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 the B → J/ψK*(892) decay amplitudes

Abstract: We present a measurement of the decay amplitudes in B-->J/psi K^*(892) channels using 20.7 fb-1 of data collected at the Upsilon(4S) resonance with the BABAR detector at PEP-II. We measure a P-wave fraction R_perp = (16.0+/-3.2+/-1.4)% and a longitudinal polarization fraction (59.7+/-2.8+/-2.4)%. The measurement of a relative phase that is neither 0 nor pi, phi_ll = 2.50+/-0.20+/-0.08 radians, favors a departure from the factorization hypothesis. Although the decay B-->J/psi Kpi proceeds mainly via K^*(892), there is also evidence for K^*_2(1430) and Kpi S-wave contributions.

Search for a Structure in the B-s(0) pi(+/-) Invariant Mass Spectrum with the ATLAS Experiment

Abstract: A search for the narrow structure, X(5568), reported by the D0 Collaboration in the decay sequence X→B_{s}^{0}π^{±}, B_{s}^{0}→J/ψϕ, is presented The analysis is based on a data sample recorded with the ATLAS detector at the LHC corresponding to 49 fb^{-1} of pp collisions at 7 TeV and 195 fb^{-1} at 8 TeV No significant signal was found Upper limits on the number of signal events, with properties corresponding to those reported by D0, and on the X production rate relative to B_{s}^{0} mesons, ρ_{X}, were determined at 95% confidence level The results are N(X)<382 and ρ_{X}<0015 for B_{s}^{0} mesons with transverse momenta above 10 GeV, and N(X)<356 and ρ_{X}<0016 for transverse momenta above 15 GeV Limits are also set for potential B_{s}^{0}π^{±} resonances in the mass range 5550 to 5700 MeV

Measurement of the cross-section and charge asymmetry of W bosons produced in proton–proton collisions at s√=8 TeV with the ATLAS detector

Abstract: This paper presents measurements of the $W^+ \rightarrow \mu ^+ u $ and $W^- \rightarrow \mu ^- u $ cross-sections and the associated charge asymmetry as a function of the absolute pseudorapidity of the decay muon. The data were collected in proton–proton collisions at a centre-of-mass energy of 8 $\text {TeV}$ with the ATLAS experiment at the LHC and correspond to a total integrated luminosity of $20.2~\text{ fb }^{-1}$ . The precision of the cross-section measurements varies between 0.8 and 1.5% as a function of the pseudorapidity, excluding the 1.9% uncertainty on the integrated luminosity. The charge asymmetry is measured with an uncertainty between 0.002 and 0.003. The results are compared with predictions based on next-to-next-to-leading-order calculations with various parton distribution functions and have the sensitivity to discriminate between them.

Measurement of spin correlation in tt̄ production using dilepton final states

Abstract: We measure the correlation between the spin of the top quark and the spin of the anti-top quark in (ttbar -> W+ W- b bbar -> l+ nu b l- nubar bbar) final states produced in ppbar collisions at a center of mass energy sqrt(s)=1.96 TeV, where l is an electron or muon. The data correspond to an integrated luminosity of 5.4 fb-1 and were collected with the D0 detector at the Fermilab Tevatron collider. The correlation is extracted from the angles of the two leptons in the t and tbar rest frames, yielding a correlation strength C= 0.10^{+0.45}_{-0.45}, in agreement with the NLO QCD prediction within two standard deviations, but also in agreement with the no correlation hypothesis.

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