Georges Aad

Bio: Georges Aad is an academic researcher from Aix-Marseille University. The author has contributed to research in topics: Large Hadron Collider & Higgs boson. The author has an hindex of 135, co-authored 1121 publications receiving 88811 citations. Previous affiliations of Georges Aad include Centre national de la recherche scientifique & University of Udine.

Measurements of b-jet tagging efficiency with the ATLAS detector using tt¯ events at √s=13 TeV

Abstract: The efficiency to identify jets containing b-hadrons (b-jets) is measured using a high purity sample of dileptonic top quark-antiquark pairs ( $ t\overline{t} $ ) selected from the 36.1 fb$^{−1}$ of data collected by the ATLAS detector in 2015 and 2016 from proton-proton collisions produced by the Large Hadron Collider at a centre-of-mass energy $ \sqrt{s}=13 $ TeV. Two methods are used to extract the efficiency from $ t\overline{t} $ events, a combinatorial likelihood approach and a tag-and-probe method. A boosted decision tree, not using b-tagging information, is used to select events in which two b-jets are present, which reduces the dominant uncertainty in the modelling of the flavour of the jets. The efficiency is extracted for jets in a transverse momentum range from 20 to 300 GeV, with data-to-simulation scale factors calculated by comparing the efficiency measured using collision data to that predicted by the simulation. The two methods give compatible results, and achieve a similar level of precision, measuring data-to-simulation scale factors close to unity with uncertainties ranging from 2% to 12% depending on the jet transverse momentum.

Identification and Energy Calibration of Hadronically Decaying Tau Leptons With the ATLAS Experiment in pp Collisions at √s=8 TeV

Abstract: This paper describes the trigger and offline reconstruction, identification and energy calibration algorithms for hadronic decays of tau leptons employed for the data collected from pp collisions in 2012 with the ATLAS detector at the LHC center-of-mass energy root s = 8 TeV. The performance of these algorithms is measured in most cases with Z decays to tau leptons using the full 2012 dataset, corresponding to an integrated luminosity of 20.3 fb(-1). An uncertainty on the offline reconstructed tau energy scale of 2-4%, depending on transverse energy and pseudorapidity, is achieved using two independent methods. The offline tau identification efficiency is measured with a precision of 2.5% for hadronically decaying tau leptons with one associated track, and of 4% for the case of three associated tracks, inclusive in pseudorapidity and for a visible transverse energy greater than 20 GeV. For hadronic tau lepton decays selected by offline algorithms, the tau trigger identification efficiency is measured with a precision of 2-8%, depending on the transverse energy. The performance of the tau algorithms, both offline and at the trigger level, is found to be stable with respect to the number of concurrent proton-proton interactions and has supported a variety of physics results using hadronically decaying tau leptons at ATLAS.

Measurement of upsilon production in 7 TeV pp collisions at ATLAS

Abstract: Using 1.8 fb(-1) of pp collisions at a center- of- mass energy of 7 TeV recorded by the ATLAS detector at the Large Hadron Collider, we present measurements of the production cross sections of Upsilon(1S,2S,3S) mesons. Upsilon mesons are reconstructed using the dimuon decay mode. Total production cross sections for p(T) < 70 GeV and in the rapidity interval vertical bar y(Upsilon)vertical bar < 2. 25 are measured to be, 8.01 +/- 0.02 +/- 0.36 +/- 0.31 nb, 2.05 +/- 0.01 +/- 0.12 +/- 0.08 nb, and 0.92 +/- 0.01 +/- 0.07 +/- 0.04 nb, respectively, with uncertainties separated into statistical, systematic, and luminosity measurement effects. In addition, differential cross section times dimuon branching fractions for Upsilon(1S), Upsilon(2S), and Upsilon(3S) as a function of Upsilon transverse momentum pT and rapidity are presented. These cross sections are obtained assuming unpolarized production. If the production polarization is fully transverse or longitudinal with no azimuthal dependence in the helicity frame, the cross section may vary by approximately +/- 20%. If a nontrivial azimuthal dependence is considered, integrated cross sections may be significantly enhanced by a factor of 2 or more. We compare our results to several theoretical models of Upsilon meson production, finding that none provide an accurate description of our data over the full range of Upsilon transverse momenta accessible with this data set.

Rapidity gap cross sections measured with the ATLAS detector in pp collisions at √ s =7 TeV

Abstract: Pseudorapidity gap distributions in proton-proton collisions at root s = 7 TeV are studied using a minimum bias data sample with an integrated luminosity of 7.1 mu b(-1). Cross sections are measured differentially in terms of Delta eta(F), the larger of the pseudorapidity regions extending to the limits of the ATLAS sensitivity, at eta = +/- 4.9, in which no final state particles are produced above a transverse momentum threshold p(T)(cut). The measurements span the region 0 Xp), enhanced by double dissociation (pp -> XY) where the invariant mass of the lighter of the two dissociation systems satisfies M-Y less than or similar to 7 GeV. The resulting cross section is ds sigma/d Delta eta(F) approximate to 1 mb for Delta eta(F) greater than or similar to 3. The large rapidity gap data are used to constrain the value of the Pomeron intercept appropriate to triple Regge models of soft diffraction. The cross section integrated over all gap sizes is compared with other LHC inelastic cross section measurements.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Phd by thesis

Abstract: Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These