New methods of evaluation of the flavour composition in annihilation by double hemisphere tagging at LEP/SLC energies

Two new methods are proposed to extract the flavour contents of the events produced at LEP/SLC, together with the classification matrix of a tagging by hemispheres. By utilising the tagging obtained in both hemispheres, the efficiencies, backgrounds and flavour compositions are directly obtained by fitting the data. A minimal dependence on modelling and a consistent treatment of systematic errors are achieved by applying these methods. The choice of the tagging algorithm is irrelevant in the methods, provided that similar efficiencies are reached. As an example, a multivariate analysis technique combining the tracking information given by a microvertex detector has been applied to extract the Z → b overlineb branching ratio using a standard simulation of a LEP/SLC experiment

Lessons learned from the ATLAS performance studies of the Iberian Cloud for the first LHC running period

In this contribution we describe the performance of the Iberian (Spain and Portugal) ATLAS cloud during the first LHC running period (March 2010-January 2013) in the context of the GRID Computing and Data Distribution Model. The evolution of the resources for CPU, disk and tape in the Iberian Tier-1 and Tier-2s is summarized. The data distribution over all ATLAS destinations is shown, focusing on the number of files transferred and the size of the data. The status and distribution of simulation and analysis jobs within the cloud are discussed. The Distributed Analysis tools used to perform physics analysis are explained as well. Cloud performance in terms of the availability and reliability of its sites is discussed. The effect of the changes in the ATLAS Computing Model on the cloud is analyzed. Finally, the readiness of the Iberian Cloud towards the first Long Shutdown (LS1) is evaluated and an outline of the foreseen actions to take in the coming years is given. The shutdown will be a good opportunity to improve and evolve the ATLAS Distributed Computing system to prepare for the future challenges of the LHC operation.Peer Reviewe

The ATLAS EventIndex: Full chain deployment and first operation

AbstractThe Event Index project consists in the development and deployment of a complete catalogue of events for experiments with large amounts of data, such as the ATLAS experiment at the LHC accelerator at CERN. Data to be stored in the EventIndex are produced by all production jobs that run at CERN or the GRID; for every permanent output file, a snippet of information, containing the file unique identifier and the relevant attributes for each event, is sent to the central catalogue. The estimated insertion rate during the LHC Run 2 is about 80 Hz of file records containing ∼15 kHz of event records. This contribution describes the system design, the initial performance tests of the full data collection and cataloguing chain, and the project evolution towards the full deployment and operation by the end of 2014

Spanish ATLAS tier-2: Facing up to LHC Run 2

The goal of this work is to describe the way of addressing the main challenges of Run 2 by the Spanish ATLAS Tier-2. The considerable increase of energy and luminosity for the upcoming Run 2 with respect to Run 1 has led to a revision of the ATLAS computing model as well as some of the main ATLAS computing tools. In this paper, the adaptation to these changes will be described. The Spanish ATLAS Tier-2 is a R&D project which consists of a distributed infrastructure composed of three sites and its members are involved in ATLAS computing progress, namely the work in different tasks and the development of new tools (e.g. Event Index)This work has been supported by MINECO, Spain (Proj. Ref. FPA2010-21919-C03-01,02,03 & FPA2013-47424-C3,01,02,03), which include FEDER funds from the European Unio

Dynamics of air–sea CO2 fluxes in the northwestern European shelf based on voluntary observing ship and satellite observations

From January 2011 to December 2013, we constructed a comprehensive pCO2 data set based on voluntary observing ship (VOS) measurements in the western English Channel (WEC). We subsequently estimated surface pCO2 and air–sea CO2 fluxes in northwestern European continental shelf waters using multiple linear regressions (MLRs) from remotely sensed sea surface temperature (SST), chlorophyll a concentration (Chl a), wind speed (WND), photosynthetically active radiation (PAR) and modeled mixed layer depth (MLD). We developed specific MLRs for the seasonally stratified northern WEC (nWEC) and the permanently well-mixed southern WEC (sWEC) and calculated surface pCO2 with uncertainties of 17 and 16 μatm, respectively. We extrapolated the relationships obtained for the WEC based on the 2011–2013 data set (1) temporally over a decade and (2) spatially in the adjacent Celtic and Irish seas (CS and IS), two regions which exhibit hydrographical and biogeochemical characteristics similar to those of WEC waters. We validated these extrapolations with pCO2 data from the SOCAT and LDEO databases and obtained good agreement between modeled and observed data. On an annual scale, seasonally stratified systems acted as a sink of CO2 from the atmosphere of −0.6 ± 0.3, −0.9 ± 0.3 and −0.5 ± 0.3 mol C m−2 yr−1 in the northern Celtic Sea, southern Celtic sea and nWEC, respectively, whereas permanently well-mixed systems acted as source of CO2 to the atmosphere of 0.2 ± 0.2 and 0.3 ± 0.2 mol C m−2 yr−1 in the sWEC and IS, respectively. Air–sea CO2 fluxes showed important inter-annual variability resulting in significant differences in the intensity and/or direction of annual fluxes. We scaled the mean annual fluxes over these provinces for the last decade and obtained the first annual average uptake of −1.11 ± 0.32 Tg C yr−1 for this part of the northwestern European continental shelf. Our study showed that combining VOS data with satellite observations can be a powerful tool to estimate and extrapolate air–sea CO2 fluxes in sparsely sampled area

Charged-particle multiplicities in pp interactions at root s=900 GeV measured with the ATLAS detector at the LHC

The first measurements from proton-proton collisions recorded with the ATLAS detector at the LHC are presented. Data were collected in December 2009 using a minimum-bias trigger during collisions at a centre-of-mass energy of 900 GeV. The charged-particle multiplicity, its dependence on transverse momentum and pseudorapidity, and the relationship between mean transverse momentum and charged-particle multiplicity are measured for events with at least one charged particle in the kinematic range |eta|500 MeV. The measurements are compared to Monte Carlo models of proton-proton collisions and to results from other experiments at the same centre-of-mass energy. The charged-particle multiplicity per event and unit of pseudorapidity at eta = 0 is measured to be 1.333 +/- 0.003 (stat.) +/- 0.040 (syst.), which is 5-15% higher than the Monte Carlo models predict

Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb(-1). The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 -dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m(jj) < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured b (b) over bar -dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta

Deep Learning to improve Experimental Sensitivity and Generative Models for Monte Carlo simulations for searching for New Physics in LHC experiments

ML/DL techniques have shown their power in the improvement of several studies and tasks in HEP, especially in physics analysis. Our approach has been to take a number of the ML/DL tools provided by several open-source platforms and apply them to several classification problems, for instance, to the tt¯ resonance extraction in the LHC experiments. Gradient-boosting Trees, Random Forest, Artificial Neural Networks (ANN), etc. have been used and optimized by means of adjusting several hyperparameters to control overfitting. On top of this, data simulation with traditional models is computationally very demanding, making the use of generative models an alternative for generating simulated Monte Carlo events with similar quality at a lower computational cost. This could help to produce more simulated data statistics available for better sensitivity and more accurate assessment of systematic errors in potential Physics Beyond Standard Model discoveries. In this work, we study the use of generative models based on Deep Learning as faster Monte Carlo event generators in the LHC context, reducing the time and energy cost of currently used methods. In particular, we focus on different configurations of Variational Autoencoders, taking as a starting point the well-known β-VAE and proposing the α-VAE as a new and simpler VAE architecture that improves the results in some experiments. Considerations will be made about the reliability of these simulated data when they are produced with very high statistics

Properties of jets measured from tracks in proton-proton collisions at center-of-mass energy sqrt(s) = 7 TeV with the ATLAS detector

Jets are identified and their properties studied in center-of-mass energy sqrt(s) = 7 TeV proton-proton collisions at the Large Hadron Collider using charged particles measured by the ATLAS inner detector. Events are selected using a minimum bias trigger, allowing jets at very low transverse momentum to be observed and their characteristics in the transition to high-momentum fully perturbative jets to be studied. Jets are reconstructed using the anti-k(t) algorithm applied to charged particles with two radius parameter choices, 0.4 and 0.6. An inclusive charged jet transverse momentum cross section measurement from 4 GeV to 100 GeV is shown for four ranges in rapidity extending to 1.9 and corrected to charged particle-level truth jets. The transverse momenta and longitudinal momentum fractions of charged particles within jets are measured, along with the charged particle multiplicity and the particle density as a function of radial distance from the jet axis. Comparison of the data with the theoretical models implemented in existing tunings of Monte Carlo event generators indicates reasonable overall agreement between data and Monte Carlo. These comparisons are sensitive to Monte Carlo parton showering, hadronization, and soft physics models