553 research outputs found
First application of machine learning algorithms to the position reconstruction in Resistive Silicon Detectors
No full textRSDs (Resistive AC-Coupled Silicon Detectors) are n-in-p silicon sensors based on the LGAD (Low-Gain Avalanche Diode) technology, featuring a continuous gain layer over the whole sensor area. The truly innovative feature of these sensors is that the signal induced by an ionising particle is seen on several pixels, allowing the use of reconstruction techniques that combine the information from many read-out channels. In this contribution, the first application of a machine learning technique to RSD devices is presented. The spatial resolution of this technique is compared to that obtained with the standard RSD reconstruction methods that use analytical descriptions of the signal sharing mechanism. A Multi-Output regressor algorithm, trained with a combination of simulated and real data, leads to a spatial resolution of less than 2 mu m for a sensor with a 100 mu m pixel. The prospects of future improvements are also discussed
Searches for strong R-parity conserving SUSY production at the LHC with the ATLAS detector
No full textSearches for supersymmetric squarks and gluinos in events containing jets, missing transverse momentum with or without leptons are presented. The results are based on the full data sample (5 fb-1) recorded in 2011 at sqrt(s)=7 TeV cebtre-of-mass energy by the ATLAS experiment at the LHC
The ATLAS Distributed Analysis System
No full textIn the LHC operations era, analysis of the multi-petabyte ATLAS data sample by globally distributed physicists is a challenging task. To attain the required scale the ATLAS Computing Model was designed around the concept of grid computing, realized in the Worldwide LHC Computing Grid (WLCG), the largest distributed computational resource existing in the sciences. The ATLAS experiment currently stores over 140 PB of data and runs about 140,000 concurrent jobs continuously at WLCG sites. During the first run of the LHC, the ATLAS Distributed Analysis (DA) service has operated stably and scaled as planned. More than 1600 users submitted jobs in 2012, with 2 million or more analysis jobs per week, peaking at about a million jobs per day. The system dynamically distributes popular data to expedite processing and maximally utilize resources. The reliability of the DA service is high but steadily improving; grid sites are continually validated against a set of standard tests, and a dedicated team of expert shifters provides user support and communicates user problems to the sites. Both the user support techniques and the direct feedback of users have been effective in improving the success rate and user experience when utilizing the distributed computing environment. In this contribution a description of the main components, activities and achievements of ATLAS distributed analysis is given. Also several future improvements being undertaken will be described
New Developments in Data-driven Background Determinations for SUSY Searches in ATLAS
No full textAny discovery of new physics relies on detailed understanding of the Standard Model background. At the LHC, we expect to extract the backgrounds from the data itself, with minimum reliance on Monte Carlo simulations. We describe new developments in ATLAS on such data-driven techniques, and prospects for their application on first data
Improving ATLAS grid site reliability with functional tests using HammerCloud
No full textWith the exponential growth of LHC (Large Hadron Collider) data in 2011, and more coming in 2012, distributed computing has become the established way to analyse collider data. The ATLAS grid infrastructure includes almost 100 sites worldwide, ranging from large national computing centers to smaller university clusters. These facilities are used for data reconstruction and simulation, which are centrally managed by the ATLAS production system, and for distributed user analysis. To ensure the smooth operation of such a complex system, regular tests of all sites are necessary to validate the site capability of successfully executing user and production jobs. We report on the development, optimization and results of an automated functional testing suite using the HammerCloud framework. Functional tests are short light-weight applications covering typical user analysis and production schemes, which are periodically submitted to all ATLAS grid sites. Results from those tests are collected and used to evaluate site performances. Sites that fail or are unable to run the tests are automatically excluded from the PanDA brokerage system, therefore avoiding user or production jobs to be sent to problematic sites
Polarized radiative decays at LHCb
No full textWe present a study of decays of the type Lambda_b->Lambda(X) gamma, where Lambda(X) is a Lambda baryon of mass X and spin 1/2 or 3/2. Detailed calculations of decay amplitudes and angular distributions are carried out employing the helicity formalism, and used to derive observables sensitive to new physics. In particular we make use of the initial polarization of the Lambda_b baryon to probe the polarization of the photon emitted in b -> s transitions. Such a measurement can be used to probe the chirality of the effective Hamiltonian, and possibly to unveil physics beyond the Standard Model. An estimate of the LHCb sensitivity to this measurement is also given
Data-driven estimations of Standard Model backgrounds to SUSY searches
No full textMismeasured multi-jet events and W, Z and top quark production in association with jets constitute a major background to searches for supersymmetry at the LHC. We describe recent work performed in the ATLAS Collaboration to estimate these backgrounds for a basic SUSY selection, and we discuss methods to derive them from the first ATLAS data
Distributed analysis functional testing using GangaRobot in the ATLAS experiment
No full textAutomated distributed analysis tests are necessary to ensure smooth operations of the ATLAS grid resources. The HammerCloud framework allows for easy definition, submission and monitoring of grid test applications. Both functional and stress test applications can be defined in HammerCloud. Stress tests are large-scale tests meant to verify the behaviour of sites under heavy load. Functional tests are light user applications running at each site with high frequency, to ensure that the site functionalities are available at all times. Success or failure rates of these tests jobs are individually monitored. Test definitions and results are stored in a database and made available to users and site administrators through a web interface. In this work we present the recent developments of the GangaRobot framework. GangaRobot monitors the outcome of functional tests, creates a blacklist of sites failing the tests, and exports the results tothe ATLAS Site Status Board (SSB) and to the Service Availability Monitor (SAM), providing on the one hand a fast way to identify systematic or temporary site failures, and on the other hand allowing for an effective distribution of the work load on the available resources
- …
