7 papers
Neural Unfolding of the Chiral Magnetic Effect in Heavy-Ion Collisions
Shuang Guo, Lingxiao Wang, Kai Zhou +1
The search for the chiral magnetic effect (CME) in relativistic heavy-ion collisions (HICs) is challenged by significant background contamination. We present a novel deep learning…
Physics-Conditioned Diffusion Models for Lattice Gauge Theory
Qianteng Zhu, Gert Aarts, Wei Wang +2
We develop diffusion models for simulating lattice gauge theories, where stochastic quantization is explicitly incorporated as a physical condition for sampling. We demonstrate the…
Towards constraining QCD phase transitions in neutron star interiors: Bayesian Inference with TOV linear response analysis
Ronghao Li, Sophia Han, Zidu Lin +3
The potential hadron-to-quark phase transition in neutron stars has not been fully understood as the property of cold, dense, and strongly interacting matter cannot be theoreticall…
Diffusion models and stochastic quantisation in lattice field theory
Gert Aarts, Lingxiao Wang, Kai Zhou
Diffusion models are currently the leading generative AI approach used for image generation in e.g. DALL-E and Stable Diffusion. In this talk we relate diffusion models to stochast…
Diffusion models learn distributions generated by complex Langevin dynamics
Diaa E. Habibi, Gert Aarts, Lingxiao Wang +1
The probability distribution effectively sampled by a complex Langevin process for theories with a sign problem is not known a priori and notoriously hard to understand. Diffusion…
Diffusion models for lattice gauge field simulations
Qianteng Zhu, Gert Aarts, Wei Wang +2
We develop diffusion models for lattice gauge theories which build on the concept of stochastic quantization. This framework is applied to gauge theory in dimensions.…