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1. Post-hoc reweighting of hadron production in the Lund string model

   Assi, Benoit; Bierlich, Christian; Ilten, Phil; Menzo, Tony; Szewc, Manuel; Wilkinson, Michael; Youssef, Ahmed; Zupan, Jure (April 2025, arXiv:2505.00142)

   Assi, Benoit; Bierlich, Christian; Ilten, Phil; Menzo, Tony; Szewc, Manuel; Wilkinson, Michael; Youssef, Ahmed; Zupan, Jure (Ed.)

   We present a method for reweighting flavor selection in the Lund string fragmentation model. This is the process of calculating and applying event weights enabling fast and exact variation of hadronization parameters on pre-generated event samples. The procedure is post hoc, requiring only a small amount of additional information stored per event, and allowing for efficient estimation of hadronization uncertainties without repeated simulation. Weight expressions are derived from the hadronization algorithm itself, and validated against direct simulation for a wide range of observables and parameter shifts. The hadronization algorithm can be viewed as a hierarchical Markov process with stochastic rejections, a structure common to many complex simulations outside of high-energy physics. This perspective makes the method modular, extensible, and potentially transferable to other domains. We demonstrate the approach in Pythia, including both numerical stability and timing benefits. 

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2. Data assimilation for online model calibration in discrete event simulation

   https://doi.org/10.1177/00375497231221578  

   Hu, Xiaolin; Yan, Mingxi (June 2024, SIMULATION)

   The increasing availability of real-time data collected from dynamic systems brings opportunities for simulation models to be calibrated online for improving the accuracy of simulation-based studies. Systematical methods are needed for assimilating real-time measurement data into simulation models. This paper presents a particle filter-based data assimilation method to support online model calibration in discrete event simulation. A joint state-parameter estimation problem is defined, and a particle filter-based data assimilation algorithm is presented. The developed method is applied to a discrete event simulation of a one-way traffic control system. Experiments results demonstrate the effectiveness of the developed method for calibrating simulation models’ parameters in real time and for improving data assimilation results. 

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3. Herwig 7.3 release note

   https://doi.org/10.1140/epjc/s10052-024-13211-9  

   Bewick, Gavin; Ferrario_Ravasio, Silvia; Gieseke, Stefan; Kiebacher, Stefan; Masouminia, Mohammad R; Papaefstathiou, Andreas; Plätzer, Simon; Richardson, Peter; Samitz, Daniel; Seymour, Michael H; et al (October 2024, The European Physical Journal C)

   Abstract A new release of the Monte Carlo event generator (version 7.3) has been launched. This iteration encompasses several enhancements over its predecessor, version 7.2. Noteworthy upgrades include: the implementation of a process-independent electroweak angular-ordered parton shower integrated with QCD and QED radiation; a new recoil scheme for initial-state radiation improving the behaviour of the angular-ordered parton shower; the incorporation of the heavy quark effective theory to refine the hadronization and decay of excited heavy mesons and heavy baryons; a dynamic strategy to regulate the kinematic threshold of cluster splittings within the cluster hadronization model; several improvements to the structure of the cluster hadronization model allowing for refined models; the possibility to extract event-by-event hadronization corrections in a well-defined way; the possibility of using the string model, with a dedicated tune. Additionally, a new tuning of the parton shower and hadronization parameters has been executed. This article discusses the novel features introduced in version 7.3.0. 

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

   https://doi.org/10.21468/SciPostPhys.18.2.054  

   Bierlich, Christian; Ilten, Philip; Menzo, Tony; Mrenna, Stephen; Szewc, Manuel; Wilkinson, Michael K; Youssef, Ahmed; Zupan, Jure (February 2025, SciPost Physics)

   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, aq\bar{q} $q\bar{q}$ string fragmenting into pions, and extract a modified Lund string fragmentation functionf(z) $f\left(z\right)$ . 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 thatf(z) $f\left(z\right)$ can be extracted from data (the inverse of hadronization), we also show that, at least in this limited setup, the fidelity of the extractedf(z) $f\left(z\right)$ suffers only limited loss when moving from (i) to (ii) to (iii). Public code is available at https://gitlab.com/uchep/mlhad. 

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5. Towards a data-driven model of hadronization using normalizing flows

   https://doi.org/10.21468/SciPostPhys.17.2.045  

   Bierlich, Christian; Ilten, Philip; Menzo, Tony; Mrenna, Stephen; Szewc, Manuel; Wilkinson, Michael K; Youssef, Ahmed; Zupan, Jure (August 2024, SciPost Physics)

   We introduce a model of hadronization based on invertible neural networks that faithfully reproduces a simplified version of the Lund string model for meson hadronization. Additionally, we introduce a new training method for normalizing flows, termed MAGIC, that improves the agreement between simulated and experimental distributions of high-level (macroscopic) observables by adjusting single-emission (microscopic) dynamics. Our results constitute an important step toward realizing a machine-learning based model of hadronization that utilizes experimental data during training. Finally, we demonstrate how a Bayesian extension to this normalizing-flow architecture can be used to provide analysis of statistical and modeling uncertainties on the generated observable distributions. 

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