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 differential cross sections for single diffractive dissociation in √s = 8 TeV pp collisions using the ATLAS ALFA spectrometer

Abstract: A dedicated sample of Large Hadron Collider proton-proton collision data at centre-of-mass energy s√ = 8 TeV is used to study inclusive single diffractive dissociation, pp → X p. The intact final-state proton is reconstructed in the ATLAS ALFA forward spectrometer, while charged particles from the dissociated system X are measured in the central detector components. The fiducial range of the measurement is −4.0 < log10ξ < −1.6 and 0.016 < |t| < 0.43 GeV2, where ξ is the proton fractional energy loss and t is the squared four-momentum transfer. The total cross section integrated across the fiducial range is 1.59 ± 0.13 mb. Cross sections are also measured differentially as functions of ξ, t, and ∆η, a variable that characterises the rapidity gap separating the proton and the system X . The data are consistent with an exponential t dependence, dσ/dt ∝ eBt with slope parameter B = 7.65 ± 0.34 GeV−2. Interpreted in the framework of triple Regge phenomenology, the ξ dependence leads to a pomeron intercept of α(0) = 1.07 ± 0.09.

Search for Dark Matter Candidates and Large Extra Dimensions in Events with a Photon and Missing Transverse Momentum in pp Collision Data at √s=7 TeV with the ATLAS Detector

Abstract: Results of a search for new phenomena in events with an energetic photon and large missing transverse momentum in proton-proton collisions at √ s = 7 TeV are reported. Data collected by the ATLAS experiment at the LHC corresponding to an integrated luminosity of 4.6 fb−1 are used. Good agreement is observed between the data and the Standard Model predictions. The results are translated into exclusion limits on models with large extra spatial dimensions and on pair production of weakly interacting dark matter candidates. Search for Dark Matter Candidates and Large Extra Dimensions in events with a photon and missing transverse momentum in pp collision data at √

Search for heavy resonances decaying into a W or Z boson and a Higgs boson in final states with leptons and b-jets in 36 fb(-1) of root s=13 TeV pp collisions with the ATLAS detector

Abstract: A search is conducted for new resonances decaying into a W or Z boson and a 125 GeV Higgs boson in the νν¯ bb¯ , l±νbb¯ , and l+l−bb¯ final states, where l± = e± or μ±, in pp collisions at s=13 TeV ...

Measurements of the top quark branching ratios into channels with leptons and quarks with the ATLAS detector

Abstract: Measurements of the branching ratios of top quark decays into leptons and jets using events with t (t) over bar ( top antitop) pairs are reported. Events were recorded with the ATLAS detector at the LHC in pp collisions at a center-of-mass energy of 7 TeV. The collected data sample corresponds to an integrated luminosity of 4.6 fb(-1). The measured top quark branching ratios agree with the Standard Model predictions within the measurement uncertainties of a few percent.

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