Describing Hadronization via Histories and Observables for Monte-Carlo Event Reweighting

TL;DR

The paper presents HOMER, a new data-driven method for extracting hadronization models directly from experimental data without predefined parametric forms, demonstrated on simplified and real data, improving the fidelity of fragmentation functions.

Contribution

HOMER introduces a three-step approach to infer hadronization models directly from data, bypassing traditional parametric assumptions, and is validated on various data types.

Findings

Successfully extracted the fragmentation function $f(z)$ from data.

Limited fidelity loss observed when moving from binned to full particle data.

Publicly available code for the HOMER method.

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 $q\bar{q}$ 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). Public code is available at https://gitlab.com/uchep/mlhad.