Brad Abbott

Bio: Brad Abbott is an academic researcher from University of Oklahoma. The author has contributed to research in topics: Large Hadron Collider & Higgs boson. The author has an hindex of 137, co-authored 1566 publications receiving 98604 citations. Previous affiliations of Brad Abbott include Aix-Marseille University & Purdue University.

Measurement of top quark pair differential cross sections in the dilepton channel in pp collisions at √s=7 and 8 TeV with ATLAS

Abstract: Measurements of normalized differential cross-sections of top quark pair ($t\bar t$) production are presented as a function of the mass, the transverse momentum and the rapidity of the $t\bar t$ system in proton-proton collisions at center-of-mass energies of $\sqrt{s}$ = 7 TeV and 8 TeV. The dataset corresponds to an integrated luminosity of 4.6 fb$^{-1}$ at 7 TeV and 20.2 fb$^{-1}$ at 8 TeV, recorded with the ATLAS detector at the Large Hadron Collider. Events with top quark pair signatures are selected in the dilepton final state, requiring exactly two charged leptons and at least two jets with at least one of the jets identified as likely to contain a $b$-hadron. The measured distributions are corrected for detector effects and selection efficiency to cross-sections at the parton level. The differential cross-sections are compared with different Monte Carlo generators and theoretical calculations of $t\bar t$ production. The results are consistent with the majority of predictions in a wide kinematic range.

Search for heavy pointlike dirac monopoles

Abstract: We have searched for central production of a pair of photons with high transverse energies in p{bar p} collisions at {radical}(s)=1.8 TeV using 70 pb{sup {minus}1} of data collected with the D0 detector at the Fermilab Tevatron in 1994{endash}1996. If they exist, virtual heavy pointlike Dirac monopoles could rescatter pairs of nearly real photons into this final state via a box diagram. We observe no excess of events above background, and set lower 95{percent} C.L. limits of 610, 870, or 1580 GeV/c{sup 2} on the mass of a spin 0, 1/2 , or 1 Dirac monopole. {copyright} {ital 1998} {ital The American Physical Society}

Model-independent measurement of the $W$ boson helicity in top quark decays at D0

Abstract: We present the first model-independent measurement of the helicity of $W$ bosons produced in top quark decays, based on a 1 fb$^{-1}$ sample of candidate $t\bar{t}$ events in the dilepton and lepton plus jets channels collected by the D0 detector at the Fermilab Tevatron $p\bar{p}$ Collider. We reconstruct the angle $\theta^*$ between the momenta of the down-type fermion and the top quark in the $W$ boson rest frame for each top quark decay. A fit of the resulting \costheta distribution finds that the fraction of longitudinal $W$ bosons $f_0 = 0.425 \pm 0.166 \hbox{(stat.)} \pm 0.102 \hbox{(syst.)}$ and the fraction of right-handed $W$ bosons $f_+ = 0.119 \pm 0.090 \hbox{(stat.)} \pm 0.053 \hbox{(syst.)}$, which is consistent at the 30% C.L. with the standard model.

Search for dark matter in association with a Higgs boson decaying to two photons at s=13 TeV with the ATLAS detector

Abstract: A search for dark matter in association with a Higgs boson decaying to two photons is presented. This study is based on data collected with the ATLAS detector, corresponding to an integrated lumino ...

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