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.

Search for the Standard Model Higgs boson decaying into bb¯ produced in association with top quarks decaying hadronically in pp collisions at √s = 8 TeV with the ATLAS detector

Abstract: A search for Higgs boson production in association with a pair of top quarks (t (t) over barH) is performed, where the Higgs boson decays to b (b) over bar, and both top quarks decay hadronically. ...

Measurement of the Λb0 lifetime using semileptonic decays

Abstract: We report a measurement of the Bs(0) lifetime in the semileptonic decay channel Bs(0) --> Ds- mu+ nuX (and its charge conjugate), using approximately 0.4 fb(-1) of data collected with the D0 detector during 2002-2004. Using 5176 reconstructed Ds- mu+ signal events, we have measured the Bs(0) lifetime to be tau(Bs(0))=1.398+/-0.044(stat)(-0.025)(+0.028)(syst) ps. This is the most precise measurement of the Bs(0) lifetime to date.

A measurement of the ratio of the production cross sections for W and Z bosons in association with jets with the ATLAS detector

Abstract: The ratio of the production cross sections for W and Z bosons in association with jets has been measured in proton-proton collisions at root s = 7 TeV with the ATLAS experiment at the Large Hadron Collider. The measurement is based on the entire 2011 dataset, corresponding to an integrated luminosity of 4.6 fb(-1). Inclusive and differential cross-section ratios for massive vector bosons decaying to electrons and muons are measured in association with jets with transverse momentum p(T) > 30 GeV and jet rapidity vertical bar y vertical bar < 4.4. The measurements are compared to next-to-leading-order perturbative QCD calculations and to predictions from different Monte Carlo generators implementing leading-order matrix elements supplemented by parton showers.

Measurement of the W boson production charge asymmetry in pp̄→W+X→eν+X events at √s=1.96 TeV.

Abstract: We present a measurement of the W boson production charge asymmetry in p (p) over bar -> W + X -> ev + X events at a center of mass energy of 1.96 TeV, using 9.7 fb(-1) of integrated luminosity collected with the D0 detector at the Fermilab Tevatron Collider. The neutrino longitudinal momentum is determined by using a neutrino weighting method, and the asymmetry is measured as a function of the W boson rapidity. The measurement extends over wider electron pseudorapidity region than previous results and is the most precise to date, allowing for precise determination of proton parton distribution functions in global fits.

Ratio of isolated photon cross sections in p(p)over-bar collisions at root s=630 and 1800 GeV

Abstract: The inclusive cross section for production of isolated photons has been measured in p (p) over bar collisions at roots = 630 GeV with the DO detector at the Fermilab Tevatron Collider. The photons span a transverse energy (E-T) range from 7-49 GeV and have pseudorapidity \eta\ s = 1800 GeV to form a ratio of the cross sections. Comparison of next-to-leading-order QCD with the measured cross section at 630 GeV and the ratio of cross sections show satisfactory agreement in most of the E-T range.

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