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

BitHEP — The limits of low-precision ML in HEP

Large language models – the future of fundamental physics?

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

Foundation model framework for all tasks involving jet physics

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

A universal vision transformer for fast calorimeter simulations

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

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

Accurate and robust methods for direct background estimation in resonant anomaly detection

Tools for unbinned unfolding

OmniJet-α_C: learning point cloud calorimeter simulations using generative transformers

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