Publication: Transverse momentum dependent distributions in e(+)e(-) and semi-inclusive deep-inelastic scattering using jets.
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2019-10-04
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Springer
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Abstract
The extraction of transverse momentum dependent distributions (TMDs) in semi-inclusive deep inelastic scattering (SIDIS) is complicated by the presence of both initial- and final-state nonperturbative physics. We recently proposed measuring jets (in- stead of hadrons) as a solution, showing that for the Winner-Take-All jet axis the same factorization formulae valid for hadrons applied to jets of arbitrary size. This amounts to simply replacing TMD fragmentation functions by our TMD jet functions. In this paper we present the calculation of these jet functions at one loop. We obtain phenomenological results for e(+)e(-)-> dijet (Belle II, LEP) and SIDIS (HERA, EIC) with a jet, building on the arTeMiDe code. Surprisingly, we find that the limit of large jet radius describes the full R results extremely well, and we extract the two-loop jet function in this limit using Event2, allowing us to achieve (NLL)-L-3 accuracy. We demonstrate the perturbative convergence of our predictions and explore the kinematic dependence of the cross section. Finally, we investigate the sensitivity to nonperturbative physics, demonstrating that jets are a promising probe of proton structure.
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© 2019 Springer.
We thank Alexey Vladimirov for assistance with the arTeMiDe program and feedback on this manuscript. D.G.R. and I.S. are supported by the Spanish MECD grant FPA2016-75654-C2-2-P. D.G.R. acknowledges the support of the Universidad Complutense de Madrid through the predoctoral grant CT17/17-CT18/17. W.W. and L.Z. are supported by ERC grant ERC-STG-2015-677323. W.W. also acknowledges support by the D-ITP consortium, a program of the Netherlands Organization for Scientific Research (NWO) that is funded by the Dutch Ministry of Education, Culture and Science (OCW). This article is based upon work from COST Action CA16201 PARTICLEFACE, supported by COST (European Cooperation in Science and Technology). This project has received funding from the European Union Horizon 2020 research and innovation program under grant agreement No 824093 (STRONG-2020).