arXiv Machine Learning
jBOT: Semantic Jet Representation Clustering Emerges from Self-Distillation
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arXiv AI
Jun 16
JetParticle-JEPA: An Efficient Self-Supervised Representation Learning method for Jet Tagging in High-Energy Physics
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.
arXiv Machine Learning
Sep 17
Similarity Pairing with Energy Mover's Distance for Self-Supervised Pre-Training at the LHC
The paper introduces a data‑driven method for pairing events at the Large Hadron Collider using the energy mover's distance (EMD) to measure similarity, thereby creating augmentation‑free views for self‑supervised pre‑training. By matching distinct events based on EMD, the approach preserves the physics content of each event without handcrafted distortions. Experiments on QCD jets demonstrate that this pairing technique yields semantic jet embeddings with downstream discrimination power comparable to or better than traditional augmentation‑based baselines.
arXiv Machine Learning
Jul 23
Enhancing next token prediction based pre-training for jet foundation models
arXiv:2512. 04149v2 Announce Type: replace-cross Abstract: Next token prediction is an attractive pre-training task for jet foundation models, in that it is simulation free and enables excellent generative capabilities that can transfer across datasets.
arXiv Machine Learning
Aug 17
Contrastive Learning for Interpretable Anomaly Detection at Collider Experiments
arXiv:2608. 13652v1 Announce Type: new Abstract: Generic event-level anomaly detection for collider physics has two recurring problems: anomaly scores are hard to interpret, and they correlate strongly with energy scale and object multiplicity.
arXiv Computer Vision
Sep 22
SAFe: Segment-guided Aggregation of Feature Densities for Anomaly-aware Segmentation
arXiv:2609.24204v1 Announce Type: new Abstract: Visual segmentation systems encounter objects outside their training distribution during real-world deployment, hindering reliable autonomous systems t...
arXiv Machine Learning
Jun 15
Machine-learned particle flow as a foundation model for collider physics
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.