Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userPerformance of an Operating High Energy Physics Data Grid: D0SAR-Grid
62
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
The D0 experiment at Fermilabs Tevatron will record several petabytes of data over the next five years in pursuing the goals of understanding nature and searching for the origin of mass. Computing resources required to analyze these data far exceed capabilities of any one institution. Moreover, the widely scattered geographical distribution of D0 collaborators poses further serious difficulties for optimal use of human and computing resources. These difficulties will exacerbate in future high energy physics experiments, like the LHC. The computing grid has long been recognized as a solution to these problems. This technology is being made a more immediate reality to end users in D0 by developing a grid in the D0 Southern Analysis Region (D0SAR), D0SAR-Grid, using all available resources within it and a home-grown local task manager, McFarm. We will present the architecture in which the D0SAR-Grid is implemented, the use of technology and the functionality of the grid, and the experience from operating the grid in simulation, reprocessing and data analyses for a currently running HEP experiment.
rate research
Read More
In this presentation the experiences of the LHC experiments using grid computing were presented with a focus on experience with distributed analysis. After many years of development, preparation, exercises, and validation the LHC (Large Hadron Collider) experiments are in operations. The computing infrastructure has been heavily utilized in the first 6 months of data collection. The general experience of exploiting the grid infrastructure for organized processing and preparation is described, as well as the successes employing the infrastructure for distributed analysis. At the end the expected evolution and future plans are outlined.
A selection of unfolding methods commonly used in High Energy Physics is compared. The methods discussed here are: bin-by-bin correction factors, matrix inversion, template fit, Tikhonov regularisation and two examples of iterative methods. Two procedures to choose the strength of the regularisation are tested, namely the L-curve scan and a scan of global correlation coefficients. The advantages and disadvantages of the unfolding methods and choices of the regularisation strength are discussed using a toy example.
Modern analysis of high energy physics (HEP) data needs advanced statistical tools to separate signal from background. A C++ package has been implemented to provide such tools for the HEP community. The package includes linear and quadratic discriminant analysis, decision trees, bump hunting (PRIM), boosting (AdaBoost), bagging and random forest algorithms, and interfaces to the standard backpropagation neural net and radial basis function neural net implemented in the Stuttgart Neural Network Simulator. Supplemental tools such as bootstrap, estimation of data moments, and a test of zero correlation between two variables with a joint elliptical distribution are also provided. The package offers a convenient set of tools for imposing requirements on input data and displaying output. Integrated in the BaBar computing environment, the package maintains a minimal set of external dependencies and therefore can be easily adapted to any other environment. It has been tested on many idealistic and realistic examples.
Bayesian inference is a widely used and powerful analytical technique in fields such as astronomy and particle physics but has historically been underutilized in some other disciplines including semiconductor devices. In this work, we introduce Bayesim, a Python package that utilizes adaptive grid sampling to efficiently generate a probability distribution over multiple input parameters to a forward model using a collection of experimental measurements. We discuss the implementation choices made in the code, showcase two examples in photovoltaics, and discuss general prerequisites for the approach to apply to other systems.
We present a procedure for reconstructing particle cascades from event data measured in a high energy physics experiment. For evaluating the hypothesis of a specific physics process causing the observed data, all possible reconstructi
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
