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H Paganetti - One of the best experts on this subject based on the ideXlab platform.

  • th c brb 04 reliability of proton nuclear interaction Cross Section Data to predict proton induced pet images in proton therapy
    Medical Physics, 2011
    Co-Authors: S Espana, Xuping Zhu, J Daartz, El G Fakhri, Thomas Bortfeld, H Paganetti
    Abstract:

    Purpose: In‐vivo PET range verification relies on the comparison of measured and simulated activity distributions. The accuracy of the simulated distribution depends on the accuracy of the Monte Carlo code, which is in turn dependent on the accuracy of the available Cross Sections Data for β+ isotope production. We have explored different Cross Section Data available in the literature for the main reaction channels (16O(p,pn)15O, 12C(p,pn)11C and 16O(p,3p3n)11C) contributing to the production of β+ isotopes by proton beams in patients. Methods: Available experimental and theoretical values were implemented in the simulation and compared with measured PETimages obtained with a high‐resolution PET scanner. Each reaction channel was studied independently. A phantom with three different materials was built, two of them with high carbon or oxygen concentration and a third one with average soft tissue composition. Monoenergetic and SOBP field irradiations of the phantom were accomplished and measured PETimages were compared with simulation results. Results: Different Cross Section values for the tissue‐equivalent material lead to range differences below 1 mm when a 5 min scan time was employed and close to 5 mm differences for a 30 min scan time with 15 min delay between irradiation and scan (a typical off‐line protocol). Conclusion: The results presented here emphasize the need of more accurate measurement of the Cross Section values of the reaction channels contributing to the production of PETisotopes by proton beams before this in‐vivo range verification method can achieve mm accuracy.

  • the reliability of proton nuclear interaction Cross Section Data to predict proton induced pet images in proton therapy
    Physics in Medicine and Biology, 2011
    Co-Authors: S Espana, Xuping Zhu, J Daartz, El G Fakhri, Thomas Bortfeld, H Paganetti
    Abstract:

    In vivo PET range verification relies on the comparison of measured and simulated activity distributions. The accuracy of the simulated distribution depends on the accuracy of the Monte Carlo code, which is in turn dependent on the accuracy of the available Cross-Section Data for ?+ isotope production. We have explored different Cross-Section Data available in the literature for the main reaction channels (16O(p,pn)15O, 12C(p,pn)11C and 16O(p,3p3n)11C) contributing to the production of ?+ isotopes by proton beams in patients. Available experimental and theoretical values were implemented in the simulation and compared with measured PET images obtained with a high-resolution PET scanner. Each reaction channel was studied independently. A phantom with three different materials was built, two of them with high carbon or oxygen concentration and a third one with average soft tissue composition. Monoenergetic and SOBP field irradiations of the phantom were accomplished and measured PET images were compared with simulation results. Different Cross-Section values for the tissue-equivalent material lead to range differences below 1 mm when a 5 min scan time was employed and close to 5 mm differences for a 30 min scan time with 15 min delay between irradiation and scan (a typical off-line protocol). The results presented here emphasize the need of more accurate measurement of the Cross-Section values of the reaction channels contributing to the production of PET isotopes by proton beams before this in vivo range verification method can achieve mm accuracy.

B Malaescu - One of the best experts on this subject based on the ideXlab platform.

  • evaluation of the strong coupling constant α s using the atlas inclusive jet Cross Section Data
    European Physical Journal C, 2012
    Co-Authors: B Malaescu, P Starovoitov
    Abstract:

    We perform a determination of the strong coupling constant using the latest ATLAS inclusive jet Cross Section Data, from proton–proton collisions at \(\sqrt{s}=7~\mathrm{TeV}\), and their full information on the bin-to-bin correlations. Several procedures for combining the statistical information from the different Data inputs are studied and compared. The theoretical prediction is obtained using NLO QCD, and it also includes non-perturbative corrections. Our determination uses inputs with transverse momenta between 45 and 600 GeV, the running of the strong coupling being also tested in this range. Good agreement is observed when comparing our result with the world average at the Z-boson scale, as well as with the most recent results from the Tevatron.

  • evaluation of the strong coupling constant alpha_s using the atlas inclusive jet Cross Section Data
    arXiv: High Energy Physics - Phenomenology, 2012
    Co-Authors: B Malaescu, P Starovoitov
    Abstract:

    We perform a determination of the strong coupling constant using the latest ATLAS inclusive jet Cross Section Data, from proton-proton collisions at sqrt{s}=7 TeV, and their full information on the bin-to-bin correlations. Several procedures for combining the statistical information from the different Data inputs are studied and compared. The theoretical prediction is obtained using NLO QCD, and it also includes non-perturbative corrections. Our determination uses inputs with transverse momenta between 45 and 600 GeV, the running of the strong coupling being also tested in this range. Good agreement is observed when comparing our result with the world average at the Z-boson scale, as well as with the most recent results from the Tevatron.

  • reevaluation of the hadronic contribution to the muon magnetic anomaly using new e e π π Cross Section Data from babar
    European Physical Journal C, 2010
    Co-Authors: M Davier, Andreas Hoecker, B Malaescu, C Z Yuan, Z Zhang
    Abstract:

    Using recently published, high-precision π+π− Cross Section Data by the BABAR experiment from the analysis of e+e− events with high-energy photon radiation in the initial state, we reevaluate the lowest order hadronic contribution \((a_{\mu}^{\mathrm{had},\mathrm{LO}})\) to the anomalous magnetic moment of the muon. We employ newly developed software featuring improved Data interpolation and averaging, more accurate error propagation and systematic validation. With the new Data, the discrepancy between the e+e−- and τ-based results for the dominant two-pion mode reduces from previously 2.4σ to 1.5σ in the dispersion integral, though significant local discrepancies in the spectra persist. We obtain for the e+e−-based evaluation \(a_{\mu}^{\mathrm{had},\mathrm{LO}}=(695.5\pm4.1)\times 10^{-10}\) , where the error accounts for all sources. The full Standard Model prediction of aμ differs from the experimental value by 3.2σ.

  • reevaluation of the hadronic contribution to the muon magnetic anomaly using new e e pi pi Cross Section Data from babar
    arXiv: High Energy Physics - Phenomenology, 2009
    Co-Authors: M Davier, Andreas Hoecker, B Malaescu, C Z Yuan, Zhiqing Zhang
    Abstract:

    Using recently published, high-precision pi+pi- Cross Section Data by the BABAR experiment from the analysis of e+e- events with high-energy photon radiation in the initial state, we reevaluate the lowest order hadronic contribution a_mu[had,LO] to the anomalous magnetic moment of the muon. We employ newly developed software featuring improved Data interpolation and averaging, more accurate error propagation and systematic validation. With the new Data, the discrepancy between the e+e- and tau-based results for the dominant two-pion mode reduces from previously 2.4 sigma to 1.5 sigma in the dispersion integral, though significant local discrepancies in the spectra persist. We obtain for the e+e- based evaluation amu[had,LO] = (695.5 +- 4.1) 10^-10, where the error accounts for all sources. The full Standard Model prediction of a_mu differs from the experimental value by 3.2 sigma.

P Starovoitov - One of the best experts on this subject based on the ideXlab platform.

  • evaluation of the strong coupling constant α s using the atlas inclusive jet Cross Section Data
    European Physical Journal C, 2012
    Co-Authors: B Malaescu, P Starovoitov
    Abstract:

    We perform a determination of the strong coupling constant using the latest ATLAS inclusive jet Cross Section Data, from proton–proton collisions at \(\sqrt{s}=7~\mathrm{TeV}\), and their full information on the bin-to-bin correlations. Several procedures for combining the statistical information from the different Data inputs are studied and compared. The theoretical prediction is obtained using NLO QCD, and it also includes non-perturbative corrections. Our determination uses inputs with transverse momenta between 45 and 600 GeV, the running of the strong coupling being also tested in this range. Good agreement is observed when comparing our result with the world average at the Z-boson scale, as well as with the most recent results from the Tevatron.

  • evaluation of the strong coupling constant alpha_s using the atlas inclusive jet Cross Section Data
    arXiv: High Energy Physics - Phenomenology, 2012
    Co-Authors: B Malaescu, P Starovoitov
    Abstract:

    We perform a determination of the strong coupling constant using the latest ATLAS inclusive jet Cross Section Data, from proton-proton collisions at sqrt{s}=7 TeV, and their full information on the bin-to-bin correlations. Several procedures for combining the statistical information from the different Data inputs are studied and compared. The theoretical prediction is obtained using NLO QCD, and it also includes non-perturbative corrections. Our determination uses inputs with transverse momenta between 45 and 600 GeV, the running of the strong coupling being also tested in this range. Good agreement is observed when comparing our result with the world average at the Z-boson scale, as well as with the most recent results from the Tevatron.

S Espana - One of the best experts on this subject based on the ideXlab platform.

  • th c brb 04 reliability of proton nuclear interaction Cross Section Data to predict proton induced pet images in proton therapy
    Medical Physics, 2011
    Co-Authors: S Espana, Xuping Zhu, J Daartz, El G Fakhri, Thomas Bortfeld, H Paganetti
    Abstract:

    Purpose: In‐vivo PET range verification relies on the comparison of measured and simulated activity distributions. The accuracy of the simulated distribution depends on the accuracy of the Monte Carlo code, which is in turn dependent on the accuracy of the available Cross Sections Data for β+ isotope production. We have explored different Cross Section Data available in the literature for the main reaction channels (16O(p,pn)15O, 12C(p,pn)11C and 16O(p,3p3n)11C) contributing to the production of β+ isotopes by proton beams in patients. Methods: Available experimental and theoretical values were implemented in the simulation and compared with measured PETimages obtained with a high‐resolution PET scanner. Each reaction channel was studied independently. A phantom with three different materials was built, two of them with high carbon or oxygen concentration and a third one with average soft tissue composition. Monoenergetic and SOBP field irradiations of the phantom were accomplished and measured PETimages were compared with simulation results. Results: Different Cross Section values for the tissue‐equivalent material lead to range differences below 1 mm when a 5 min scan time was employed and close to 5 mm differences for a 30 min scan time with 15 min delay between irradiation and scan (a typical off‐line protocol). Conclusion: The results presented here emphasize the need of more accurate measurement of the Cross Section values of the reaction channels contributing to the production of PETisotopes by proton beams before this in‐vivo range verification method can achieve mm accuracy.

  • the reliability of proton nuclear interaction Cross Section Data to predict proton induced pet images in proton therapy
    Physics in Medicine and Biology, 2011
    Co-Authors: S Espana, Xuping Zhu, J Daartz, El G Fakhri, Thomas Bortfeld, H Paganetti
    Abstract:

    In vivo PET range verification relies on the comparison of measured and simulated activity distributions. The accuracy of the simulated distribution depends on the accuracy of the Monte Carlo code, which is in turn dependent on the accuracy of the available Cross-Section Data for ?+ isotope production. We have explored different Cross-Section Data available in the literature for the main reaction channels (16O(p,pn)15O, 12C(p,pn)11C and 16O(p,3p3n)11C) contributing to the production of ?+ isotopes by proton beams in patients. Available experimental and theoretical values were implemented in the simulation and compared with measured PET images obtained with a high-resolution PET scanner. Each reaction channel was studied independently. A phantom with three different materials was built, two of them with high carbon or oxygen concentration and a third one with average soft tissue composition. Monoenergetic and SOBP field irradiations of the phantom were accomplished and measured PET images were compared with simulation results. Different Cross-Section values for the tissue-equivalent material lead to range differences below 1 mm when a 5 min scan time was employed and close to 5 mm differences for a 30 min scan time with 15 min delay between irradiation and scan (a typical off-line protocol). The results presented here emphasize the need of more accurate measurement of the Cross-Section values of the reaction channels contributing to the production of PET isotopes by proton beams before this in vivo range verification method can achieve mm accuracy.

Nathaniel Beck - One of the best experts on this subject based on the ideXlab platform.

  • random coefficient models for time series Cross Section Data monte carlo experiments
    Political Analysis, 2007
    Co-Authors: Nathaniel Beck, Jonathan N Katz
    Abstract:

    This article considers random coefficient models (RCMs) for time-series‐Cross-Section Data. These models allow for unit to unit variation in the model parameters. The heart of the article compares the finite sample properties of the fully pooled estimator, the unit by unit (unpooled) estimator, and the (maximum likelihood) RCM estimator. The maximum likelihood estimator RCM performs well, even where the Data were generated so that the RCM would be problematic. In an appendix, we show that the most common feasible generalized least squares estimator of the RCM models is always inferior to the maximum likelihood estimator, and in smaller samples dramatically so.

  • time series Cross Section Data what have we learned in the past few years
    Annual Review of Political Science, 2001
    Co-Authors: Nathaniel Beck
    Abstract:

    ▪ Abstract This article treats the analysis of “time-series–Cross-Section” (TSCS) Data, which has become popular in the empirical analysis of comparative politics and international relations (IR). Such Data consist of repeated observations on a series of fixed (nonsampled) units, where the units are of interest in themselves. An example of TSCS Data is the post–World War II annual observations on the political economy of OECD nations. TSCS Data are also becoming more common in IR studies that use the “dyad-year” design; such Data are often complicated by a binary dependent variable (the presence or absence of dyadic conflict). Among the issues considered here are estimation and specification. I argue that treating TSCS issues as an estimation nuisance is old-fashioned; those wishing to pursue this approach should use ordinary least squares with panel correct standard errors rather than generalized least squares. A modern approach models dynamics via a lagged dependent variable or a single equation error c...

  • what to do and not to do with time series Cross Section Data
    American Political Science Review, 1995
    Co-Authors: Nathaniel Beck, Jonathan N Katz
    Abstract:

    We examine some issues in the estimation of time-series Cross-Section models, calling into question the conclusions of many published studies, particularly in the field of comparative political economy. We show that the generalized least squares approach of Parks produces standard errors that lead to extreme overconfidence, often underestimating variability by 50% or more. We also provide an alternative estimator of the standard errors that is correct when the error structures show complications found in this type of model. Monte Carlo analysis shows that these “panel-corrected standard errors” perform well. The utility of our approach is demonstrated via a reanalysis of one “social democratic corporatist” model.

  • what to do and not to do with time series Cross Section Data
    Social Science Research Network, 1995
    Co-Authors: Nathaniel Beck, Jonathan N Katz
    Abstract:

    T v political economy. We show that the generalized least squares approach of Parks produces standard errors that lead to extreme overconfidence, often underestimating variability by 50% or more. We also provide an alternative estimator of the standard errors that is correct when the error structures show complications found in this type of model. Monte Carlo analysis shows that these "panel-corrected standard errors" perform well. The utility of our approach is demonstrated via a reanalysis of one "social democratic corporatist" model. W e shall show that a commonly used technique for the analysis of time-series CrossSection (TSCS) Data produces incorrect results. Our result either invalidates or calls into question the findings of at least five articles published in the American Political Science Review, as well as a like number in other leading journals in political science and sociology. Table 1 provides an incomplete list of relevant articles whose conclusions are based on the use of this problematic technique. All of these articles use an application of the generalized least squares (GLS) method first described by Parks (1967), a method designed to deal with some common problems that occur in TSCS Data. We show that the Parks method produces dramatically inaccurate standard errors when used for the type of Data commonly analyzed by students of comparative politics. We then offer a new method that is both easier to implement and produces accurate standard errors. Time-series Cross-Section Data are characterized by having repeated observations on fixed units, such as states or nations. The number of units analyzed would typically range from about 10 to 100, with each unit observed over a relatively long time period (often 20 to 50 years). Both the temporal and spatial properties of TSCS Data make the use of ordinary least squares (OLS) problematic. In particular, models for TSCS Data often allow for temporally and spatially correlated errors, as well as for heteroscedasticity. Parks proposed a method for dealing with these problems based on GLS.1 The use of this method can lead to dramatic underestimates of parameter variability in common research situations. Why the severe problems with the Parks method? Is it not just an application of well-known GLS? While GLS has optimal properties for TSCS Data, it assumes that we have knowledge about the error process that, in practice, we never have. Thus analysts use not GLS, but feasible generalized least squares (FGLS). It is "feasible" because it uses an estimate of the error process, avoiding the GLS assumption that the error process is known. The FGLS formula for standard errors, however, assumes that the error process is known, not estimated. In many applications this is not a problem because the error process has few enough parameters that they can be well estimated. Such is not the case for TSCS models, where the error process has a large number of parameters. This oversight causes estimates of the standard errors of the estimated coefficients to understate their true variability. We provide a measure of how much the Parks standard errors understate true sampling variability, that is, how much the Parks method falsely inflates confidence in the findings of TSCS studies. Unfortunately, it is not possible to provide analytic formulae for the degree of overconfidence introduced by the Parks method. Instead, we provide evidence from Monte Carlo experiments using simulated Data to assess the performance of the various estimators. This evidence clearly shows the overconfidence induced by the Parks method. The Parks estimator may understate variability by between 50% and 300% in practical research situations. It is this extreme overconfidence that leads us either to overturn or to cast doubt on the findings of many analyses based on the Parks method. Having demonstrated the problems of the Parks method, we instead advocate a simpler method for estimating TSCS models. It is well known that even though OLS estimates of TSCS model parameters may not be optimal, they often perform well in practical research situations. It is also well known that the OLS estimates of standard errors may be highly inaccurate in such situations. We therefore propose to retain OLS parameter estimates but replace the OLS standard errors with panel-corrected standard errors. Monte Carlo analysis shows that these new estimates of sampling variability are very accurate, even in the presence of complicated panel error structures. We shall detail the problems of the Parks method, laying out the structure of TSCS models and showing why OLS is. problematic. In order to understand Parks' solution and why it is problematic, it is neces