The paper introduces a particle‑level generative model that uses residual‑quantized full‑event data to enable fast, ML‑based surrogate simulation for collider events. It demonstrates conditional generation from detector‑stable particles, explores scaling across dataset and model sizes, and shows that token‑level loss predicts downstream physical fidelity. The work offers an empirical framework for scalable collider full‑event generation using residual‑quantized representations.
By Dan Godi, Dmitrii Kobylianskii, Eilam Gross
The paper introduces a model calibration method using optimal transport to address discrepancies between simulation and experimental data in high-dimensional machine learning applications. Applied to jet tagging in particle physics, the technique calibrates a 128‑dimensional latent representation from a general‑purpose classifier, ensuring downstream derived quantities are properly calibrated. This enables more reliable use of foundation models for jet flavor analysis in LHC experiments and offers a general framework for correcting high‑dimensional simulations across scientific fields.
By Malte Algren, Tobias Golling, Francesco Armando Di Bello, Christopher Pollard
The paper introduces a Deep Sets surrogate for optimal transport (OT) that respects key metric properties—non-negativity, exchange symmetry, and zero self-distance—while leaving the triangle inequality unconstrained. Applied to the Energy Mover's Distance between collider events, the Metric-Aware Particle Flow Network achieves percent‑level mean absolute percentage error and markedly higher inference throughput compared to other exact and approximate methods. The architectural constraints also dramatically reduce triangle‑inequality violations, improving geometric fidelity across a large set of held‑out event triplets.
By Lauren Hay, Rishabh Jain, Matt LeBlanc, Jennifer Roloff
arXiv:2609.40287v1 Announce Type: new
Abstract: Physics-constrained generative models aim to generate physical fields that match a target distribution and satisfy prescribed constraints. However, enf...
By Zhangyong Liang, Haibin Ling
arXiv:2606. 14373v1 Announce Type: cross Abstract: The workflow from particle collision to physics analysis passes through a series of reconstruction steps that are traditionally modular and disconnected, with no shared representation linking low-level detector data to high-level analysis tasks.
By Farouk Mokhtar, Joosep Pata, Michael Kagan, Javier Duarte
arXiv:2605. 17985v2 Announce Type: replace-cross Abstract: We propose a new method for compressing physics foundation models (PFMs) which is a new trend in AI for Science.
By Chengjie Hong, Feixiang He, Yiheng Zeng, Lulu Kang, He Wang