Statistical Methods in Experimental Physics

James, F.

doi:10.1142/6096

Cited by 459 publications

(374 citation statements)

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“…In addition, the errors evaluated using fit 1 are Gaussian distributed, in agreement with the statistical expectations (see, for instance, Ref. [47]). …”

Section: Fitting Strategysupporting

confidence: 86%

First extraction of the scalar proton dynamical polarizabilities from real Compton scattering data

2018

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We present the first attempt to extract the scalar dipole dynamical polarizabilities from proton real Compton scattering data below pion-production threshold. The theoretical framework combines dispersion relations technique, low-energy expansion, and multipole decomposition of the scattering amplitudes. The results are obtained with statistical tools that have never been applied so far to Compton scattering data and are crucial to overcome problems inherent to the analysis of the available data set.

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“…In addition, the errors evaluated using fit 1 are Gaussian distributed, in agreement with the statistical expectations (see, for instance, Ref. [47]). …”

Section: Fitting Strategysupporting

confidence: 86%

First extraction of the scalar proton dynamical polarizabilities from real Compton scattering data

2018

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“…This method, sometimes called MINOS (the name of the routine that first implemented it in Minuit), is long known in the statistics field (Wilks 1938). It is exact up to order O(1/N) (James 2007), N being the number of samples, and is in practice excellent unless N is very small.…”

Section: Profile Likelihoodsmentioning

confidence: 99%

“…Is this an effect of the Bayesian methodology, which computes credible intervals and where such effects may arise when combining incompatible data? Naively, in a frequentist analysis adding some information (in the Fisher sense, see James 2007) can only lower the size of confidence interval, since the profilelikelihood "error" (its curvature at the minimum) can only decrease and thresholding it at a constant value should only lead to a smaller region.…”

Section: Mass Of Standard Neutrinosmentioning

confidence: 99%

Planckintermediate results

Ade

Aghanim

Arnaud

et al. 2014

A&A

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We explore the 2013 Planck likelihood function with a high-precision multi-dimensional minimizer (Minuit). This allows a refinement of the ΛCDM best-fit solution with respect to previously-released results, and the construction of frequentist confidence intervals using profile likelihoods. The agreement with the cosmological results from the Bayesian framework is excellent, demonstrating the robustness of the Planck results to the statistical methodology. We investigate the inclusion of neutrino masses, where more significant differences may appear due to the non-Gaussian nature of the posterior mass distribution. By applying the Feldman-Cousins prescription, we again obtain results very similar to those of the Bayesian methodology. However, the profile-likelihood analysis of the cosmic microwave background (CMB) combination (Planck+WP+highL) reveals a minimum well within the unphysical negative-mass region. We show that inclusion of the Planck CMB-lensing information regularizes this issue, and provide a robust frequentist upper limit m ν ≤ 0.26 eV (95% confidence) from the CMB+lensing+BAO data combination.

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“…The comparison of a data set of N elements to a distribution function is a general mathematical issue that appears in many research fields [47,48]. For a one-dimensional probability density function g(x), a data set can be compared to g considering the cumulative distri- [49], where the supremum is used rather than the maximum since G N (x) is a piecewise continuous function.…”

Section: B Verification Of a Pic Simulation Code Using The Methods Ofmentioning

confidence: 99%

A methodology for the rigorous verification of Particle-in-Cell simulations

2017

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A methodology to perform a rigorous verification of Particle-in-Cell (PIC) simulations is presented, both for assessing the correct implementation of the model equations (code verification), and evaluating the numerical uncertainty affecting the simulation results (solution verification).The proposed code verification methodology is a generalization of the procedure developed for plasma simulation codes based on finite difference schemes that was described by Riva et al.

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Statistical Methods in Experimental Physics

Cited by 459 publications

References 0 publications

First extraction of the scalar proton dynamical polarizabilities from real Compton scattering data

First extraction of the scalar proton dynamical polarizabilities from real Compton scattering data

Planckintermediate results

A methodology for the rigorous verification of Particle-in-Cell simulations

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