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 massive colored scalars in four-jet final states in root s=7 TeV proton-proton collisions with the ATLAS detector

Abstract: A search for pair-produced scalar particles decaying to a four-jet final state is presented. The analysis is performed using an integrated luminosity of 34 pb^-1 recorded by the ATLAS detector in 2010. No deviation from the Standard Model is observed. For a scalar mass of 100 GeV (190 GeV) the limit on the scalar gluon pair production cross section at 95% confidence level is 1 nb (0.28 nb). When these results are interpreted as mass limits, scalar-gluons (hyperpions) with masses of 100 to 185 GeV (100 to 155 GeV) are excluded at 95% confidence level with the exception of a mass window of width about 5 GeV (15 GeV) around 140 GeV.

Search for heavy lepton resonances decaying to a Z boson and a lepton in pp collisions at √s=8 TeV with the ATLAS detector

Abstract: A search for heavy leptons decaying to a Z boson and an electron or a muon is presented. The search is based on pp collision data taken at root s = 8TeV by the ATLAS experiment at the CERN Large Hadron Collider, corresponding to an integrated luminosity of 20.3 fb(-1). Three high-transverse-momentum electrons or muons are selected, with two of them required to be consistent with originating from a Z boson decay. No significant excess above Standard Model background predictions is observed, and 95% confidence level limits on the production cross section of high-mass trilepton resonances are derived. The results are interpreted in the context of vector-like lepton and type-III seesaw models. For the vector-like lepton model, most heavy lepton mass values in the range 114-176 GeV are excluded. For the type-III seesaw model, most mass values in the range 100-468 GeV are excluded.

Measurements of fiducial and differential cross sections for Higgs boson production in the diphoton decay channel at √s=8 TeV with ATLAS

Abstract: Measurements of fiducial and differential cross sections are presented for Higgs boson production in proton-proton collisions at a centre-of-mass energy of TeV. The analysis is performed in the H -> gamma gamma decay channel using 20.3 fb(-1) of data recorded by the ATLAS experiment at the CERN Large Hadron Collider. The signal is extracted using a fit to the diphoton invariant mass spectrum assuming that the width of the resonance is much smaller than the experimental resolution. The signal yields are corrected for the effects of detector inefficiency and resolution. The pp -> H -> gamma gamma fiducial cross section is measured to be 43.2 +/- 9.4(stat.) (-aEuro parts per thousand 2.9) (+ 3.2) (syst.) +/- 1.2(lumi)fb for a Higgs boson of mass 125.4GeV decaying to two isolated photons that have transverse momentum greater than 35% and 25% of the diphoton invariant mass and each with absolute pseudorapidity less than 2.37. Four additional fiducial cross sections and two cross-section limits are presented in phase space regions that test the theoretical modelling of different Higgs boson production mechanisms, or are sensitive to physics beyond the Standard Model. Differential cross sections are also presented, as a function of variables related to the diphoton kinematics and the jet activity produced in the Higgs boson events. The observed spectra are statistically limited but broadly in line with the theoretical expectations.

Higgs boson production cross-section measurements and their EFT interpretation in the 4 ℓ decay channel at √s= 13 TeV with the ATLAS detector

Abstract: Higgs boson properties are studied in the fourlepton decay channel (where lepton = e, mu) using 139 fb(-1) of proton-proton collision data recorded at v s =13 TeV by the ATLAS experiment at the Lar ...

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