Talks

Addressing Quantum Chromodynamics (QCD) in the infrared is a notoriously difficult task due to the size of the interaction strength. This has instigated the development of non-perturbative approaches based either on the lattice discretization and the statistical evaluation of the QCD functional integral or on its continuum reformulation in terms of infinite hierarchies of equations. Lattice QCD provides an almost exact description of QCD that can however not be used in all situations. As for continuum approaches, even though they can be extended to these situations in principle, they very often rely on uncontrolled truncations that prevent a quantitative estimate of the error.Over the last decades, the lattice simulation of Euclidean QCD correlators in the Landau gauge has revealed unexpected features that shed some light on how the various QCD fields are coupled in the infrared. This allows one to contemplate a "third way" into infrared QCD. In this talk, I review a ten-year effort to put these ideas into solid ground, using the Curci-Ferrari action, a one-parameter model for Landau gauge-fixed QCD in the infrared. In particular, I will discuss how this approach allows one to retrieve the lattice results for the QCD correlation functions and to investigate features of the phase structure that relate to the most fundamental properties of QCD such as confinement or chiral symmetry breaking.

Current and future weak lensing surveys contain significant information about our universe. However, their optimal cosmological analysis is challenging, with traditional analyses often resulting in information loss due to reliance on summary statistics like two-point correlation functions. While deep learning methods offer promise in capturing the complex non-linear features of these cosmological fields, they often suffer from issues such as inadequate uncertainty quantification, susceptibility to distribution shifts, and interpretability limitations, which hinder their scientific applicability. In this talk, I propose a novel approach leveraging generative probabilistic modeling with Normalizing Flows to learn the data likelihood function at the field level, facilitating more effective cosmological information extraction. This framework not only enables anomaly detection of distribution shifts to improve the robustness of the analysis, but also fostering interpretability via generated samples. I will also discuss incorporating physical prior knowledge, such as symmetries and multiscale structure, into the model architectures to improve their generalization capabilities. Finally, I will explore the broader implications of deep probabilistic models in physics, highlighting their potential applications in diverse areas ranging from astronomical observations to high-energy physics and lattice field theory.

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