通过用于蒙特卡洛事件重加权的历史与可观测量描述强子化
Describing Hadronization via Histories and Observables for Monte-Carlo Event Reweighting
AI总结：
该研究提出HOMER方法，无需显式参数形式直接从实验数据提取碎裂模型，通过三步实现蒙特卡洛事件重加权，在简化强子化问题和三类实验数据上验证，能提取修改后的碎裂函数且保真度损失有限。
摘要：
我们提出一种无需显式参数形式、直接从实验数据中提取碎裂模型的新方法，称为用于蒙特卡洛事件重加权的历史与可观测量（HOMER），包含三个步骤：训练模拟与数据间的分类器、推断单个碎裂权重、计算完整强子化链的权重。我们在简化强子化问题（q\bar{q}弦碎裂为π介子）上演示HOMER的应用，提取修改后的伦德弦碎裂函数f(z)；随后在三类实验数据上验证：(i) 高级可观测量的分箱分布，(ii) 这些可观测量的无分箱逐事件分布，(iii) 完整粒子云信息。在证明可从数据中提取f(z)（强子化逆问题）后，我们还发现，至少在该受限设置中，从(i)到(ii)再到(iii)时提取的f(z)保真度仅受有限损失。公开代码见https://gitlab.com/uchep/mlhad。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
