arXiv Machine Learning
Jun 4

CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters

arXiv:2606. 04165v1 Announce Type: cross Abstract: High-precision calorimeter simulation at current and future colliders imposes rapidly growing computational demands, motivating the development of machine-learning surrogates for traditional Monte Carlo tools such as Geant4.

By Cheng Jiang, Sitian Qian, Kevin Pedro, Oz Amram, Huilin Qu, Maggie Voetberg
arXiv Machine Learning
Jun 16

GPT-Based Fast Simulation of CLAS12 Detector Hits via Conditional Autoregressive Generation

arXiv:2606. 16035v1 Announce Type: cross Abstract: Modern particles physics experiments have demonstrated an increasing need for fast, high-fidelity detector simulation as detector components have improved and subsequent computational requirements approach the limits of available resources.

By Cole Granger, James Giroux, Richard Tyson, Maurizio Ungaro, Cristiano Fanelli
arXiv AI
Oct 15

Contrastive Metric Learning for Point Cloud Segmentation in Highly Granular Detectors

The paper introduces a clustering method for point‑cloud segmentation that uses supervised contrastive metric learning (CML) to embed points from the same particle shower close together while separating unrelated points. After learning this latent space, clusters are reconstructed with a density‑based readout, decoupling representation learning from cluster formation. Compared to object condensation, CML yields a more stable, separable embedding, improving local neighbourhood consistency, shower separation, and generalization to unseen multiplicities and energies, which translates into higher reconstruction efficiency, purity, and energy resolution in highly granular calorimeter data.

By Max Marriott-Clarke, Lazar Novakovic, Elizabeth Ratzer, Robert J. Bainbridge, Loukas Gouskos, Benedikt Maier
arXiv Machine Learning
Oct 3

Scaling Collider Event Generation with Residual-Quantized Tokens

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