6 papers
Machine-learned RG-improved gauge actions and classically perfect gradient flows
Kieran Holland, Andreas Ipp, David I. Müller +1
Extracting continuum properties of quantum field theories from discretized spacetime is challenging due to lattice artifacts. Renormalization-group (RG)-improved lattice actions ca…
The (3+1)D structure of the dilute Glasma
Andreas Ipp, Markus Leuthner, David I. Müller +3
We study the (3+1)D structure of the Glasma in the dilute approximation, which allows us to describe the longitudinal dynamics that arise from the three-dimensional nuclear structu…
Charged particle multiplicity in pp-collisions from the dilute Glasma
Andreas Ipp, Markus Leuthner, David I. Müller +3
Proton-proton collisions are studied in the dilute Glasma framework. Compared to experimental multiplicity distributions, the dilute Glasma underestimates large multiplicity events…
Limiting fragmentation in the dilute Glasma
Andreas Ipp, Markus Leuthner, David I. Müller +3
We discuss the local longitudinal scaling behavior of the dilute Glasma. We gain insight into how the fragmentation region is dominated by the longitudinal structure of one of the…
HMC and gradient flow with machine-learned classically perfect fixed-point actions
Kieran Holland, Andreas Ipp, David I. Müller +1
Fixed-point (FP) lattice actions are classically perfect, i.e., they have continuum classical properties unaffected by discretization effects and are expected to have suppressed la…
Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics
Gert Aarts, Kenji Fukushima, Tetsuo Hatsuda +4
The integration of deep learning techniques and physics-driven designs is reforming the way we address inverse problems, in which accurate physical properties are extracted from co…