Center for Theoretical Physics

As the partons in a jet propagate through the quark-gluon plasma (QGP) produced in a heavy-ion collision, they lose energy to, kick, and are kicked by the medium. The resulting modifications to the parton shower encode information about the microscopic nature of QGP. The momentum and energy lost by the parton shower are gained by the medium and, since QGP is a strongly coupled liquid, this means that the jet excites a wake in the droplet of QGP. After freezeout, this wake becomes soft hadrons with net momentum in the jet direction meaning that reconstructed jets include hadrons originating from both the modified parton shower and its wake. This makes it challenging to find an unambiguous experimental view of the response of a droplet of QGP to a jet. Recent years have seen significant advances in the understanding of the substructure of jets using correlation functions of the energy flux operator. So far, such studies have focused primarily on the two-point correlator, which serves to identify the angular scale of the underlying dynamics. Higher-point correlators hold the promise of mapping out the dynamics themselves. We perform the first study of the shape-dependent three-point energy-energy-energy correlator in heavy-ion collisions. Using the Hybrid Model to simulate the interactions of high energy jets with QGP, we show that hadrons originating from wakes are the dominant contribution to the three-point correlator in the regime where the three points are well-separated in angle, forming a roughly equilateral triangle. This equilateral region of the correlator is far from the region populated by collinear vacuum emissions, making it a canvas on which jet wakes can be imaged. Our work is a key step towards the systematic use of energy correlators to image and unravel the dynamical response of a droplet of QGP to a passing jet, and motivates many experimental and theoretical studies.

Deconvolving ("unfolding'') detector distortions is a critical step in the comparison of cross section measurements with theoretical predictions in particle and nuclear physics. However, most existing approaches require histogram binning while many theoretical predictions are at the level of statistical moments. We develop a new approach to directly unfold distribution moments as a function of another observable without having to first discretize the data. Our Moment Unfolding technique uses machine learning and is inspired by Generative Adversarial Networks (GANs). We demonstrate the performance of this approach using jet substructure measurements in collider physics. With this illustrative example, we find that our Moment Unfolding protocol is more precise than bin-based approaches and is as or more precise than completely unbinned methods.

Quantum dynamics can be analyzed via the structure of energy eigenstates. However, in the many-body setting, preparing eigenstates associated with finite temperatures requires time scaling exponentially with system size. In this work we present an efficient quantum search algorithm which produces quasi-stationary states, having energies supported within narrow windows of a dense many-body spectrum. In time scaling polynomially with system size, the algorithm produces states with inverse polynomial energy width, which can in turn be used to analyze many-body dynamics out to polynomial times. The algorithm is based on quantum singular value transformations and quantum signal processing, and provides a quadratic speedup over measurement-based approaches. We discuss how this algorithm can be used as a primitive to investigate the mechanisms underlying thermalization and hydrodynamics in many-body quantum systems.

We present an exact solution for the linearized junction array modes of the superconducting qubit fluxonium in the absence of array disorder. This solution holds for arrays of any length and ground capacitance, and for both differential and grounded devices. Array mode energies are determined by roots of convex combinations of Chebyshev polynomials, and their spatial profiles are plane waves. We also provide a simple, approximate solution, which estimates array mode properties over a wide range of circuit parameters, and an accompanying Mathematica file that implements both the exact and approximate solutions.