Center for Theoretical Physics

The black hole entropy has been observed to generically turn negative at exponentially low temperatures \(T\sim e^{-S_0}\) in the extremal Bekenstein-Hawking entropy \(S_0\), a seeming pathology often attributed to missing non-perturbative effects. In fact, we show that this negativity must happen for any effective theory of quantum gravity with an ensemble description. To do so, we identify the usual gravitational entropy as an annealed entropy \(S_a\), and prove that this quantity gives \(S_0\) at extremality if and only if the ground-state energy is protected by supersymmetry, and diverges negatively otherwise. The actual thermodynamically-behaved quantity is the average or quenched entropy \(S_q\), whose calculation is poorly understood in gravity: it involves replica wormholes in a regime where the topological expansion breaks down. Using matrix integrals we find new instanton saddles that dominate gravitational correlators at \(T\sim e^{-S_0}\) and are dual to semiclassical wormholes involving dynamical branes. These brane solutions give the leading contribution to any black hole very near extremality, and a duality with matrix ensembles would not make sense without them. In the non-BPS case, they are required to make \(S_q\) non-negative and also enhance the negativity of \(S_a\), both effects consistent with matrix integrals evaluated exactly. Our instanton results are tested against the on-shell action of D3-branes dual to multiply wrapped Wilson loops in \(\mathcal{N}=4\) super-YM, and a precise match is found. Our analysis of low-energy random matrix spectra also explains the origin of spectral gaps in supersymmetric theories, not only when there are BPS states at zero energy, but also for purely non-BPS supermultiplets. In the former, our prediction for the gap in terms of the degeneracy of BPS states agrees with the R-charge scaling in gapped multiplets of \(\mathcal{N}=2\) super-JT gravity.

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.