Hal Evans

Bio: Hal Evans is an academic researcher from Indiana University. The author has contributed to research in topics: Large Hadron Collider & Lepton. The author has an hindex of 141, co-authored 1445 publications receiving 107406 citations. Previous affiliations of Hal Evans include Queen's University & University of Cambridge.

The ATLAS Experiment at the CERN Large Hadron Collider

Abstract: The ATLAS detector as installed in its experimental cavern at point 1 at CERN is described in this paper. A brief overview of the expected performance of the detector when the Large Hadron Collider begins operation is also presented.

Direct evidence for neutrino flavor transformation from neutral current interactions in the Sudbury Neutrino Observatory

Abstract: Observations of neutral-current nu interactions on deuterium in the Sudbury Neutrino Observatory are reported. Using the neutral current (NC), elastic scattering, and charged current reactions and assuming the standard 8B shape, the nu(e) component of the 8B solar flux is phis(e) = 1.76(+0.05)(-0.05)(stat)(+0.09)(-0.09)(syst) x 10(6) cm(-2) s(-1) for a kinetic energy threshold of 5 MeV. The non-nu(e) component is phi(mu)(tau) = 3.41(+0.45)(-0.45)(stat)(+0.48)(-0.45)(syst) x 10(6) cm(-2) s(-1), 5.3sigma greater than zero, providing strong evidence for solar nu(e) flavor transformation. The total flux measured with the NC reaction is phi(NC) = 5.09(+0.44)(-0.43)(stat)(+0.46)(-0.43)(syst) x 10(6) cm(-2) s(-1), consistent with solar models.

Combined Measurement of the Higgs Boson Mass in pp Collisions at √s=7 and 8 TeV with the ATLAS and CMS Experiments

Abstract: A measurement of the Higgs boson mass is presented based on the combined data samples of the ATLAS and CMS experiments at the CERN LHC in the H→γγ and H→ZZ→4l decay channels. The results are obtained from a simultaneous fit to the reconstructed invariant mass peaks in the two channels and for the two experiments. The measured masses from the individual channels and the two experiments are found to be consistent among themselves. The combined measured mass of the Higgs boson is mH=125.09±0.21 (stat)±0.11 (syst) GeV.

Measurement of the rate of ve + d → p + p + e- interactions produced by 8B solar neutrinos at the sudbury neutrino observatory

Abstract: Solar neutrinos from the decay of 8B have been detected at the Sudbury Neutrino Observatory (SNO) via the charged current (CC) reaction on deuterium and by the elastic scattering (ES) of electrons. The CC reaction is sensitive exclusively to νe, while the ES reaction also has a small sensitivity to νμ and ντ. The flux of νe from 8B decay measured by the CC reaction rate is φCC(ν e )=[1.75±0.07(stat.) −0.11 +0.12 (syst.)×0.05(theor.)]×106cm−2s−1. Assuming no flavor transformation, the flux inferred from the ES reaction rate is φES(ν x )=[2.39±0.34(stat.) −0.14 +0.16 (syst.)]×106cm−2s−1. Comparison of φCC(νe) to the Super-Kamiokande collaboration’s precision value of φES(νx) yields a 3.3σ difference, assuming the systematic uncertainties are normally distributed, providing evidence that there is a nonelectron flavor active neutrino component in the solar flux. The total flux of active 8B neutrinos is thus determined to be (5.44±0.99)×106 cm−2 s−1, in close agreement with the predictions of solar models.

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