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Institution

University of Notre Dame

EducationNotre Dame, Indiana, United States
About: University of Notre Dame is a education organization based out in Notre Dame, Indiana, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 22238 authors who have published 55201 publications receiving 2032925 citations. The organization is also known as: University of Notre Dame du Lac & University of Notre Dame, South Bend.


Papers
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Journal ArticleDOI
TL;DR: In this paper, a set of constraints on the dark energy equation-of-state parameter w = P/(rho c(2)) were derived using 60 SNe Ia from the ESSENCE supernova survey.
Abstract: We present constraints on the dark energy equation-of-state parameter, w = P/(rho c(2)), using 60 SNe Ia fromthe ESSENCE supernova survey. We derive a set of constraints on the nature of the dark energy assuming a flat universe. By including constraints on (Omega(M), w) from baryon acoustic oscillations, we obtain a value for a static equation-of-state parameter w = -1:05(-0.12)(+0: 13) (stat 1 sigma) +/- 0: 13 (sys) and Omega(M) = 0:274(-0.020)(+0:033) (stat 1 sigma) with a bestfit chi(2)/dof of 0.96. These results are consistent with those reported by the Supernova Legacy Survey from the first year of a similar program measuring supernova distances and redshifts. We evaluate sources of systematic error that afflict supernova observations and present Monte Carlo simulations that explore these effects. Currently, the largest systematic with the potential to affect our measurements is the treatment of extinction due to dust in the supernova host galaxies. Combining our set of ESSENCE SNe Ia with the first-results Supernova Legacy Survey SNe Ia, we obtain a joint constraint of w = -1:07(-0: 09)(+0:09) (stat 1 sigma) +/- 0: 13 ( sys), Omega(M) 0:267(-0:028)(+0:028) (stat 1 sigma) with a best-fit chi(2)/dof of 0.91. The current global SN Ia data alone rule out empty (Omega(M) = 0), matter-only Omega(M) = 0: 3, and Omega(M) = 1 universes at > 4.5 sigma. The current SN Ia data are fully consistent with a cosmological constant.

989 citations

Journal ArticleDOI
TL;DR: Enhanced image resolution and lower noise have been achieved, concurrently with the reduction of helical cone-beam artifacts, as demonstrated by phantom studies and clinical results illustrate the capabilities of the algorithm on real patient data.
Abstract: Multislice helical computed tomography scanning offers the advantages of faster acquisition and wide organ coverage for routine clinical diagnostic purposes. However, image reconstruction is faced with the challenges of three-dimensional cone-beam geometry, data completeness issues, and low dosage. Of all available reconstruction methods, statistical iterative reconstruction (IR) techniques appear particularly promising since they provide the flexibility of accurate physical noise modeling and geometric system description. In this paper, we present the application of Bayesian iterative algorithms to real 3D multislice helical data to demonstrate significant image quality improvement over conventional techniques. We also introduce a novel prior distribution designed to provide flexibility in its parameters to fine-tune image quality. Specifically, enhanced image resolution and lower noise have been achieved, concurrently with the reduction of helical cone-beam artifacts, as demonstrated by phantom studies. Clinical results also illustrate the capabilities of the algorithm on real patient data. Although computational load remains a significant challenge for practical development, superior image quality combined with advancements in computing technology make IR techniques a legitimate candidate for future clinical applications.

987 citations

Journal ArticleDOI
TL;DR: In this article, a generalized Gaussian Markov random field (GGMRF) is proposed for image reconstruction in low-dosage transmission tomography, which satisfies several desirable analytical and computational properties for map estimation, including continuous dependence of the estimate on the data and invariance of the character of solutions to scaling of data.
Abstract: The authors present a Markov random field model which allows realistic edge modeling while providing stable maximum a posterior (MAP) solutions. The model, referred to as a generalized Gaussian Markov random field (GGMRF), is named for its similarity to the generalized Gaussian distribution used in robust detection and estimation. The model satisfies several desirable analytical and computational properties for map estimation, including continuous dependence of the estimate on the data, invariance of the character of solutions to scaling of data, and a solution which lies at the unique global minimum of the a posteriori log-likelihood function. The GGMRF is demonstrated to be useful for image reconstruction in low-dosage transmission tomography. >

978 citations

Book
13 Nov 2009
TL;DR: For certain classes of node distributions, most notably Poisson point processes, and attenuation laws, closed-form results are available, for both the interference itself as well as the signal-to-interference ratios, which determine the network performance.
Abstract: Since interference is the main performance-limiting factor in most wireless networks, it is crucial to characterize the interference statistics. The two main determinants of the interference are the network geometry (spatial distribution of concurrently transmitting nodes) and the path loss law (signal attenuation with distance). For certain classes of node distributions, most notably Poisson point processes, and attenuation laws, closed-form results are available, for both the interference itself as well as the signal-to-interference ratios, which determine the network performance. This monograph presents an overview of these results and gives an introduction to the analytical techniques used in their derivation. The node distribution models range from lattices to homogeneous and clustered Poisson models to general motion-invariant ones. The analysis of the more general models requires the use of Palm theory, in particular conditional probability generating functionals, which are briefly introduced in the appendix.

976 citations

Journal ArticleDOI
G. L. Bayatian, S. Chatrchyan, G. Hmayakyan, Albert M. Sirunyan  +2060 moreInstitutions (143)
TL;DR: In this article, the authors present a detailed analysis of the performance of the Large Hadron Collider (CMS) at 14 TeV and compare it with the state-of-the-art analytical tools.
Abstract: CMS is a general purpose experiment, designed to study the physics of pp collisions at 14 TeV at the Large Hadron Collider (LHC). It currently involves more than 2000 physicists from more than 150 institutes and 37 countries. The LHC will provide extraordinary opportunities for particle physics based on its unprecedented collision energy and luminosity when it begins operation in 2007. The principal aim of this report is to present the strategy of CMS to explore the rich physics programme offered by the LHC. This volume demonstrates the physics capability of the CMS experiment. The prime goals of CMS are to explore physics at the TeV scale and to study the mechanism of electroweak symmetry breaking--through the discovery of the Higgs particle or otherwise. To carry out this task, CMS must be prepared to search for new particles, such as the Higgs boson or supersymmetric partners of the Standard Model particles, from the start-up of the LHC since new physics at the TeV scale may manifest itself with modest data samples of the order of a few fb−1 or less. The analysis tools that have been developed are applied to study in great detail and with all the methodology of performing an analysis on CMS data specific benchmark processes upon which to gauge the performance of CMS. These processes cover several Higgs boson decay channels, the production and decay of new particles such as Z' and supersymmetric particles, Bs production and processes in heavy ion collisions. The simulation of these benchmark processes includes subtle effects such as possible detector miscalibration and misalignment. Besides these benchmark processes, the physics reach of CMS is studied for a large number of signatures arising in the Standard Model and also in theories beyond the Standard Model for integrated luminosities ranging from 1 fb−1 to 30 fb−1. The Standard Model processes include QCD, B-physics, diffraction, detailed studies of the top quark properties, and electroweak physics topics such as the W and Z0 boson properties. The production and decay of the Higgs particle is studied for many observable decays, and the precision with which the Higgs boson properties can be derived is determined. About ten different supersymmetry benchmark points are analysed using full simulation. The CMS discovery reach is evaluated in the SUSY parameter space covering a large variety of decay signatures. Furthermore, the discovery reach for a plethora of alternative models for new physics is explored, notably extra dimensions, new vector boson high mass states, little Higgs models, technicolour and others. Methods to discriminate between models have been investigated. This report is organized as follows. Chapter 1, the Introduction, describes the context of this document. Chapters 2-6 describe examples of full analyses, with photons, electrons, muons, jets, missing ET, B-mesons and τ's, and for quarkonia in heavy ion collisions. Chapters 7-15 describe the physics reach for Standard Model processes, Higgs discovery and searches for new physics beyond the Standard Model

973 citations


Authors

Showing all 22586 results

NameH-indexPapersCitations
George Davey Smith2242540248373
David Miller2032573204840
Patrick O. Brown183755200985
Dorret I. Boomsma1761507136353
Chad A. Mirkin1641078134254
Darien Wood1602174136596
Wei Li1581855124748
Timothy C. Beers156934102581
Todd Adams1541866143110
Albert-László Barabási152438200119
T. J. Pearson150895126533
Amartya Sen149689141907
Christopher Hill1441562128098
Tim Adye1431898109010
Teruki Kamon1422034115633
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023115
2022543
20212,777
20202,925
20192,774
20182,624