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.

Search for new phenomena in events with same-charge leptons and b-jets in pp collisions at √s=13 TeV with the ATLAS detector

Abstract: A search for new phenomena in events with two same-charge leptons or three leptons and jets identified as originating from b-quarks in a data sample of 361 fb−1 of pp collisions at s√=13 TeV recorded by the ATLAS detector at the Large Hadron Collider is reported No significant excess is found and limits are set on vector-like quark, four-top-quark, and same-sign top-quark pair production The observed (expected) 95% CL mass limits for a vector-like T- and B-quark singlet are mT > 098 (099) TeV and mB > 100 (101) TeV respectively Limits on the production of the vector-like T5/3-quark are also derived considering both pair and single production; in the former case the lower limit on the mass of the T5/3-quark is (expected to be) 119 (121) TeV The Standard Model four-top-quark production cross-section upper limit is (expected to be) 69 (29) fb Constraints are also set on exotic four-top-quark production models Finally, limits are set on same-sign top-quark pair production The upper limit on uu → tt production is (expected to be) 89 (59) fb for a mediator mass of 1 TeV, and a dark-matter interpretation is also derived, excluding a mediator of 3 TeV with a dark-sector coupling of 10 and a coupling to ordinary matter above 031

Measurement of the tt¯Z and tt¯W cross sections in proton-proton collisions at √s=13 TeV with the ATLAS detector

Abstract: A measurement of the associated production of a top-quark pair (tt) with a vector boson (W, Z) in proton-proton collisions at a center-of-mass energy of 13 TeV is presented, using 36.1 fb-1 of integrated luminosity collected by the ATLAS detector at the Large Hadron Collider. Events are selected in channels with two same- or opposite-sign leptons (electrons or muons), three leptons or four leptons, and each channel is further divided into multiple regions to maximize the sensitivity of the measurement. The ttZ and ttW production cross sections are simultaneously measured using a combined fit to all regions. The best-fit values of the production cross sections are σttZ=0.95±0.08stat±0.10syst pb and σttW=0.87±0.13stat±0.14syst pb in agreement with the Standard Model predictions. The measurement of the ttZ cross section is used to set constraints on effective field theory operators which modify the ttZ vertex.

Search for supersymmetry in events with four or more leptons in √s =13 TeV pp collisions with ATLAS

Abstract: Results from a search for supersymmetry in events with four or more charged leptons (electrons, muons and taus) are presented. The analysis uses a data sample corresponding to 36.1 fb-1 of proton-proton collisions delivered by the Large Hadron Collider at s=13 TeV and recorded by the ATLAS detector. Four-lepton signal regions with up to two hadronically decaying taus are designed to target a range of supersymmetric scenarios that can be either enriched in or depleted of events involving the production and decay of a Z boson. Data yields are consistent with Standard Model expectations and results are used to set upper limits on the event yields from processes beyond the Standard Model. Exclusion limits are set at the 95% confidence level in simplified models of general gauge mediated supersymmetry, where Higgsino masses are excluded up to 295 GeV. In R-parity-violating simplified models with decays of the lightest supersymmetric particle to charged leptons, lower limits of 1.46, 1.06, and 2.25 TeV are placed on wino, slepton and gluino masses, respectively. © 2018 CERN.

Measurement of the cross-section for W boson production in association with b-jets in pp collisions at √s=7 TeV with the ATLAS detector

Abstract: This paper reports a measurement of the W+b-jets (W+b+X and W+b (b) over bar +X) production cross-section in proton-proton collisions at a centre-of-mass energy of 7 TeV at the LHC. These results are based on data corresponding to an integrated luminosity of 4.6 fb(-1), collected with the ATLAS detector. Cross-sections are presented as a function of jet multiplicity and of the transverse momentum of the leading b-jet for both the muon and electron decay modes of the W boson. The W+b-jets cross-section, corrected for all known detector effects, is quoted in a limited kinematic range. Combining the muon and electron channels, the fiducial cross-section for W+b-jets is measured to be 7.1 +/- 0.5 (stat) +/- 1.4 (syst) pb, consistent with the next-to-leading order QCD prediction, corrected for non-perturbative and double-parton interactions (DPI) contributions, of 4.70 +/- 0.09 (stat) (+0.60)(-0.49) (scale) +/- 0.06 (PDF) +/- 0.16 (non-pert) (+0.52)(-0.38) (DPI) pb.

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