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The energy-energy correlator (EEC) is an event shape observable which probes the angular correlations of energy depositions in detectors at high energy collider facilities. It has been investigated extensively in the context of precision QCD. In this work, we introduce a novel definition of EEC adapted to the Breit frame in deep-inelastic scattering (DIS). In the back-to-back limit, the observable we propose is sensitive to the universal transverse momentum dependent (TMD) parton distribution functions and fragmentation functions, and it can be studied within the traditional TMD factorization formalism. We further show that the new observable is insensitive to experimental pseudorapidity cuts, often imposed in the Laboratory frame due to detector acceptance limitations. In this work the singular distributions for the new observable are obtained in soft collinear effective theory up to $mathcal{O}(alpha_s^3)$ and are verified by the full QCD calculations up to $mathcal{O}(alpha_s^2)$. The resummation in the singular limit is performed up to next-to-next-to-next-to-leading logarithmic accuracy. After incorporating non-perturbative effects, we present a comparison of our predictions to PYTHIA 8 simulations.
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We study the lepton-jet correlation in deep inelastic scattering. We perform one-loop calculations for the spin averaged and transverse spin dependent differential cross sections depending on the total transverse momentum of the final state lepton and the jet. The transverse momentum dependent (TMD) factorization formalism is applied to describe the relevant observables. To show the physics reach of this process, we perform a phenomenological study for HERA kinematics and comment on an ongoing analysis of experimental data. In addition, we highlight the potential of this process to constrain small-$x$ dynamics.
We study the use of deep learning techniques to reconstruct the kinematics of the deep inelastic scattering (DIS) process in electron-proton collisions. In particular, we use simulated data from the ZEUS experiment at the HERA accelerator facility, and train deep neural networks to reconstruct the kinematic variables $Q^2$ and $x$. Our approach is based on the information used in the classical construction methods, the measurements of the scattered lepton, and the hadronic final state in the detector, but is enhanced through correlations and patterns revealed with the simulated data sets. We show that, with the appropriate selection of a training set, the neural networks sufficiently surpass all classical reconstruction methods on most of the kinematic range considered. Rapid access to large samples of simulated data and the ability of neural networks to effectively extract information from large data sets, both suggest that deep learning techniques to reconstruct DIS kinematics can serve as a rigorous method to combine and outperform the classical reconstruction methods.
We derive mass corrections for semi-inclusive deep inelastic scattering of leptons from nucleons using a collinear factorization framework which incorporates the initial state mass of the target nucleon and the final state mass of the produced hadron. The formalism is constructed specifically to ensure that physical kinematic thresholds for the semi-inclusive process are explicitly respected. A systematic study of the kinematic dependencies of the mass corrections to semi-inclusive cross sections reveals that these are even larger than for inclusive structure functions, especially at very small and very large hadron momentum fractions. The hadron mass corrections compete with the experimental uncertainties at kinematics typical of current facilities, and will be important to efforts at extracting parton distributions or fragmentation functions from semi-inclusive processes at intermediate energies.
The spin-dependent cross sections for semi-inclusive lepton-nucleon scattering are derived in the framework of collinear factorization, including the effects of masses of the target and produced hadron at finite momentum transfer squared Q^2. At leading order the cross sections factorize into products of parton distribution and fragmentation functions evaluated in terms of new, mass-dependent scaling variables. The size of the hadron mass corrections is estimated at kinematics relevant for future semi-inclusive deep-inelastic scattering experiments.
We analyze the left-right asymmetry of pion production in semi-inclusive deep inelastic scattering (SIDIS) process of unpolarized charged lepton on transversely polarized nucleon target. Unlike available treatments, in which some specific weighting functions are multiplied to separate theoretically motivated quantities, we do not introduce any weighting function following the analyzing method by the E704 experiment. The advantage is that this basic observable is free of any theoretical bias, although we can perform the calculation under the current theoretical framework. We present numerical calculations at both HERMES kinematics for the proton target and JLab kinematics for the neutron target. We find that with the current theoretical understanding, Sivers effect plays a key role in our analysis.
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