The LCG PI project: using interfaces for physics data analysis

In the context of the LHC computing grid (LCG) project, the applications area develops and maintains that part of the physics applications software and associated infrastructure that is shared among the LHC experiments. The "physicist interface" (PI) project of the LCG application area encompasses the interfaces and tools by which physicists will directly use the software, providing implementations based on agreed standards like the analysis systems subsystem (AIDA) interfaces for data analysis. In collaboration with users from the experiments, work has started with implementing the AIDA interfaces for (binned and unbinned) histogramming, fitting and minimization as well as manipulation of tuples. These implementations have been developed by re-using existing packages either directly or by using a (thin) layer of wrappers. In addition, bindings of these interfaces to the Python interpreted language have been done using the dictionary subsystem of the LCG applications area/SEAL project. The actual status and the future planning of the project will be presented

Anaphe - OO Libraries and Tools for Data Analysis

The Anaphe project is an ongoing effort to provide an Object Oriented software environment for data analysis in HENP experiments. A range of commercial and public domain libraries is used to cover basic functionalities; on top of these libraries a set of HENP-specific C++ class libraries for histogram management, fitting, plotting and ntuple-like data analysis has been developed. In order to comply with the user requirements for a command-line driven tool, we have chosen to use a scripting language (Python) as the front-end for a data analysis tool. The loose coupling provided by the consequent use of (AIDA compliant) Abstract Interfaces for each component in combination with the use of shared libraries for their implementation provides an easy integration of existing libraries into modern scripting languages thus allowing for rapid application development. This integration is simplified even further using a specialised toolkit (SWIG) to create "shadow classes" for the Python language, which map the definitions of the Abstract Interfaces almost at a one-to-one level. This paper will give an overview of the architecture and design choices and will present the current status and future developments of the project

Managing Research Data in Big Science

The project which led to this report was funded by JISC in 2010--2011 as part of its 'Managing Research Data' programme, to examine the way in which Big Science data is managed, and produce any recommendations which may be appropriate. Big science data is different: it comes in large volumes, and it is shared and exploited in ways which may differ from other disciplines. This project has explored these differences using as a case-study Gravitational Wave data generated by the LSC, and has produced recommendations intended to be useful variously to JISC, the funding council (STFC) and the LSC community. In Sect. 1 we define what we mean by 'big science', describe the overall data culture there, laying stress on how it necessarily or contingently differs from other disciplines. In Sect. 2 we discuss the benefits of a formal data-preservation strategy, and the cases for open data and for well-preserved data that follow from that. This leads to our recommendations that, in essence, funders should adopt rather light-touch prescriptions regarding data preservation planning: normal data management practice, in the areas under study, corresponds to notably good practice in most other areas, so that the only change we suggest is to make this planning more formal, which makes it more easily auditable, and more amenable to constructive criticism. In Sect. 3 we briefly discuss the LIGO data management plan, and pull together whatever information is available on the estimation of digital preservation costs. The report is informed, throughout, by the OAIS reference model for an open archive

Managing Research Data: Gravitational Waves

The project which led to this report was funded by JISC in 2010–2011 as part of its ‘Managing Research Data’ programme, to examine the way in which Big Science data is managed, and produce any recommendations which may be appropriate. Big science data is different: it comes in large volumes, and it is shared and exploited in ways which may differ from other disciplines. This project has explored these differences using as a case-study Gravitational Wave data generated by the LSC, and has produced recommendations intended to be useful variously to JISC, the funding council (STFC) and the LSC community. In Sect. 1 we define what we mean by ‘big science’, describe the overall data culture there, laying stress on how it necessarily or contingently differs from other disciplines. In Sect. 2 we discuss the benefits of a formal data-preservation strategy, and the cases for open data and for well-preserved data that follow from that. This leads to our recommendations that, in essence, funders should adopt rather light-touch prescriptions regarding data preservation planning: normal data management practice, in the areas under study, corresponds to notably good practice in most other areas, so that the only change we suggest is to make this planning more formal, which makes it more easily auditable, and more amenable to constructive criticism. In Sect. 3 we briefly discuss the LIGO data management plan, and pull together whatever information is available on the estimation of digital preservation costs. The report is informed, throughout, by the OAIS reference model for an open archive. Some of the report’s findings and conclusions were summarised in [1]. See the document history on page 37

Recent developments in GEANT 4

Fil: Depaola, Gerardo Osvaldo. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.GEANT4 is a software toolkit for the simulation of the passage of particles through matter. It is used by a large number of experiments and projects in a variety of application domains, including high energy physics, astrophysics and space science, medical physics and radiation protection. Over the past several years, major changes have been made to the toolkit in order to accommodate the needs of these user communities, and to efficiently exploit the growth of computing power made available by advances in technology. The adaptation of GEANT4 to multithreading, advances in physics, detector modeling and visualization, extensions to the toolkit, including biasing and reverse Monte Carlo, and tools for physics and release validation are discussed here.info:eu-repo/semantics/publishedVersionFil: Depaola, Gerardo Osvaldo. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.Física de Partículas y Campo