Describing hadronization via histories and observables for Monte-Carlo event reweighting

Abstract

We introduce a novel method for extracting a fragmentation model directly from experimental data without requiring an explicit parametric form, called Histories and Observables for Monte-Carlo Event Reweighting (HOMER), consisting of three steps: the training of a classifier between simulation and data, the inference of single fragmentation weights, and the calculation of the weight for the full hadronization chain. We illustrate the use of HOMER on a simplified hadronization problem, a q ¯q string fragmenting into pions, and extract a modified Lund string fragmentation function f (z). We then demonstrate the use of HOMER on three types of experimental data: (i) binned distributions of high-level observables, (ii) unbinned event-by-event distributions of these observables, and (iii) full particle cloud information. After demonstrating that f (z) can be extracted from data (the inverse of hadronization), we also show that, at least in this limited setup, the fidelity of the extracted f (z) suffers only limited loss when moving from (i) to (ii) to (iii).