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 azimuthal anisotropy of charged-particle production in Xe+Xe collisions at √sNN =5.44 TeV with the ATLAS detector

Abstract: This paper describes the measurements of flow harmonics v2-v6 in 3μb-1 of Xe+Xe collisions at sNN=5.44 TeV performed using the ATLAS detector at the Large Hadron Collider (LHC). Measurements of the centrality, multiplicity, and pT dependence of the vn obtained using two-particle correlations and the scalar product technique are presented. The measurements are also performed using a template-fit procedure, which was developed to remove nonflow correlations in small collision systems. This nonflow removal is shown to have a significant influence on the measured vn at high pT, especially in peripheral events. Comparisons of the measured vn with measurements in Pb+Pb collisions and p+Pb collisions at sNN=5.02 TeV are also presented. The vn values in Xe+Xe collisions are observed to be larger than those in Pb+Pb collisions for n=2, 3, and 4 in the most central events. However, with decreasing centrality or increasing harmonic order n, the vn values in Xe+Xe collisions become smaller than those in Pb+Pb collisions. The vn in Xe+Xe and Pb+Pb collisions are also compared as a function of the mean number of participating nucleons, (Npart), and the measured charged-particle multiplicity in the detector. The v3 values in Xe+Xe and Pb+Pb collisions are observed to be similar at the same (Npart) or multiplicity, but the other harmonics are significantly different. The ratios of the measured vn in Xe+Xe and Pb+Pb collisions, as a function of centrality, are also compared to theoretical calculations.

Search for doubly charged higgs boson pair production in pp̄ collisions at √s=1.96TeV

Abstract: We present a search for pair production of doubly charged Higgs bosons in the processes $q\overline{q}\ensuremath{\rightarrow}{H}^{++}{H}^{--}$ decaying through ${H}^{\ifmmode\pm\else\textpm\fi{}\ifmmode\pm\else\textpm\fi{}}\ensuremath{\rightarrow}{\ensuremath{\tau}}^{\ifmmode\pm\else\textpm\fi{}}{\ensuremath{\tau}}^{\ifmmode\pm\else\textpm\fi{}},{\ensuremath{\mu}}^{\ifmmode\pm\else\textpm\fi{}}{\ensuremath{\tau}}^{\ifmmode\pm\else\textpm\fi{}},{\ensuremath{\mu}}^{\ifmmode\pm\else\textpm\fi{}}{\ensuremath{\mu}}^{\ifmmode\pm\else\textpm\fi{}}$. The search is performed in $p\overline{p}$ collisions at a center-of-mass energy of $\sqrt{s}=1.96\text{ }\text{ }\mathrm{TeV}$ using an integrated luminosity of up to $7.0\text{ }\text{ }{\mathrm{fb}}^{\ensuremath{-}1}$ collected by the D0 experiment at the Fermilab Tevatron Collider. The results are used to set 95% C.L. limits on the pair production cross section of doubly charged Higgs bosons and on their mass for different ${H}^{\ifmmode\pm\else\textpm\fi{}\ifmmode\pm\else\textpm\fi{}}$ branching fractions. Models predicting different ${H}^{\ifmmode\pm\else\textpm\fi{}\ifmmode\pm\else\textpm\fi{}}$ decays are investigated. Assuming $\mathcal{B}({H}^{\ifmmode\pm\else\textpm\fi{}\ifmmode\pm\else\textpm\fi{}}\ensuremath{\rightarrow}{\ensuremath{\tau}}^{\ifmmode\pm\else\textpm\fi{}}{\ensuremath{\tau}}^{\ifmmode\pm\else\textpm\fi{}})=1$ yields an observed (expected) lower limit on the mass of a left-handed ${H}_{L}^{\ifmmode\pm\else\textpm\fi{}\ifmmode\pm\else\textpm\fi{}}$ boson of 128 (116) GeV and assuming $\mathcal{B}({H}^{\ifmmode\pm\else\textpm\fi{}\ifmmode\pm\else\textpm\fi{}}\ensuremath{\rightarrow}{\ensuremath{\mu}}^{\ifmmode\pm\else\textpm\fi{}}{\ensuremath{\tau}}^{\ifmmode\pm\else\textpm\fi{}})=1$ the corresponding limits are 144 (149) GeV. In a model with $\mathcal{B}({H}^{\ifmmode\pm\else\textpm\fi{}\ifmmode\pm\else\textpm\fi{}}\ensuremath{\rightarrow}{\ensuremath{\tau}}^{\ifmmode\pm\else\textpm\fi{}}{\ensuremath{\tau}}^{\ifmmode\pm\else\textpm\fi{}})=\mathcal{B}({H}^{\ifmmode\pm\else\textpm\fi{}\ifmmode\pm\else\textpm\fi{}}\ensuremath{\rightarrow}{\ensuremath{\mu}}^{\ifmmode\pm\else\textpm\fi{}}{\ensuremath{\tau}}^{\ifmmode\pm\else\textpm\fi{}})=\mathcal{B}({H}^{\ifmmode\pm\else\textpm\fi{}\ifmmode\pm\else\textpm\fi{}}\ensuremath{\rightarrow}{\ensuremath{\mu}}^{\ifmmode\pm\else\textpm\fi{}}{\ensuremath{\mu}}^{\ifmmode\pm\else\textpm\fi{}})=1/3$, we obtain $M({H}_{L}^{\ifmmode\pm\else\textpm\fi{}\ifmmode\pm\else\textpm\fi{}})g130\text{ }(138)\text{ }\text{ }\mathrm{GeV}$.

Multivariate searches for single top quark production with the D0 detector

Abstract: We present a search for electroweak production of single top quarks in (p{bar p} {yields} t{bar b} + X) and t-channel (p{bar p} {yields} tq{bar b} + X) modes We have analyzed 230 pb{sup -1} of data collected with the D0 detector at the Fermilab Tevatron collider at a center-of-mass energy of {radical}s = 196 TeV Two separate analysis methods are used: neural networks and a cut-based analysis No evidence for a single top quark signal is found We set 95% confidence level upper limits on the production cross sections using Bayesian statistics, based on event counts and binned likelihoods formed from the neural network output The limits from the neural network (cut-based) analysis are 64 pb (106 pb) in the s-channel and 50 pb (113 pb) in the t-channel

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