arXiv Machine Learning
Jun 18
Predicting the Neutrino Mass Ordering Using Neural Networks
arXiv:2606. 03745v1 Announce Type: cross Abstract: Determining the neutrino mass ordering remains a central open problem in particle physics.
Uncovering Hidden Leptonic Correlations with Flow Matching and Autoencoders
arXiv:2608. 15042v1 Announce Type: cross Abstract: We perform a global search for values of the Yukawa matrices and Majorana masses in the Type-I seesaw mechanism.
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arXiv Machine Learning
Jul 20
Learning Standard Model structure from LHC data with Riemannian flow matching
arXiv:2607. 16144v1 Announce Type: cross Abstract: In this work we demonstrate that a single transformer-based generative model can capture Standard Model structure spanning five decades of invariant mass, from the sub-GeV regime to the TeV continuum, a range that no single Monte Carlo sample covers.
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
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.
arXiv AI
Jun 19
Towards Engineering Scaling Laws with Pretraining Data Composition
arXiv:2606. 19781v1 Announce Type: cross Abstract: Neural scaling laws describe how model performance improves as a power law in compute, model size, and dataset size.
arXiv Machine Learning
Jul 31
Emulating Cosmic Structure Formation with a Lagrangian Neural Cellular Automaton
arXiv:2607. 27320v1 Announce Type: cross Abstract: Field-level inference of cosmological initial conditions from galaxy surveys requires a forward model that is simultaneously accurate in the non-linear regime, computationally efficient, and fully differentiable.
arXiv Machine Learning
Jul 14
Forecasting Generative Amplification
arXiv:2509. 08048v4 Announce Type: replace-cross Abstract: Generative networks are perfect tools to enhance the speed and precision of LHC simulations.
arXiv Machine Learning
Jun 16
GPT-Based Fast Simulation of CLAS12 Detector Hits via Conditional Autoregressive Generation
arXiv:2606. 16035v1 Announce Type: cross Abstract: Modern particles physics experiments have demonstrated an increasing need for fast, high-fidelity detector simulation as detector components have improved and subsequent computational requirements approach the limits of available resources.
arXiv Machine Learning
Jul 14
Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthrough
arXiv:2607. 10039v1 Announce Type: cross Abstract: Machine learning (ML) has become integral to fundamental physics, accelerating statistical workflows from data acquisition through inference and hypothesis testing.
arXiv Machine Learning
1d ago
Diffusion-model approach to flavor models: A case study for $S_4^\prime$ modular flavor model
arXiv:2504. 00944v2 Announce Type: replace-cross Abstract: We propose a numerical method of searching for parameters with experimental constraints in generic flavor models by utilizing diffusion models, which are classified as a type of generative artificial intelligence (generative AI).
arXiv Machine Learning
Jul 15
Physically Consistent Parameter Inference: Transparent Machine Learning Emulation in High Energy Physics and Cosmology
arXiv:2607. 12726v1 Announce Type: cross Abstract: Global fits in high energy physics and cosmology often face the challenge of exploring high-dimensional parameter spaces with computationally expensive or topologically complex likelihood functions.
arXiv Machine Learning
Jun 19
Statistical Properties of Training & Generalization
arXiv:2606. 20299v1 Announce Type: cross Abstract: Deep learning has managed to evade numerous intuitions from classical statistics to achieve unprecedented performance on a number of real-world tasks.