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.

Observation of Long-Range Elliptic Azimuthal Anisotropies in s =13 and 2.76 TeV pp Collisions with the ATLAS Detector

Abstract: ATLAS has measured two-particle correlations as a function of relative azimuthal-angle, $\Delta \phi$, and pseudorapidity, $\Delta \eta$, in $\sqrt{s}$=13 and 2.76 TeV $pp$ collisions at the LHC using charged particles measured in the pseudorapidity interval $|\eta|$<2.5. The correlation functions evaluated in different intervals of measured charged-particle multiplicity show a multiplicity-dependent enhancement at $\Delta \phi \sim 0$ that extends over a wide range of $\Delta\eta$, which has been referred to as the "ridge". Per-trigger-particle yields, $Y(\Delta \phi)$, are measured over 2<$|\Delta\eta|$<5. For both collision energies, the $Y(\Delta \phi)$ distribution in all multiplicity intervals is found to be consistent with a linear combination of the per-trigger-particle yields measured in collisions with less than 20 reconstructed tracks, and a constant combinatoric contribution modulated by $\cos{(2\Delta \phi)}$. The fitted Fourier coefficient, $v_{2,2}$, exhibits factorization, suggesting that the ridge results from per-event $\cos{(2\phi)}$ modulation of the single-particle distribution with Fourier coefficients $v_2$. The $v_2$ values are presented as a function of multiplicity and transverse momentum. They are found to be approximately constant as a function of multiplicity and to have a $p_{\mathrm{T}}$ dependence similar to that measured in $p$+Pb and Pb+Pb collisions. The $v_2$ values in the 13 and 2.76 TeV data are consistent within uncertainties. These results suggest that the ridge in $pp$ collisions arises from the same or similar underlying physics as observed in $p$+Pb collisions, and that the dynamics responsible for the ridge has no strong $\sqrt{s}$ dependence.

Measurement of the inclusive jet cross section in pp̄ collisions at s=1.96TeV

Abstract: We report on a measurement of the inclusive jet cross section in p (p) over bar collisions at a center-of-mass energy root s = 1.96 TeV using data collected by the D0 experiment at the Fermilab Tevatron Collider corresponding to an integrated luminosity of 0: 70 fb(-1). The data cover jet transverse momenta from 50 to 600 GeV and jet rapidities in the range -2.4 to 2.4. Detailed studies of correlations between systematic uncertainties in transverse momentum and rapidity are presented, and the cross section measurements are found to be in good agreement with next-to-leading order QCD calculations.

Luminosity Determination in pp Collisions at sqrt(s)=7 TeV Using the ATLAS Detector at the LHC

Abstract: Measurements of luminosity obtained using the ATLAS detector during early running of the Large Hadron Collider (LHC) at root s = 7 TeV are presented. The luminosity is independently determined using several detectors and multiple algorithms, each having different acceptances, systematic uncertainties and sensitivity to background. The ratios of the luminosities obtained from these methods are monitored as a function of time and of mu, the average number of inelastic interactions per bunch crossing. Residual time- and mu-dependence between the methods is less than 2% for 0 < mu < 2.5. Absolute luminosity calibrations, performed using beam separation scans, have a common systematic uncertainty of +/- 11%, dominated by the measurement of the LHC beam currents. After calibration, the luminosities obtained from the different methods differ by at most +/- 2%. The visible cross sections measured using the beam scans are compared to predictions obtained with the PYTHIA and PHOJET event generators and the ATLAS detector simulation.

Measurement of the muon reconstruction performance of the ATLAS detector using 2011 and 2012 LHC proton–proton collision data

Abstract: This paper presents the performance of the ATLAS muon reconstruction during the LHC run with pp collisions at root s = 7-8 TeV in 2011-2012, focusing mainly on data collected in 2012. Measurements ...

Search for dark matter candidates and large extra dimensions in events with a jet and missing transverse momentum with the ATLAS detector

Abstract: A search for new phenomena in events with a high-energy jet and large missing transverse momentum is performed using data from proton-proton collisions at root s = 7 TeV with the ATLAS experiment at the Large flatiron Collider. Four kinematic regions are explored using a dataset corresponding to an integrated luminosity of 4.7 fb(-1). No excess of events beyond expectations from Standard Model processes is observed, and limits are set on large extra dimensions and the pair production of dark matter particles.

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