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Monte Carlo simulation of virtual Compton scattering below pion threshold

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 Added by Peter Janssens
 Publication date 2006
  fields Physics
and research's language is English




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This paper describes the Monte Carlo simulation developed specifically for the VCS experiments below pion threshold that have been performed at MAMI and JLab. This simulation generates events according to the (Bethe-Heitler + Born) cross section behaviour and takes into account all relevant resolution-deteriorating effects. It determines the `effective solid angle for the various experimental settings which are used for the precise determination of photon electroproduction absolute cross section.



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Several models for the Monte Carlo simulation of Compton scattering on electrons are quantitatively evaluated with respect to a large collection of experimental data retrieved from the literature. Some of these models are currently implemented in general purpose Monte Carlo systems; some have been implemented and evaluated for possible use in Monte Carlo particle transport for the first time in this study. Here we present first and preliminary results concerning total and differential Compton scattering cross sections.
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This article presents the motivation for developing a comprehensive modeling framework in which different models and parameter inputs can be compared and evaluated for a large range of jet-quenching observables measured in relativistic heavy-ion collisions at RHIC and the LHC. The concept of a framework us discussed within the context of recent efforts by the JET Collaboration, the authors of JEWEL, and the JETSCAPE collaborations. The framework ingredients for each of these approaches is presented with a sample of important results from each. The role of advanced statistical tools in comparing models to data is also discussed, along with the need for a more detailed accounting of correlated errors in experimental results.
Diffractive deeply virtual Compton scattering (DiDVCS) is the process $gamma^*(- Q^2) + N rightarrow rho^0 + gamma^* (Q^2)+ N$, where N is a nucleon or light nucleus, in the kinematical regime of large rapidity gap between the $rho^0$ and the final photon-nucleus system, and in the generalized Bjorken regime where both photon virtualities $Q^2$ and $ Q^2$ are large. We show that this process has the unique virtue of combining the large diffractive cross sections at high energy with the tomographic ability of deeply virtual Compton scattering to scrutinize the quark and gluon content of nucleons and light nuclei. Its study at an electron-ion collider would enlighten the internal structure of hadrons.
Recently, a Monte Carlo method has been presented which allows for the form-free retrieval of size distributions from isotropic scattering patterns, complete with uncertainty estimates linked to the data quality. Here, we present an adaptation to this method allowing for the fitting of anisotropic 2D scattering patterns. The model consists of a finite number of non-interacting ellipsoids of revolution (but would work equally well for cylinders), polydisperse in both dimensions, and takes into account disorientation in the plane parallel to the detector plane. The method application results in three form-free distributions, two for the ellipsoid dimensions, and one for the orientation distribution. It is furthermore shown that a morphological restriction is needed to obtain a unique solution.
Monte-Carlo (MC) methods, based on random updates and the trial-and-error principle, are well suited to retrieve particle size distributions from small-angle scattering patterns of dilute solutions of scatterers. The size sensitivity of size determination methods in relation to the range of scattering vectors covered by the data is discussed. Improvements are presented to existing MC methods in which the particle shape is assumed to be known. A discussion of the problems with the ambiguous convergence criteria of the MC methods are given and a convergence criterion is proposed, which also allows the determination of uncertainties on the determined size distributions.
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