Center for Theoretical Physics

"Attractor" solutions for the pre-hydrodynamic, far-from-equilibrium, evolution of the matter produced in relativistic heavy ion collisions have emerged as crucial descriptors of the rapid hydrodynamization of quark-gluon plasma (QGP). Adiabatic Hydrodynamization (AH) has been proposed as a framework with which to describe, explain, and predict attractor behavior that draws upon an analogy to the adiabatic approximation in quantum mechanics. In this work, we systematize the description of pre-hydrodynamic attractors in kinetic theory by showing how to use the AH framework to identify these long-lived solutions to which varied initial conditions rapidly evolve, demonstrating the robustness of this framework. In a simplified QCD kinetic theory in the small-angle scattering limit, we use AH to explain both the early- and late-time scaling behavior of a longitudinally expanding gluon gas in a unified framework. In this context, we show that AH provides a unified description of, and intuition for, all the stages of what in QCD would be bottom-up thermalization, starting from a pre-hydrodynamic attractor and ending with hydrodynamization. We additionally discuss the connection between the notions of scaling behavior and adiabaticity and the crucial role of time-dependent coordinate redefinitions in identifying the degrees of freedom of kinetic theories that give rise to attractor solutions. The tools we present open a path to the intuitive explanation of how attractor behavior arises and how the attractor evolves in all stages of the hydrodynamization of QGP in heavy ion collisions.

Extracting scientific understanding from particle-physics experiments requires solving diverse learning problems with high precision and good data efficiency. We propose the Lorentz Geometric Algebra Transformer (L-GATr), a new multi-purpose architecture for high-energy physics. L-GATr represents high-energy data in a geometric algebra over four-dimensional space-time and is equivariant under Lorentz transformations, the symmetry group of relativistic kinematics. At the same time, the architecture is a Transformer, which makes it versatile and scalable to large systems. L-GATr is first demonstrated on regression and classification tasks from particle physics. We then construct the first Lorentz-equivariant generative model: a continuous normalizing flow based on an L-GATr network, trained with Riemannian flow matching. Across our experiments, L-GATr is on par with or outperforms strong domain-specific baselines.

We update and extend a previous N\(^3\)LL\(^\prime\)+\({\cal O}(\alpha_s^3)\) strong coupling determination from thrust data. In particular, we carry out a fit with data fully restricted to the dijet region seeking to minimize the potential impact of power corrections that go beyond dijet configurations. In addition, we parametrize deviations from the dijet power correction in order to add an additional source of uncertainty in the result for \(\alpha_s(m_Z)\). We also show that the inclusion of resummation is important to achieve stability with respect to varying the fit region.

We demonstrate that "natural inflation", also known as "axion inflation", can be compatible with Planck 2018 measurements of the cosmic microwave background, while predicting an exponentially small tensor-to-scalar ratio, e.g., \(r\sim 10^{-15}\). The strong suppression of \(r\) arises from dynamics of the radial component of the complex scalar field, whose phase is the axion. Such tiny values of \(r\) remain well below the threshold for detection by CMB-S4 or Simons Observatory B-mode searches. The model is testable with the running \(\alpha_s\) of the spectral index, which is within reach of next-generation CMB and large-scale structure experiments, motivating the running as a primary science goal for future experiments.