Citing Articles For: Solving key challenges in collider physics with foundation models

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Articles Citing

Solving key challenges in collider physics with foundation models

jBOT: Semantic jet representation clustering emerges from self-distillation

Unfolding without iterations, adversaries, or surrogates

BitHEP — The limits of low-precision ML in HEP

Large language models – the future of fundamental physics?

OmniCosmos: Transferring Particle Physics Knowledge Across the Cosmos

Machine-Learned Particle Flow as a Foundation Model for Collider Physics

Encoding physics at the precision frontier

Conditional deep generative models for simultaneous simulation and reconstruction of entire events

Foundation model framework for all tasks involving jet physics

Foundation model for particle collision data analysis

Enhancing next token prediction based pre-training for jet foundation models

A universal vision transformer for fast calorimeter simulations

Mind the gap: navigating inference with optimal transport maps

Cross-geometry transfer learning in fast electromagnetic shower simulation

Pretrained event classification model for high energy physics analysis

Entanglement and Bell nonlocality in τ+τ− at the LHC using machine learning for neutrino reconstruction

Machine learning is good for physics—and vice versa

Reconstructing hadronically decaying tau leptons with a jet foundation model

Incorporating Physical Priors into Weakly Supervised Anomaly Detection

Machine learning assisted unfolding for neutrino cross-section measurements with the OmniFold technique

Anomaly-preserving contrastive neural embeddings for end-to-end model-independent searches at the LHC

Single interpolated generative model for anomalies

Optimizers for stabilizing likelihood-free inference

Fundamental limit of jet tagging

Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders

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