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.

Muon reconstruction and identification efficiency in ATLAS using the full Run 2 pp collision data set at √ s = 13 TeV

Abstract: This article documents the muon reconstruction and identification efficiency obtained by the ATLAS experiment for 139 $$\hbox {fb}^{-1}$$ fb - 1 of pp collision data at $$\sqrt{s}=13$$ s = 13 TeV collected between 2015 and 2018 during Run 2 of the LHC. The increased instantaneous luminosity delivered by the LHC over this period required a reoptimisation of the criteria for the identification of prompt muons. Improved and newly developed algorithms were deployed to preserve high muon identification efficiency with a low misidentification rate and good momentum resolution. The availability of large samples of $$Z\rightarrow \mu \mu $$ Z → μ μ and $$J/\psi \rightarrow \mu \mu $$ J / ψ → μ μ decays, and the minimisation of systematic uncertainties, allows the efficiencies of criteria for muon identification, primary vertex association, and isolation to be measured with an accuracy at the per-mille level in the bulk of the phase space, and up to the percent level in complex kinematic configurations. Excellent performance is achieved over a range of transverse momenta from 3 GeV to several hundred GeV, and across the full muon detector acceptance of $$|\eta |<2.7$$ | η | < 2.7 .

Search for charged Higgs bosons in the H± → tb decay channel in pp collisions at √s = 8 TeV using the ATLAS detector

Abstract: Charged Higgs bosons heavier than the top quark and decaying via H-+/- -> tb are searched for in proton-proton collisions measured with the ATLAS experiment at root s = 8 TeV corresponding to an ...

Search for heavy Majorana or Dirac neutrinos and right-handed W gauge bosons in final states with two charged leptons and two jets at s√=13 TeV with the ATLAS detector

Abstract: A search for heavy right-handed Majorana or Dirac neutrinos N (R) and heavy right-handed gauge bosons W (R) is performed in events with a pair of energetic electrons or muons, with the same or oppo ...

Direct observation of the strange b Baryon Ξb

Abstract: We report the first direct observation of the strange b baryon Xi(b)- (Xi(b)+). We reconstruct the decay Xi(b)- -->J/psiXi-, with J/psi-->mu+mu-, and Xi--->Lambdapi--->ppi-pi- in pp collisions at square root of s =1.96 TeV. Using 1.3 fb(-1) of data collected by the D0 detector, we observe 15.2 +/- 4.4(stat)(-0.4)(+1.9)(syst) Xi(b)- candidates at a mass of 5.774 +/- 0.011(stat) +/- 0.015(syst) GeV. The significance of the observed signal is 5.5 sigma, equivalent to a probability of 3.3 x 10(-8) of it arising from a background fluctuation. Normalizing to the decay Lambda(b)-->J/psiLambda, we measure the relative rate sigma(Xi(b-) x B(Xi)b})- -->J/psiXi-)/sigma(Lambda(b)) x B(Lambda(b)-->J/psiLambda) = 0.28+/-0.09(stat)(-0.08)(+0.09)(syst).

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