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.

Combination of the searches for pair-produced vectorlike partners of the third-generation quarks at s =13 TeV with the ATLAS detector

Abstract: A combination of the searches for pair-produced vectorlike partners of the top and bottom quarks in various decay channels (T -> Zt/Wb/Ht, B -> Zb/Wt/Hb) is performed using 36.1 fb(-1) of pp ...

Technical Design Report for the Phase-I Upgrade of the ATLAS TDAQ System

Abstract: The Phase-I upgrade of the ATLAS Trigger and Data Acquisition (TDAQ) system is to allow the ATLAS experiment to efficiently trigger and record data at instantaneous luminosities that are up to three times that of the original LHC design while maintaining trigger thresholds close to those used in the initial run of the LHC.

Measurement of the inclusive jet cross-section in proton-proton collisions at √s=7 $$ TeV using 4.5 fb−1 of data with the ATLAS detector

Abstract: The inclusive jet cross-section is measured in proton-proton collisions at a centre-of-mass energy of 7 TeV using a data set corresponding to an integrated luminosity of 4.5 fb−1 collected with the ATLAS detector at the Large Hadron Collider in 2011. Jets are identified using the anti-k t algorithm with radius parameter values of 0.4 and 0.6. The double-differential cross-sections are presented as a function of the jet transverse momentum and the jet rapidity, covering jet transverse momenta from 100 GeV to 2 TeV. Next-to-leading-order QCD calculations corrected for non-perturbative effects and electroweak effects, as well as Monte Carlo simulations with next-to-leading-order matrix elements interfaced to parton showering, are compared to the measured cross-sections. A quantitative comparison of the measured cross-sections to the QCD calculations using several sets of parton distribution functions is performed.

Measurement of long-range pseudorapidity correlations and azimuthal harmonics in √sNN =5.02 TeV proton-lead collisions with the ATLAS detector

Abstract: Measurements of two-particle correlation functions and the first five azimuthal harmonics, v(1) to v(5), are presented, using 28 nb(-1) of p + Pb collisions at a nucleon-nucleon center-of-mass energy of root s(NN) = 5.02 TeV measured with the ATLAS detector at the LHC. Significant long-range "ridgelike" correlations are observed for pairs with small relative azimuthal angle (|Delta phi| 2 pi/3) over the transverse momentum range 0.4 4 GeV. The v(2)(p(T)), v(3)(p(T)), and v(4)(p(T)) are compared to the v(n) coefficients in Pb + Pb collisions at root s(NN) = 2.76 TeV with similar event multiplicities. Reasonable agreement is observed after accounting for the difference in the average p(T) of particles produced in the two collision systems.

Measurement of the inclusive isolated prompt photon cross section in pp collisions at √s=7TeV with the ATLAS detector

Abstract: A measurement of the cross section for the inclusive production of isolated prompt photons in pp collisions at a center-of-mass energy root s = 7 TeV is presented. The measurement covers the pseudorapidity ranges vertical bar eta(gamma)vertical bar < 1: 37 and 1: 52 <= vertical bar eta(gamma)vertical bar < 1: 81 in the transverse energy range 15 <= E-T(gamma) < 100 GeV. The results are based on an integrated luminosity of 880 nb(-1), collected with the ATLAS detector at the Large Hadron Collider. Photon candidates are identified by combining information from the calorimeters and from the inner tracker. Residual background in the selected sample is estimated from data based on the observed distribution of the transverse isolation energy in a narrow cone around the photon candidate. The results are compared to predictions from next-to-leading-order perturbative QCD calculations.

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