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 pair production of up-type vector-like quarks and for four-top-quark events in final states with multiple b-jets with the ATLAS detector

Abstract: A search for pair production of up-type vector-like quarks (T) with a significant branching ratio into a top quark and either a Standard Model Higgs boson or a Z boson is presented. The same analys ...

Search for top-squark pair production in final states with one lepton, jets, and missing transverse momentum using 36 fb−1 of s√=13 TeV pp collision data with the ATLAS detector

Abstract: The results of a search for the direct pair production of top squarks, the supersymmetric partner of the top quark, in final states with one isolated electron or muon, several energetic jets, and missing transverse momentum are reported. The analysis also targets spin-0 mediator models, where the mediator decays into a pair of dark-matter particles and is produced in association with a pair of top quarks. The search uses data from proton-proton collisions delivered by the Large Hadron Collider in 2015 and 2016 at a centre-of-mass energy of $ \sqrt{s}=13 $ TeV and recorded by the ATLAS detector, corresponding to an integrated luminosity of 36 fb$^{−1}$. A wide range of signal scenarios with different mass-splittings between the top squark, the lightest neutralino and possible intermediate supersymmetric particles are considered, including cases where the W bosons or the top quarks produced in the decay chain are off-shell. No significant excess over the Standard Model prediction is observed. The null results are used to set exclusion limits at 95% confidence level in several supersymmetry benchmark models. For pair-produced top-squarks decaying into top quarks, top-squark masses up to 940 GeV are excluded. Stringent exclusion limits are also derived for all other considered top-squark decay scenarios. For the spin-0 mediator models, upper limits are set on the visible cross-section.

Search for heavy resonances decaying into a W or Z boson and a Higgs boson in final states with leptons and b-jets in 36 fb -1 of √{s}=13 TeV pp collisions with the ATLAS detector

Abstract: A search is conducted for new resonances decaying into a $W$ or $Z$ boson and a 125 GeV Higgs boson in the $ u\bar{ u}b\bar{b}$, $\ell^{\pm}{ u}b\bar{b}$, and $\ell^+\ell^-b\bar{b}$ final states, where $\ell ^{\pm}= e^{\pm}$ or $\mu^{\pm}$, in $pp$ collisions at $\sqrt s = 13$ TeV. The data used correspond to a total integrated luminosity of 36.1 fb$^{-1}$ collected with the ATLAS detector at the Large Hadron Collider during the 2015 and 2016 data-taking periods. The search is conducted by examining the reconstructed invariant or transverse mass distributions of $Wh$ and $Zh$ candidates for evidence of a localised excess in the mass range of 220 GeV up to 5 TeV. No significant excess is observed and the results are interpreted in terms of constraints on the production cross-section times branching fraction of heavy $W^\prime$ and $Z^\prime$ resonances in heavy-vector-triplet models and the CP-odd scalar boson $A$ in two-Higgs-doublet models. Upper limits are placed at the 95 % confidence level and range between $9.0\times 10^{-4}$ pb and $8.1\times 10^{-1}$ pb depending on the model and mass of the resonance.

Search for anomalous production of prompt same-sign lepton pairs and pair-produced doubly charged Higgs bosons with s =8 TeV pp collisions using the ATLAS detector

Abstract: A low-background inclusive search for new physics in events with same-sign dileptons is presented The search uses proton-proton collisions corresponding to 203 fb−1 of integrated luminosity taken in 2012 at a centre-of-mass energy of 8 TeV with the ATLAS detector at the LHC Pairs of isolated leptons with the same electric charge and large transverse momenta of the type e±e±, e±μ±, and μ±μ± are selected and their invariant mass distribution is examined No excess of events above the expected level of Standard Model background is found The results are used to set upper limits on the cross-sections for processes beyond the Standard Model Limits are placed as a function of the dilepton invariant mass within a fiducial region corresponding to the signal event selection criteria Exclusion limits are also derived for a specific model of doubly charged Higgs boson production

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