SciPost Submission Page
Describing Hadronization via Histories and Observables for Monte-Carlo Event Reweighting
by Christian Bierlich, Phil Ilten, Tony Menzo, Stephen Mrenna, Manuel Szewc, Michael K. Wilkinson, Ahmed Youssef, Jure Zupan
Submission summary
| Authors (as registered SciPost users): | Christian Bierlich · Tony Menzo · Stephen Mrenna · Manuel Szewc |
| Submission information | |
|---|---|
| Preprint Link: | https://arxiv.org/abs/2410.06342v3 (pdf) |
| Code repository: | https://gitlab.com/uchep/mlhad/-/tree/master/HOMER |
| Date accepted: | Jan. 22, 2025 |
| Date submitted: | Jan. 13, 2025, 1:41 p.m. |
| Submitted by: | Manuel Szewc |
| Submitted to: | SciPost Physics |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
|
| Approaches: | Computational, Phenomenological |
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.
Author indications on fulfilling journal expectations
- Provide a novel and synergetic link between different research areas.
- Open a new pathway in an existing or a new research direction, with clear potential for multi-pronged follow-up work
- Detail a groundbreaking theoretical/experimental/computational discovery
- Present a breakthrough on a previously-identified and long-standing research stumbling block
Author comments upon resubmission
List of changes
- We have added a more detailed explanation in section 2.2.3 on how one obtains the measured fragmentation function, which now also complements the discussion in section 3.1.1, where we describe how fig. 7 was obtained.
- We have added a warning regarding a source of possible overfitting in section 3, recommending the use of three datasets in case of more realistic applications.
- We have changed the binning of fig. 3 and others in that style to logarithmic.
- We have clarified our definition of point cloud and the use of a Deep Sets-based classifier in section 3.2.
- We have fixed the typos pointed out by the reports.
Published as SciPost Phys. 18, 054 (2025)