PaperPanorama
FAQ

Nuclear Theory·nucl-th

Thursday·October 13, 2022

7 papers—5 primary·2 cross-listed

All7Primary5Cross-lists2
  1. 06

    Nucleon transverse quark spin densities

    Constantia Alexandrou🇨🇾 · Simone Bacchio🇨🇾 · Martha Constantinou🇺🇸 · Petros Dimopoulos🇮🇹 · Jacob Finkenrath🇨🇾 · Roberto Frezzotti🇮🇹 · Kyriakos Hadjiyiannakou🇨🇾 · Karl Jansen🇩🇪 · Bartosz Kostrzewa🇩🇪 · Giannis Koutsou🇨🇾 · Gregoris Spanoudes🇨🇾 · Carsten Urbach🇩🇪

    We present a calculation of the Mellin moments of the nucleon transverse quark spin densities extracted from the unpolarized and transversity generalized form factors. We use three ensembles of twisted mass fermions with quark masses tuned to their physical values and lattice spacings ~fm, ~fm and ~fm and extrapolate the form factors to the continuum limit. Besides isovector densities we also include results for the tensor charge for each quark flavor using the ensemble with ~fm for which we include the disconnected contributions.

    ↳ hep-lathep-phnucl-thPoS(2023)·2 citations
  2. 07

    Machine learning-based jet and event classification at the Electron-Ion Collider with applications to hadron structure and spin physics

    Kyle Lee🇺🇸 · James Mulligan🇺🇸 · Mateusz Płoskoń🇺🇸 · Felix Ringer🇺🇸 · Feng Yuan🇺🇸

    We explore machine learning-based jet and event identification at the future Electron-Ion Collider (EIC). We study the effectiveness of machine learning-based classifiers at relatively low EIC energies, focusing on (i) identifying the flavor of the jet and (ii) identifying the underlying hard process of the event. We propose applications of our machine learning-based jet identification in the key research areas at the future EIC and current Relativistic Heavy Ion Collider program, including enhancing constraints on (transverse momentum dependent) parton distribution functions, improving experimental access to transverse spin asymmetries, studying photon structure, and quantifying the modification of hadrons and jets in the cold nuclear matter environment in electron-nucleus collisions. We establish first benchmarks and contrast the estimated performance of flavor tagging at the EIC with that at the Large Hadron Collider. We perform studies relevant to aspects of detector design including particle identification, charge information, and minimum transverse momentum capabilities. Additionally, we study the impact of using full event information instead of using only information associated with the identified jet. These methods can be deployed either on suitably accurate Monte Carlo event generators, or, for several applications, directly on experimental data. We provide an outlook for ultimately connecting these machine learning-based methods with first principles calculations in quantum chromodynamics.

    ↳ hep-phnucl-exnucl-thJHEP(2023)·28 citations

Affiliations

first authorsco-authorsvia INSPIRE