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.

Measurement of the tt¯ production cross-section and lepton differential distributions in eμ dilepton events from pp collisions at √s = 13TeV with the ATLAS detector

Abstract: The inclusive top quark pair ( $$t\bar{t}$$ ) production cross-section $$\sigma _{t\bar{t}}$$ has been measured in proton–proton collisions at $$\sqrt{s}=13\,\text {TeV}$$ , using 36.1 fb $$^{-1}$$ of data collected in 2015–2016 by the ATLAS experiment at the LHC. Using events with an opposite-charge $$e\mu $$ pair and b-tagged jets, the cross-section is measured to be: $$\begin{aligned} \sigma _{t\bar{t}} = 826.4 \pm 3.6\,\mathrm {(stat)}\ \pm 11.5\,\mathrm {(syst)}\ \pm 15.7\,\mathrm {(lumi)}\ \pm 1.9\,\mathrm {(beam)}\,\mathrm {pb}, \end{aligned}$$ where the uncertainties reflect the limited size of the data sample, experimental and theoretical systematic effects, the integrated luminosity, and the LHC beam energy, giving a total uncertainty of 2.4%. The result is consistent with theoretical QCD calculations at next-to-next-to-leading order. It is used to determine the top quark pole mass via the dependence of the predicted cross-section on $$m_t^{\mathrm{pole}}$$ , giving $$m_t^{\mathrm{pole}}=173.1^{+2.0}_{-2.1}\,\text {GeV}$$ . It is also combined with measurements at $$\sqrt{s}=7\,\text {TeV}$$ and $$\sqrt{s}=8\,\text {TeV}$$ to derive ratios and double ratios of $$t\bar{t}$$ and Z cross-sections at different energies. The same event sample is used to measure absolute and normalised differential cross-sections as functions of single-lepton and dilepton kinematic variables, and the results are compared with predictions from various Monte Carlo event generators.

Search for the production of single vector-like and excited quarks in the Wt final state in pp collisions at √s = 8 TeV with the ATLAS detector

Abstract: A search for vector-like quarks and excited quarks in events containing a top quark and a W boson in the final state is reported here. The search is based on 20.3 fb−1 of proton-proton collision data taken at the LHC at a centre-of-mass energy of 8 TeV recorded by the ATLAS detector. Events with one or two leptons, and one, two or three jets are selected with the additional requirement that at least one jet contains a b-quark. Single-lepton events are also required to contain at least one large-radius jet from the hadronic decay of a high-pTW boson or a top quark. No significant excess over the expected background is observed and upper limits on the cross-section times branching ratio for different vector-like quark and excited-quark model masses are derived. For the excited-quark production and decay to Wt with unit couplings, quarks with masses below 1500 GeV are excluded and coupling-dependent limits are set.

Search for strong gravity in multijet final states produced in pp collisions at √s=13 TeV using the ATLAS detector at the LHC

Abstract: A search is conducted for new physics in multijet final states using 3.6 inverse femtobarns of data from proton-proton collisions at root s = 13TeV taken at the CERN Large Hadron Collider with the ...

Search for pair production of a heavy up-type quark decaying to a W boson and a b quark in the lepton+jets channel with the ATLAS detector

Abstract: A search is presented for production of a heavy up-type quark (t') together with its antiparticle, assuming subsequent decay to a W boson and a b quark, t'(t) over bar' -> W(+)bW(-)(b) over bar. The search is based on 1.04 fb(-1) of proton-proton collisions at root s = 7 TeV collected by the ATLAS detector at the CERN Large Hadron Collider. Data are analyzed in the lepton + jets final state, characterized by a high transverse momentum isolated electron or muon, high missing transverse momentum, and at least three jets. No significant excess of events above the background expectation is observed. A 95% C.L. lower limit of 404 GeV is set for the mass of the t' quark.

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