’s specific response. The resulting charge histograms from these signals provide valuable insights into the flux of primary astroparticles and their key characteristics. However, these data are insufficient to effectively distinguish between the contributions of different secondary particles. In this work, we extend our previous research by using detailed simulations of the expected atmospheric response to the primary flux and the corresponding response of our WCDs to atmospheric radiation. This dataset, which was created through the combination of the outputs of the ARTI and Meiga simulation frameworks, simulated the expected WCD signals produced by the flux of secondary particles during one day at the LAGO site in Bariloche, Argentina, situated at 865 m above sea level. This was achieved by analyzing the real-time magnetospheric and local atmospheric conditions for February and March of 2012, where the resultant atmospheric secondary-particle flux was integrated into a specific Meiga application featuring a comprehensive Geant4 model of the WCD at this LAGO location. The final output was modified for effective integration into our machine-learning pipeline. With an implementation of Ordering Points to Identify the Clustering Structure (OPTICS), a density-based clustering algorithm used to identify patterns in data collected by a single WCD, we have further refined our approach to implement a method that categorizes particle groups using advanced unsupervised machine learning techniques. This allowed for the differentiation among particle types and utilized the detector’s nuanced response to each, thus pinpointing the principal contributors within each group. Our analysis has demonstrated that applying our enhanced methodology can accurately identify the originating particles with a high degree of confidence on a single-pulse basis, highlighting its precision and reliability. These promising results suggest the feasibility of future implementations of machine-leaning-based models throughout LAGO’s distributed detection network and other astroparticle observatories for semi-automated, onboard and real-time data analysis.

Neoclassical (NC) transport calculations are necessary for the complete simulation cycle of the behavior of plasmas inside both tokamaks and stellarators, which are more complex to be performed on the latest. In addition, due to the fact that the NC transport is mainly determined by the magnetic properties of the device (mathematically designed by the number of Fourier harmonics needed to describe the confining magnetic field), its study is mandatory to ensure the efficiency of a certain coil configuration before it is implemented in a fusion reactor. Thus, improvements to the already constructed devices and certain design decisions for the new ones, such as W7-X, NCSX, or QPS, have been made based on the estimations of the NC transport, among other criteria. In this sense, a compromise between the number of modes and polynomials to describe the distribution function and the required computing time to obtain reliable estimates is a must. This paper presents the porting process to the Grid and the computational optimization of the Drift Kinetic Equation Solver code, devoted to obtain the monoenergetic diffusion coefficients of the NC transport as well as the first coupled prototype for estimating the transport coefficients with such a new code release. The advantage of this code over the Monte Carlo applications already in production on the Grid is that it allows the estimation of all the transport coefficients, not only the diagonal ones. The tests and results obtained have been applied to the TJ-II Flexible Heliac, a stellarator in operation at National Fusion Laboratory (Spain), by using the EELA-2 Project infrastructure.