Speaker
Description
In this work, we analyze the impact of open-heavy-flavor-production data in proton-lead collisions on the nCTEQ nuclear PDFs. For the theoretical predictions of this process, we use an existing next-to-leading-order calculation in a general-mass variable-flavor-number scheme (GM-VFNS), which, as a pQCD alternative, we compare to the data-driven approach for open heavy-flavor production used in the most recent nCTEQ global nuclear PDF analyses. The latter uses a "crystal-ball" function, an effective hard-scattering matrix element that is fitted to data from proton-proton collisions and then used as the theoretical prediction in the proton-lead case. Since the crystal-ball function is given by an analytical formula, it provides the required performance for PDF fits out of the box.
Conversely, pQCD calculations are usually given as Monte Carlo (MC) simulations, which can take multiple CPU hours to reach a viable precision. Thus, a significant speedup of the evaluation of the pQCD predictions is required to make the direct MC calculation feasible for global PDF analyses. This is achieved by "gridding" the hard-scattering cross sections, i.e., calculating them once and storing their interpolated values in look-up tables (grids). The physical predictions can then be obtained by convolving the grids with a PDF of choice, only taking up a small fraction of the runtime of the full calculation. One library providing this functionality is PineAPPL, which we use to grid the predictions for open heavy-flavor production in the GM-VFNS.
| Category | Theory |
|---|---|
| Collaboration (if applicable) | nCTEQ |
