arXiv:2606. 14813v1 Announce Type: cross Abstract: Jet tagging at the Large Hadron Collider increasingly relies on deep learning models trained on massive simulated datasets, leading to high computational costs and limited robustness to detector mismodeling.
By Guillaume Letellier (LPCC), Antonin Vacheret (LPCC), Fr\'ed\'eric Jurie
arXiv:2610.15919v1 Announce Type: cross
Abstract: Supervised machine learning in high-energy physics (HEP) requires training on labeled simulations of particle collisions, which are known to be unfai...
By Zichun Hao, Raghav Kansal, Abhijith Gandrakota, Chang Sun, Haoyang Li, Zihan Zhao, Ngadiuba Jennifer, Javier Duarte, Maria Spiropulu
arXiv:2601.11719v4 Announce Type: replace
Abstract: Self-supervised learning, in the context of foundation model training, is a powerful pre-training method for learning feature representations witho...
By Ho Fung Tsoi, Dylan Rankin
arXiv:2412. 10665v3 Announce Type: replace-cross Abstract: We introduce a foundation model for event classification in high-energy physics, built on a Graph Neural Network architecture and trained on 120 million simulated proton-proton collision events spanning 12 distinct physics processes.
By Joshua Ho, Benjamin Ryan Roberts, Shuo Han, Haichen Wang
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
The paper investigates anomaly detection in high-energy physics by learning low-dimensional representations of collision events. Using supervised and self-supervised neural embeddings on a large simulated dataset, the authors find that supervised objectives generally outperform self-supervised ones and improve signal efficiency compared to traditional kinematic-feature autoencoders. They also propose a local diagnostic pipeline that models the Standard Model latent space with a Gaussian Mixture Model, profiles components via high-level observables, and ranks these observables by their local discriminating power, thereby linking anomaly scores to specific kinematic features.
By Donatella Genovese, Philip Ploner, Simone Scardapane, Stefano Giagu, Thea Klaeboe Aarrestad