English
Related papers

Related papers: Deconvoluting GPD from an explicit DVCS amplitude

200 papers

In this work we estimate the differential and total cross sections for the high energy deeply virtual Compton scattering in the weak sector. In the weak neutral sector one considers neutrino scattering off an unpolarized proton target…

High Energy Physics - Phenomenology · Physics 2008-11-26 M. V. T. Machado

In our earlier work [Fareed et al., Comput. Math. Appl. 75 (2018), no. 6, 1942-1960], we developed an incremental approach to compute the proper orthogonal decomposition (POD) of PDE simulation data. Specifically, we developed an…

Numerical Analysis · Mathematics 2021-02-01 Hiba Fareed , John R. Singler

We calculate the cross section of the deeply virtual Compton scattering at large energies and intermediate momentum transfers.

High Energy Physics - Phenomenology · Physics 2008-11-26 I. Balitsky , E. Kuchina

Double Deeply Virtual Compton Scattering (DDVCS) is the only experimental channel for the determination of the dependence of the Generalized Parton Distributions (GPDs) on both the average and the transferred momentum independently. The…

Nuclear Experiment · Physics 2021-12-20 S. Zhao , A. Camsonne , D. Marchand , M. Mazouz , N. Sparveris , S. Stepanyan , E. Voutier , Z. W. Zhao

We study the virtual Compton amplitude in the generalized Bjorken region (q^2 -> Infinity, t small) in QCD by means of a light-cone expansion of the product of e.m. currents in string operators in coordinate space. Electromagnetic gauge…

High Energy Physics - Phenomenology · Physics 2014-11-17 A. V. Radyushkin , C. Weiss

Measuring Deeply Virtual Compton Scattering on the neutron is one of the necessary steps to understand the structure of the nucleon in terms of Generalized Parton Distributions (GPDs). Neutron targets play a complementary role to…

High Energy Physics - Experiment · Physics 2024-06-26 CLAS Collaboration , A. Hobart , S. Niccolai , M. Čuić , K. Kumerički , P. Achenbach , J. S. Alvarado , W. R. Armstrong , H. Atac , H. Avakian , L. Baashen , N. A. Baltzell , L. Barion , M. Bashkanov , M. Battaglieri , B. Benkel , F. Benmokhtar , A. Bianconi , A. S. Biselli , S. Boiarinov , M. Bondi , W. A. Booth , F. Bossù , K. -Th. Brinkmann , W. J. Briscoe , W. K. Brooks , S. Bueltmann , V. D. Burkert , T. Cao , R. Capobianco , D. S. Carman , P. Chatagnon , G. Ciullo , P. L. Cole , M. Contalbrigo , A. D'Angelo , N. Dashyan , R. De Vita , M. Defurne , A. Deur , S. Diehl , C. Dilks , C. Djalali , R. Dupre , H. Egiyan , A. El Alaoui , L. El Fassi , L. Elouadrhiri , S. Fegan , A. Filippi , C. Fogler , K. Gates , G. Gavalian , G. P. Gilfoyle , D. Glazier , R. W. Gothe , Y. Gotra , M. Guidal , K. Hafidi , H. Hakobyan , M. Hattawy , F. Hauenstein , D. Heddle , M. Holtrop , Y. Ilieva , D. G. Ireland , E. L. Isupov , H. Jiang , H. S. Jo , K. Joo , T. Kageya , A. Kim , W. Kim , V. Klimenko , A. Kripko , V. Kubarovsky , S. E. Kuhn , L. Lanza , M. Leali , S. Lee , P. Lenisa , X. Li , I. J. D. MacGregor , D. Marchand , V. Mascagna , M. Maynes , B. McKinnon , Z. E. Meziani , S. Migliorati , R. G. Milner , T. Mineeva , M. Mirazita , V. Mokeev , C. Muñoz Camacho , P. Nadel-Turonski , P. Naidoo , K. Neupane , G. Niculescu , M. Osipenko , P. Pandey , M. Paolone , L. L. Pappalardo , R. Paremuzyan , E. Pasyuk , S. J. Paul , W. Phelps , N. Pilleux , M. Pokhrel , S. Polcher Rafael , J. Poudel , J. W. Price , Y. Prok , T. Reed , J. Richards , M. Ripani , J. Ritman , P. Rossi , A. A. Golubenko , C. Salgado , S. Schadmand , A. Schmidt , Marshall B. C. Scott , E. M. Seroka , Y. G. Sharabian , E. V. Shirokov , U. Shrestha , N. Sparveris , M. Spreafico , S. Stepanyan , I. I. Strakovsky , S. Strauch , J. A. Tan , N. Trotta , R. Tyson , M. Ungaro , S. Vallarino , L. Venturelli , V. Tommaso , H. Voskanyan , E. Voutier , D. P Watts , X. Wei , R. Williams , M. H. Wood , L. Xu , N. Zachariou , J. Zhang , Z. W. Zhao , M. Zurek

This paper addresses the problem of high-resolution Doppler blood flow estimation from an ultrafast sequence of ultrasound images. Formulating the separation of clutter and blood components as an inverse problem has been shown in the…

Image and Video Processing · Electrical Eng. & Systems 2020-07-13 Duong-Hung Pham , Adrian Basarab , Ilyess Zemmoura , Jean-Pierre Remenieras , Denis Kouame

Generalized parton distribution (GPD) contains rich information of partons in a hadron, including transverse profile, and is also non-perturbative information necessary in describing a variety of hard processes, such as meson…

High Energy Physics - Phenomenology · Physics 2015-05-28 Ryoichi Nishio , Taizan Watari

In a scenario where the constituent quarks are composite systems, Generalized Parton Distributions (GPDs) are built from wave functions to be evaluated in a Constituent Quark Model (CQM), convoluted with the GPDs of the constituent quarks…

High Energy Physics - Phenomenology · Physics 2007-05-23 Sergio Scopetta , Vicente Vento

The generalized singular value decomposition (GSVD) is a valuable tool that has many applications in computational science. However, computing the GSVD for large-scale problems is challenging. Motivated by applications in hyper-differential…

Numerical Analysis · Mathematics 2020-02-10 Arvind K. Saibaba , Joseph Hart , Bart van Bloemen Waanders

Coherent Deeply virtual Compton scattering off the $^4$He nucleus is studied in impulse approximation. A convolution formula for the nuclear Generalized Parton Distribution (GPD) is derived in terms of the $^4$He non-diagonal spectral…

Nuclear Theory · Physics 2018-07-18 Sara Fucini , Sergio Scopetta , Michele Viviani

We consider Deeply Virtual Compton Scattering (DVCS) on nucleons and nuclei in the framework of generalized vector meson dominance (GVMD) model. We demonstrate that the GVMD model provides a good description of the HERA data on the…

High Energy Physics - Phenomenology · Physics 2008-11-26 K. Goeke , V. Guzey , M. Siddikov

Very deep convolutional neural networks (CNNs) have been firmly established as the primary methods for many computer vision tasks. However, most state-of-the-art CNNs are large, which results in high inference latency. Recently, depth-wise…

Computer Vision and Pattern Recognition · Computer Science 2025-01-07 Yihui He , Jianing Qian , Jianren Wang , Cindy X. Le , Congrui Hetang , Qi Lyu , Wenping Wang , Tianwei Yue

Positron initiated deeply virtual Compton scattering (DVCS) off $^4$He and $^3$He nuclei is described. The way the so-called $d-$term could be obtained from the real part of the relevant Compton form factor is summarized, and the importance…

High Energy Physics - Phenomenology · Physics 2021-09-29 Sara Fucini , Mohammad Hattawy , Matteo Rinaldi , Sergio Scopetta

We use gauge/gravity duality to study deeply virtual Compton scattering (DVCS) in the limit of high center of mass energy at fixed momentum transfer, corresponding to the limit of low Bjorken x, where the process is dominated by the…

High Energy Physics - Theory · Physics 2013-05-30 Miguel S. Costa , Marko Djurić

This report reviews the recent experimental results from the CLAS collaboration (Hall B of Jefferson Lab, or JLab) on Deeply Virtual Compton Scattering (DVCS) and Deeply Virtual Meson Production (DVMP) and discusses their interpretation in…

Nuclear Experiment · Physics 2015-06-03 Hyon-Suk Jo

We determine the properties of generalised parton distributions (GPDs) from a lattice QCD calculation of the off-forward Compton amplitude (OFCA). By extending the Feynman-Hellmann relation to second-order matrix elements at off-forward…

In the context of nucleon structure studies, Generalized Parton Distributions (GPDs) are crucial for understanding the correlation between the longitudinal momentum and the transverse position of partons inside the nucleon. A privileged…

High Energy Physics - Experiment · Physics 2025-09-16 J. S. Alvarado , M. Hoballah , E. Voutier

The electromagnetic scattering from elongated, arbitrarily shaped, open-ended cavities have been studied extensively over the years. In this paper we introduce the fast encapsulating domain decomposition (EDD) scheme for the analysis of…

Computational Physics · Physics 2018-01-17 Moti Zelig , Ehud Heyman , Amir Boag