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 long-lived, massive particles in events with a displaced vertex and a muon with large impact parameter in pp collisions at s =13 TeV with the ATLAS detector

Abstract: A search for long-lived particles decaying into hadrons and at least one muon is presented. The analysis selects events that pass a muon or missing-transverse-momentum trigger and contain a displaced muon track and a displaced vertex. The analyzed dataset of proton-proton collisions at s=13 TeV was collected with the ATLAS detector and corresponds to 136 fb-1. The search employs dedicated reconstruction techniques that significantly increase the sensitivity to long-lived particle decays that occur in the ATLAS inner detector. Background estimates for Standard Model processes and instrumental effects are extracted from data. The observed event yields are compatible with those expected from background processes. The results are presented as limits at 95% confidence level on model-independent cross sections for processes beyond the Standard Model, and interpreted as exclusion limits in scenarios with pair production of long-lived top squarks that decay via a small R-parity-violating coupling into a quark and a muon. Top squarks with masses up to 1.7 TeV are excluded for a lifetime of 0.1 ns, and masses below 1.3 TeV are excluded for lifetimes between 0.01 ns and 30 ns.

A search for $t\bar{t}$ resonances in the lepton plus jets final state with ATLAS using 4.7 fb$^{-1}$ of $pp$ collisions at $\sqrt{s}$ = 7 TeV

Abstract: A search for new particles that decay into top quark pairs (t (t) over bar) is performed with the ATLAS experiment at the LHC using an integrated luminosity of 4.7 fb(-1) of proton-proton (pp) collision data collected at a center-of-mass energy root s = 7 TeV. In the t (t) over bar) -> WbWb decay, the lepton plus jets final state is used, where one W boson decays leptonically and the other hadronically. The t (t) over bar) system is reconstructed using both small-radius and large-radius jets, the latter being supplemented by a jet substructure analysis. A search for local excesses in the number of data events compared to the Standard Model expectation in the t (t) over bar) invariant mass spectrum is performed. No evidence for a t (t) over bar) resonance is found and 95% credibility-level limits on the production rate are determined for massive states predicted in two benchmark models. The upper limits on the cross section times branching ratio of a narrow Z' resonance range from 5.1 pb for a boson mass of 0.5 TeV to 0.03 pb for a mass of 3 TeV. A narrow leptophobic topcolor Z' resonance with a mass below 1.74 TeV is excluded. Limits are also derived for a broad color-octet resonance with m 15.3%. A Kaluza-Klein excitation of the gluon in a Randall-Sundrum model is excluded for masses below 2.07 TeV.

Search for tb Resonances in Proton-Proton Collisions at root s=7 TeV with the ATLAS Detector

Abstract: This Letter presents a search for tb resonances in 1.04 fb(-1) of LHC proton-proton collision data collected by the ATLAS detector at a center-of-mass energy of 7 TeV. Events with a lepton, missing transverse momentum, and two jets are selected and the invariant mass of the corresponding final state is reconstructed. The search exploits the shape of the tb invariant mass distribution compared to the expected standard model backgrounds. The model of a right-handed W(R)' with standard model-like couplings is chosen as the benchmark model for this search. No statistically significant excess of events is observed in the data, and upper limits on the cross section times the branching ratio of W(R)' resonances at 95% C.L. lie in the range of 6.1-1.0 pb for W(R)' masses ranging from 0.5 to 2.0 TeV. These limits are translated into a lower bound on the allowed right-handed W(R)' mass, giving m(W(R)'))>1.13 TeV at 95% C.L.

Measurement of WW/WZ→ℓνqq′ production with the hadronically decaying boson reconstructed as one or two jets in pp collisions at √s=8 TeV with ATLAS, and constraints on anomalous gauge couplings

Abstract: This paper presents a study of the production of WW or WZ boson pairs, with one W boson decaying to ev or mu v and one W or Z boson decaying hadronically. The analysis uses 20.2 fb(-1) of root s = ...

Search for a Heavy Neutral Particle Decaying to eμ, eτ, or μτ in pp Collisions at √s=8 TeV with the ATLAS Detector

Abstract: This Letter presents a search for a heavy neutral particle decaying into an opposite-sign different-flavor dilepton pair, e(+/-) mu(-/+), e(+/-) tau(-/+), or mu(+/-) tau(-/+) using 20.3 fb(-1) of pp collision data at root s = 8 TeV collected by the ATLAS detector at the LHC. The numbers of observed candidate events are compatible with the standard model expectations. Limits are set on the cross section of new phenomena in two scenarios: the production of (nu) over tilde (tau) in R-parity-violating supersymmetric models and the production of a lepton-flavor-violating Z' vector boson.

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