On the beam spin asymmetries of electroproduction of charged hadrons off the nucleon targets

We study the beam single-spin asymmetries $A_{LU}^{\sin\phi_h}$ for charged hadrons produced in semi-inclusive deep inelastic scattering process, by considering the $e H_1^\perp$ term and the $g^\perp D_1$ term simultaneously. Besides the asymmetries for charged pions, for the first time we present the analysis on the asymmetries in the production of charged kaons, protons and antiprotons by longitudinally polarized leptons scattered off unpolarized proton and deuteron targets. In our calculation we use two sets of transverse momentum dependent distributions $g^\perp(x,\bm k_T^2)$ and $e(x,\bm k_T^2)$ calculated from two different spectator models, and compare the numerical results with the preliminary data recently obtained by the HERMES Collaboration. We also predict the beam spin asymmetries for $\pi^\pm$, $K^\pm$, $p/\bar{p}$ electroproduction in semi-inclusive deep-inelastic scattering of 12 GeV polarized electrons from unpolarized proton and deuteron targets.Comment: 9 pages, 6 figures, version accepted by EPJ

Beam Spin Asymmetries of Charged and Neutral Pion Productions in Semi-inclusive DIS

We present a study on the beam single spin asymmetries $A_{LU}^{\sin\phi_h}$ of $\pi^+$, $\pi^-$ and $\pi^0$ production in semi-inclusive deep inelastic scattering process, by considering Collins effect and the $g^\perp D_1$ term simultaneously. We calculate the twist-3 distributions $e(x, \bm k_T^2)$ and $g^\perp(x,\bm k_T^2)$ for the valence quarks inside the proton in a spectator model. We consider two different options for the form of diquark propagator, as well as two different choices for the model parameters in the calculation. Using the model results, we estimate the beam spin asymmetries $A_{LU}^{\sin\phi_h}$ for the charged and neutral pions and compare the results with the measurement from the HERMES Collaboration. We also make predictions on the asymmetries at CLAS with a $5.5 \,\textrm{GeV}$ beam using the same model results. It is found that different choices for the diquark propagator will not only lead to different expressions for the distribution functions, but also result in different sizes of the asymmetries. Our study also shows that, although the spectator model calculation can describe the asymmetries for certain pion production in some kinematic regions, it seems difficult to explain the asymmetries of pion production for all three pions in a consistent way from the current versions.Comment: 9 pages, 8 figure

Transverse single-spin asymmetries of pion production in semi-inclusive DIS at subleading twist

We study the single-spin asymmetries with the $\sin\phi_S$ and $\sin(2\phi_h -\phi_S)$ angular dependences for charged and neutral pions produced in semi-inclusive deep inelastic scattering on the transversely polarized proton target. The theoretical interpretations of the two asymmetries are presented in terms of the convolution of the twist-3 quark transverse momentum dependent distributions and twist-2 fragmentation functions. Specifically, we investigate the role of the distributions $f_T$, $h_T$ and $h_T^\perp$ in the $\sin\phi_S$ asymmetry, as well as the role of the distributions $f_T^\perp$, $h_T$ and $h_T^\perp$ in the $\sin(2\phi_h -\phi_S)$ asymmetry. We calculate these distributions in a spectator-diquark model and predict the corresponding asymmetries for the first time, considering the kinematics at HERMES, JLab and COMPASS. The numerical estimates show that the asymmetries are sizable, and the dominant contribution to the $\sin\phi_S$ asymmetry comes from the T-odd distribution $f_T$, while $f_T^\perp$ gives the main contribution to the $\sin(2\phi_h -\phi_S)$ asymmetry. The future measurements on these asymmetries can shed light on the information of twist-3 transverse momentum dependent distributions.Comment: 11 pages, 12 figures, version published in PR

Boer-Mulders effect in the unpolarized pion induced Drell-Yan process at COMPASS within TMD factorization

We investigate the theoretical framework of the $\cos 2\phi$ azimuthal asymmetry contributed by the coupling of two Boer-Mulders functions in the dilepton production unpolarized $\pi p$ Drell-Yan process by applying the transverse momentum dependent factorization at leading order. We adopt the model calculation results of the unpolarized distribution function $f_1$ and Boer-Mulders function $h_1^\perp$ of pion meson from the light-cone wave functions. We take into account the transverse momentum evolution effects for both the distribution functions of pion and proton by adopting the existed extraction of the nonperturbative Sudakov form factor for the pion and proton distribution functions. An approximate kernel is included to deal with the energy dependence of the Boer-Mulders function related twist-3 correlation function $T_{q,F}^{(\sigma)}(x,x)$ needed in the calculation. We numerically estimate the Boer-Mulders asymmetry $\nu_{BM}$ as the functions of $x_p$, $x_\pi$, $x_F$ and $q_T$ considering the kinematics at COMPASS Collaboration.Comment: 11 pages, 2 figures, typos correcte

Double Spin Asymmetries ALTcos⁡ϕSA_{LT}^{\cos\phi_S} and ALTcos⁡(2ϕh−ϕS)A_{LT}^{\cos(2\phi_h -\phi_S)} in semi-inclusive DIS

We investigate the double spin asymmetries of pion production in semi-inclusive deep inelastic scattering with a longitudinal polarized beam off a transversely polarized proton target. Particularly, we consider the $\cos\phi_S$ and $\cos(2\phi_h -\phi_S)$ modulations, which can be interpreted by the convolution of the twist-3 transverse momentum dependent distributions and twist-2 fragmentation functions. Three different origins are taken into account simultaneously for each asymmetry: the $g_T D_1$ term, the $e_T H_1^\perp$ term, and the $e_T^\perp H_1^\perp$ term in the $\cos\phi_S$ asymmetry; and the $g_T^\perp D_1$ term, the $e_T H_1^\perp$ term, and the $e_T^\perp H_1^\perp$ term in the $\cos(2\phi_h -\phi_S)$ asymmetry. We calculate the four twist-3 distributions $g_T(x,\boldsymbol{k}_T^2)$, $g_T^\perp(x,\boldsymbol{k}_T^2)$, $e_T(x,\boldsymbol{k}_T^2)$, and $e_T^\perp(x,\boldsymbol{k}_T^2)$ in a spectator-diquark model including vector diquarks. Then we predict the two corresponding asymmetries for charged and neutral pions at the kinematics of HERMES, JLab, and COMPASS for the first time. The numerical estimates indicate that the two different angular-dependence asymmetries are sizable by several percent at HERMES and JLab, and the $\cos\phi_S$ asymmetry has a strong dependence on the Bjorken $x$. Our predictions also show that the dominant contribution to the $\cos\phi_S$ asymmetry comes from the $g_T D_1$ term, while the $g_T^\perp D_1$ term gives the main contribution to the $\cos(2\phi_h -\phi_S)$ asymmetry; the other two $T$-odd terms almost give negligible contributions. In particular, the $\cos(2\phi_h -\phi_S)$ asymmetry provides a unique opportunity to probe the distribution $g_T^\perp$.Comment: minor revision, match the published version in PRD. arXiv admin note: text overlap with arXiv:1405.387

Last-Layer Fairness Fine-tuning is Simple and Effective for Neural Networks

As machine learning has been deployed ubiquitously across applications in modern data science, algorithmic fairness has become a great concern and varieties of fairness criteria have been proposed. Among them, imposing fairness constraints during learning, i.e. in-processing fair training, has been a popular type of training method because they don't require accessing sensitive attributes during test time in contrast to post-processing methods. Although imposing fairness constraints have been studied extensively for classical machine learning models, the effect these techniques have on deep neural networks is still unclear. Recent research has shown that adding fairness constraints to the objective function leads to severe over-fitting to fairness criteria in large models, and how to solve this challenge is an important open question. To address this challenge, we leverage the wisdom and power of pre-training and fine-tuning and develop a simple but novel framework to train fair neural networks in an efficient and inexpensive way. We conduct comprehensive experiments on two popular image datasets with state-of-art architectures under different fairness notions to show that last-layer fine-tuning is sufficient for promoting fairness of the deep neural network. Our framework brings new insights into representation learning in training fair neural networks