LHC Expectations (Machine, Detectors and Physics)

Starting in two years from now, particle physics will enter a new regime in terms of energies and luminosities, thanks to the Large Hadron Collider (LHC) at CERN. This report summarizes the status of the preparations, both for the machine and the detectors, as of fall 2005. The commissioning and start-up scenarios are outlined and some highlights from the very rich physics programme are given, concentrating on measurements of Standard Model processes, as well as on early discovery scenarios. The prospects of B-physics and heavy ion collisions at LHC are also briefly discussed. The report concludes with an outlook on the ultimate physics reach and on upgrade scenarios.Comment: Plenary talk given at the International Europhysics Conference on High Energy Physics, July 21st - 27th 2005, Lisboa, Portuga

e-Infrastructures for e-Science: A Global View

In the last 10 years, a new way of doing science is spreading in the world thank to the development of virtual research communities across many geographic and administrative boundaries. A virtual research community is a widely dispersed group of researchers and associated scientific instruments working together in a common virtual environment. This new kind of scientific environment, usually addressed as a "collaboratory", is based on the availability of high-speed networks and broadband access, advanced virtual tools and Grid-middleware technologies which, altogether, are the elements of the e-Infrastructures. The European Commission has heavily invested in promoting this new way of collaboration among scientists funding several international projects with the aim of creating e-Infrastructures to enable the European Research Area and connect the European researchers with their colleagues based in Africa, Asia and Latin America. In this paper we describe the actual status of these e- Infrastructures and present a complete picture of the virtual research communities currently using them. Information on the scientific domains and on the applications supported are provided together with their geographic distribution

Prototype of machine learning “as a service” for CMS physics in signal vs background discrimination

Big volumes of data are collected and analysed by LHC experiments at CERN. The success of this scientific challenges is ensured by a great amount of computing power and storage capacity, operated over high performance networks, in very complex LHC computing models on the LHC Computing Grid infrastructure. Now in Run-2 data taking, LHC has an ambitious and broad experimental programme for the coming decades: it includes large investments in detector hardware, and similarly it requires commensurate investment in the R&D in software and com- puting to acquire, manage, process, and analyse the shear amounts of data to be recorded in the High-Luminosity LHC (HL-LHC) era. The new rise of Artificial Intelligence - related to the current Big Data era, to the technological progress and to a bump in resources democratization and efficient allocation at affordable costs through cloud solutions - is posing new challenges but also offering extremely promising techniques, not only for the commercial world but also for scientific enterprises such as HEP experiments. Machine Learning and Deep Learning are rapidly evolving approaches to characterising and describing data with the potential to radically change how data is reduced and analysed, also at LHC. This thesis aims at contributing to the construction of a Machine Learning “as a service” solution for CMS Physics needs, namely an end-to-end data-service to serve Machine Learning trained model to the CMS software framework. To this ambitious goal, this thesis work contributes firstly with a proof of concept of a first prototype of such infrastructure, and secondly with a specific physics use-case: the Signal versus Background discrimination in the study of CMS all-hadronic top quark decays, done with scalable Machine Learning techniques

Annual report 2015

Accessions considered in the study. Overview of the material considered in this study. For all materials, the GenBank identifier, the accession and species name as used in this study (Species) as well as their species synonyms used in the donor seed banks or in the NCBI GenBank (Material source/Reference) are provided. The genome symbol, and the country of origin, where the material was originally collected are given. The ploidy level measured in the scope of this study and the information if a herbarium voucher could be deposited in the herbarium of IPK Gatersleben (GAT) is given. Genomic formulas of tetraploids and hexploids are given as “female x male parent”. The genomes of Aegilops taxa follow Kilian et al. [74] and Li et al. [84]. Genome denominations for Hordeum follow Blattner [107] and Bernhardt [12] for the remaining taxa. (XLS 84 kb