First Measurement of Timelike Compton Scattering

Chatagnon, P.; Niccolai, S.; Stepanyan, S.; Amaryan, M. J.; Angelini, G.; Armstrong, W. R.; Atac, H.; Gayoso, C. Ayerbe; Baltzell, N. A.; Barion, L.; Bashkanov, M.; Battaglieri, M.; Bedlinskiy, I.; Benmokhtar, F.; Bianconi, A.; Biondo, L.; Biselli, A. S.; Bondi, M.; Bossù, F.; Boiarinov, S.; Briscoe, W. J.; Brooks, W. K.; Bulumulla, D.; Burkert, V. D.; Carman, D. S.; Carvajal, J. C.; Caudron, M.; Celentano, A.; Chetry, T.; Ciullo, G.; Clark, L.; Cole, P. L.; Contalbrigo, M.; Costantini, G.; Crede, V.; d'Angelo, A.; Dashyan, N.; Defurne, M.; Vita, R. de; Deur, A.; Diehl, S.; Djalali, C.; Dupré, R.; Egiyan, H.; Ehrhart, M.; Alaoui, A. El; Fassi, L. El; Elouadrhiri, L.; Fegan, S.; Fersch, R.; Filippi, A.; Gavalian, G.; Ghandilyan, Y.; Gilfoyle, G. P.; Girod, F. X.; Glazier, D. I.; Golubenko, A. A.; Gothe, R. W.; Gotra, Y.; Griffioen, K. A.; Guidal, M.; Guo, L.; Hakobyan, H.; Hattawy, M.; Hayward, T. B.; Heddle, D.; Hobart, A.; Holtrop, M.; Hyde, C. E.; Ilieva, Y.; Ireland, D. G.; Isupov, E. L.; Jo, H. S.; Joo, K.; Kabir, M. L.; Keller, D.; Khachatryan, G.; Khanal, A.; Kim, A.; Kim, W.; Kripko, A.; Kubarovsky, V.; Kuhn, S. E.; Lanza, L.; Leali, M.; Lee, S.; Lenisa, P.; Livingston, K.; Macgregor, I . J . D.; Marchand, D.; Marsicano, L.; Mascagna, V.; Mckinnon, B.; Mclauchlin, C.; Migliorati, S.; Mirazita, M.; Mokeev, V.; Montgomery, R. A.; Camacho, C. Muñoz; Nadel-Turonski, P.; Naidoo, P.; Neupane, K.; O’Connell, T.; Osipenko, M.; Ouillon, M.; Pandey, P.; Paolone, M.; Pappalardo, L. L.; Paremuzyan, R.; Pasyuk, E.; Phelps, W.; Pogorelko, O.; Poudel, J.; Price, J. W.; Prok, Y.; Raue, B. A.; Reed, T.; Ripani, M.; Rizzo, A.; Rossi, P.; Rowley, J.; Sabatié, F.; Schmidt, A.; Segarra, E. P.; Sharabian, Y. G.; Shirokov, E. V.; Shrestha, U.; Sokhan, D.; Soto, O.; Sparveris, N.; Strakovsky, I. I.; Strauch, S.; Tyler, N.; Tyson, R.; Ungaro, M.; Vallarino, S.; Venturelli, L.; Voskanyan, H.; Vossen, A.; Voutier, E.; Watts, D. P.; Wei, K.; Wei, X.; Wishart, R.; Yale, B.; Zachariou, N.; Zhang, J.; Zhao, Z. W.; Collaboration, The Clas

doi:10.1103/physrevlett.127.262501

“…GPDs have been primarily studied in leptoproduction experiments, in particular those conducted in JLab, DESY and CERN, and are key objects of interest in programmes of future electron-ion colliders, like EIC [16,17], EicC [18] and LHeC [19]. Many data sets have already been collected and reviewed for DVCS [20,21] and DVMP [22], while the first measurement for TCS has been completed recently [23].…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network modelling of generalised parton distributions

Dutrieux

¹

,

Grocholski

²

,

Moutarde

³

et al. 2022

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We discuss the use of machine learning techniques in effectively nonparametric modelling of generalised parton distributions (GPDs) in view of their future extraction from experimental data. Current parameterisations of GPDs suffer from model dependency that lessens their impact on phenomenology and brings unknown systematics to the estimation of quantities like Mellin moments. The new strategy presented in this study allows to describe GPDs in a way fulfilling theory-driven constraints, keeping model dependency to a minimum. Getting a better grip on the control of systematic effects, our work will help the GPD phenomenology to achieve its maturity in the precision era commenced by the new generation of experiments.

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“…So, now only one free parameter remains -the D-term of the constituent quark D q in the form factor (15), (16).…”

Section: Gffs Of Pion In the Instant Form Rqm The Model And Details O...mentioning

confidence: 99%

“…The GPD gives the information on the space distribution of strong forces that act on quarks and gluons inside hadrons. In this connection, in works [12][13][14][15] the processes of deeply virtual Compton scattering (DVCS) on nucleons were investigated. In the paper [12] new technique of artificial neural networks was used for the reduction of model dependence of the data handling.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Relativistic composite-particle theory of the gravitational form factors of pion: quantitative results

Krutov,

Troitsky

2022

Preprint

0

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We use a version of the instant-form relativistic quantum mechanics of composite systems to obtain the gravitational form factors of the pion in a common approach to its electroweak and gravitational properties. In the preceding work [Phys.Rev. D 103, 014029 (2021)] we formulated the mathematical background, presented the principal scheme of calculation and testified the obtained qualitative results to satisfy the general constraints given by the principles of the theory of hadron structure. In the present work we give the detailed calculation of the gravitational form factors in large range of momentum transfer, their static limits, the mean-square mass and mechanical radii of the pion. Now we take into account the qravitational structure of the constituent quarks. We show that the results are almost insensitive to the type of the model two-quark wave function in a close analogy to the case of the pion electromagnetic form factor. We present a careful regularization of the ill-defined form factor D and corresponding matrix element of the energy-momentum tensor going beyond the scope of the modified impulse approximation. Most of the parameters that we use for the calculation had been fixed even earlier in our works on the pion electromagnetic form factors. The only free parameter is the D-term of the constituent quark, which we fix by fitting the result for the pion mechanical radius to a choosen experimental value.

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“…GPDs have been primarily studied in leptoproduction experiments, in particular those conducted in JLab, DESY and CERN, and are key objects of interest in programmes of future electron-ion colliders, like EIC [16,17], EicC [18] and LHeC [19]. Many data sets have already been collected and reviewed for DVCS [20,21] and DVMP [22], while the first measurement for TCS has been completed recently [23].…”

mentioning

confidence: 99%

Artificial neural network modelling of generalised parton distributions

Dutrieux,

Grocholski,

Moutarde

et al. 2021

Preprint

View full text Add to dashboard Cite

We discuss the use of machine learning techniques in effectively nonparametric modelling of generalised parton distributions (GPDs) in view of their future extraction from experimental data. Current parameterisations of GPDs suffer from model dependency that lessens their impact on phenomenology and brings unknown systematics to the estimation of quantities like Mellin moments. The new strategy presented in this study allows to describe GPDs in a way fulfilling theory-driven constraints, keeping model dependency to a minimum. Getting a better grip on the control of systematic effects, our work will help the GPD phenomenology to achieve its maturity in the precision era commenced by the new generation of experiments. IntroductionGeneralised parton distributions (GPDs) [1][2][3][4][5] are widely recognised as one of the key objects to explore the structure of hadrons. They encompass information coming from one-dimensional parton distribution functions (PDFs) and elastic form factors (EFFs). GPDs allow for a hadron tomography [6][7][8], where densities of partons carrying a fraction of hadron momentum are studied in the plane perpendicular to the hadron's direction of motion. GPDs also provide access to the matrix elements of the energy-momentum tensor [2,3,9], making it possible to evaluate the total angular momentum and "mechanical" properties of hadrons, like pressure and shear stress at a given point of space [10][11][12][13].

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First Measurement of Timelike Compton Scattering

Cited by 22 publications

References 48 publications

Artificial neural network modelling of generalised parton distributions

Artificial neural network modelling of generalised parton distributions

Relativistic composite-particle theory of the gravitational form factors of pion: quantitative results

Artificial neural network modelling of generalised parton distributions

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