Jun 26 – 27, 2023
America/Chicago timezone

Extraction of Drell-Yan Angular Coefficients using Neural Network-based Classifiers

Jun 27, 2023, 8:30 AM
One West (Fermilab)

One West



Mr Dinupa Nawarathne (New Mexico State University)


Study of angular distributions in the Drell-Yan process is a valuable tool for unraveling the structure of hadrons. Measuring the $\cos2\phi$ angular dependence, where $\phi$ denotes the azimuthal angle of dimuons in the Collins-Soper frame, can be used to extract the Boer-Mulders (BM) function. The BM function describes the transverse-polarization asymmetry of quarks within an unpolarized hadron and is a result of the coupling between transverse momentum and transverse spin of the quarks inside the hadron. Conventional methods for extracting the angular-distribution coefficients typically involve unfolding low-dimensional detector data, which may not fully exploit the complete phase space for best parameter optimization. To overcome this limitation, we propose a novel approach utilizing Neural Network-based Classifiers to directly extract the angular coefficients using high-dimensional information at the detector level. In this presentation, we will explain the design of the neural network architecture, training strategies, and outline our plans to achieve conclusive results.

Primary author

Mr Dinupa Nawarathne (New Mexico State University)

Presentation materials