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CERN and Mistral have signed a statement of intent for a long-term partnership to build a Large Physics Model — the first foundation model for collider physics — trained on billions of simulated LHC proton-proton collisions to support tasks from particle reconstruction to anomaly detection.
The article argues that the decision by CERN and Fermilab to abandon Scientific Linux in favor of CentOS was a mistake, as it removed a critical institutional exit option, highlighted by recent moves towards Debian.
Presents NEXUS, a lightweight foundation model with ~3M parameters pre-trained on LHC collision data, demonstrating improved downstream performance and cross-domain transfer to gravitational waves, flood forecasting, and neural activity.
This paper presents a reinforcement learning approach for dynamically adjusting trigger thresholds at the Large Hadron Collider, improving signal efficiency and maintaining background rates, with the first demonstration on real collision data.
Proposes a Wasserstein-GAN approach for unsupervised calibration of sensor-induced distribution drifts, validated on tracking detector toy models and simulated calorimeter data with aging effects.
This paper introduces Quiver, a paradigm that enriches classical machine learning models with quantum-inspired features derived from the quantum Fisher information matrix, demonstrating improvements on molecule property prediction and jet flavor classification benchmarks.
Researchers are developing a new understanding of lightning initiation, finding that high-energy processes typically associated with supernovas and particle colliders may play a critical role in how lightning bolts form. Scientists like Joseph Dwyer have applied astrophysics instruments to thunderstorm research, revealing X-rays, gamma rays, and unexpected bolt directions.