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 anomalous production of prompt like-sign lepton pairs at √s =7 TeV with the ATLAS detector

Abstract: An inclusive search for anomalous production of two prompt, isolated leptons with the same electric charge is presented. The search is performed in a data sample corresponding to 4.7 fb(-1) of integrated luminosity collected in 2011 at root s = 7TeV with the ATLAS detector at the LHC. Pairs of leptons (e(+/-)e(+/-), e(+/-)mu(+/-), and mu(+/-)mu(+/-)) with large transverse momentum are selected, and the dilepton invariant mass distribution is examined for any deviation from the Standard Model expectation. No excess is found, and upper limits on the production cross section of like-sign lepton pairs from physics processes beyond the Standard Model are placed as a function of the dilepton invariant mass within a fiducial region close to the experimental selection criteria. The 95% confidence level upper limits on the cross section of anomalous e(+/-)e(+/-), e(+/-)mu(+/-), or mu(+/-)mu(+/-) production range between 1.7 fb and 64 fb depending on the dilepton mass and flavour combination.

Measurement of inclusive two-particle angular correlations in pp collisions with the atlas detector at the LHC

Abstract: We present a measurement of two-particle angular correlations in proton-proton collisions at root s = 900 GeV and 7 TeV. The collision events were collected during 2009 and 2010 with the ATLAS detector at the Large Hadron Collider using a single-arm minimum bias trigger. Correlations are measured for charged particles produced in the kinematic range of transverse momentum p(T) > 100 MeV and pseudorapidity vertical bar eta vertical bar < 2.5. A complex structure in pseudorapidity and azimuth is observed at both collision energies. Results are compared to PYTHIA 8 and HERWIG++ as well as to the AMBT2B, DW and Perugia 2011 tunes of PYTHIA 6. The data are not satisfactorily described by any of these models.

Exclusive dimuon production in ultraperipheral Pb + Pb collisions at √sNN= 5.02 TeV with ATLAS

Abstract: Exclusive dimuon production in ultraperipheral collisions (UPC), resulting from photon-photon interactions in the strong electromagnetic fields of colliding high-energy lead nuclei, $\mathrm{PbPb}(\gamma\gamma) \rightarrow \mu^+\mu^- (\mathrm{Pb}^{(\star)}\mathrm{Pb}^{(\star)} )$, is studied using $\mathcal{L}_{\mathrm{int}} = 0.48$ nb$^{-1}$ of $\sqrt{s_\mathrm{NN}}=5.02$ TeV lead-lead collision data at the LHC with the ATLAS detector. Dimuon pairs are measured in the fiducial region $p_{\mathrm{T}\mu} > 4$ GeV, $|\eta_{\mu}| 10$ GeV, and $p_{\mathrm{T,\mu\mu}} < 2$ GeV. The primary background from single-dissociative processes is extracted from the data using a template fitting technique. Differential cross sections are presented as a function of $m_{\mu\mu}$, absolute pair rapidity ($|y_{\mu\mu}|$), scattering angle in the dimuon rest frame ($|\cos \vartheta^{\star}_{\mu\mu}|$) and the colliding photon energies. The total cross section of the UPC $\gamma \gamma \rightarrow \mu^{+}\mu^{-}$ process in the fiducial volume is measured to be $\sigma_{\mathrm{fid}}^{\mu\mu} = 34.1 \! \pm \! 0.3 \mathrm{(stat.)} \! \pm \! 0.7 \mathrm{(syst.)}$ $\mu\mathrm{b}$. Generally good agreement is found with calculations from STARlight, which incorporate the leading-order Breit-Wheeler process with no final-state effects, albeit differences between the measurements and theoretical expectations are observed. In particular, the measured cross sections at larger $|y_{\mu\mu}|$ are found to be about 10-20% larger in data than in the calculations, suggesting the presence of larger fluxes of photons in the initial state. Modification of the dimuon cross sections in the presence of forward and/or backward neutron production is also studied and is found to be associated with a harder incoming photon spectrum, consistent with expectations.

Search for phenomena beyond the Standard Model in events with large b-jet multiplicity using the ATLAS detector at the LHC

Abstract: A search is presented for new phenomena in events characterised by high jet multiplicity, no leptons (electrons or muons), and four or more jets originating from the fragmentation of b-quarks (b-jets). The search uses $139~\hbox {fb}^{-1}$ of $\sqrt{s}$ = 13 TeV proton–proton collision data collected by the ATLAS experiment at the Large Hadron Collider during Run 2. The dominant Standard Model background originates from multijet production and is estimated using a data-driven technique based on an extrapolation from events with low b-jet multiplicity to the high b-jet multiplicities used in the search. No significant excess over the Standard Model expectation is observed and 95% confidence-level limits that constrain simplified models of R-parity-violating supersymmetry are determined. The exclusion limits reach 950 GeV in top-squark mass in the models considered.

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