Scientific Series

We study the spectrum of local operators living on a defect in a generic conformal field theory, and their coupling to the local bulk operators.  We establish the existence of universal accumulation points in the spectrum at large s, s being the charge of the operators under rotations in the space transverse to the defect. Our tools include a formula that inverts the bulk to defect OPE and is analytic in s, analogous to the Caron-Huot formula for the four-point function. Some important assumptions are made in deriving this result: we comment on them.

In the last decades, advances in the level of precision in controlling atomic and optical systems opened up the low-energy precision frontier to fundamental physics tests. Exploitation of quantum entanglement in such systems to further improve the sensitivity of certain existing approaches is currently an active field of research. Drawing from the experiments in our lab, in this talk I will focus on the properties, generation and usage of a particular set of entangled states called spin squeezed states.

The lensing convergence measurable with future CMB experiments will be highly correlated with the clustering of galaxies that will be observed by imaging surveys such as LSST. I will discuss prospects for using that cross-correlation signal to constrain local primordial non-Gaussianity, the amplitude of matter fluctuations as a function of redshift, halo bias, and possibly the sum of neutrino masses. A key limitation for such analyses and large-scale structure analyses in general is that the mapping from initial conditions to observables is nonlinear for wavenumbers k>0.1h/Mpc. This can destroy cosmological information or move it to non-Gaussian tails of the probability distribution that are difficult to measure. I will describe how we can use recently developed initial condition reconstruction methods to help us recover some of that information in the nonlinear regime.

Time series data contains useful information on the phase of a system. Here we propose the use of recurrent neural networks (LSTM) to learn and extract such information in order to classify phases and locate phase boundaries. We demonstrate this on a many-body localized model, and attempt to interpret the learned behavior by looking at individual LSTM cells. We also discuss the validity of the learned model and investigate its limits.

The causal structure of spacetimes containing fully evaporated black holes is considered from the perspective of Lorentzian geometry. The starting point is provided by theorems, due to Kodama, Geroch and Wald, that derive non-global hyperbolicity from a set of premises relating two partial Cauchy surfaces that are thought of as, respectively, lying before and after the evaporation. Here, we consider the Geroch-Wald theorem in the setting of a conformally embedded spacetime a la Penrose and show, under the assumption that complete null geodesics outside the black hole reach the boundary, that there exists visible incomplete null geodesics and that causal continuity fails.

In this talk, I will discuss two important cryptographic tasks in a post-quantum world. I will discuss the impossibility of bit-commitment and the possibility of physically unforgeable money in the framework of Generalized Probabilistic Theories. Our proofs rely crucially on cone programming, a natural generalization of semidefinite programming. This is based on work with John Selby, arXiv:1803.10279 and arXiv:1711.02662.  

Recently, we have shown that deep neural networks can be used to solve the Schrödinger Equation (https://journals.aps.org/pra/abstract/10.1103/PhysRevA.96.042113), classical spin models (https://doi.org/10.1103/PhysRevE.97.032119), and 2d-materials such as graphene and boron-nitride (https://doi.org/10.1016/j.commatsci.2018.03.005).

I will argue that these "deep models" can be used to simulate and understand matter in a way which was not previously possible. Specifically, I will show how our recently reported extensive deep neural networks (https://arxiv.org/abs/1708.06686) can be used to infer the properties of meso-scale materials based on training data generated from much smaller structural motifs (evaluated using electronic structure methods such as density functional theory). Extensive deep neural networks scale as O(N) and can be efficiently evaluated in parallel using petascale computational platforms.

The talk will focus on issues connected with the quantization of initial data for vacuum general relativity on null hypersurfaces. Complete and constraint free initial data for GR can be given on pairs of intersecting null hypersurfaces. These offer a route to a canonical quantization of GR which avoids having to deal with constraints. However the Poisson algebra of these data is rather intricate and it is not obvious how one might quantize it. This algebra is however almost the same in the cylindrically symmetric case as in the full theory, which suggests that the cylindrically symmetric theory ought to offer valuable insights. Here I present recent joint work with Javier Peraza and Miguel Paternain relating the Poisson algebra in the cylindrically symmetric case to well studied algebraic structures.

Two years ago the Laser Interferometer Gravitational-wave Observatory.

(LIGO) announced the first direct detection of gravitational waves; minute distortions in space-time caused by cataclysmic events far away in the universe.  Very recently, the merger of a binary neutron star system was detected by both of the Advanced LIGO detectors and the Advanced Virgo detector in Italy, triggering a massive follow-up campaign by ground and space-based telescopes.  A counterpart to the gravitational-wave source was located, and transient emission was detected from gamma rays to radio.  I will talk about the sources of the signals we detected, the physics behind the detectors, and prospects for the future of this emerging field.

When performing numerical simulation of many-body systems we work with the goal of computing some set of averaged observables.  This process has a weakness in that, when our simulation is complete, we have no ability to determine what was the key physics that gave our result.  In this talk, I'll give a simple example, a gap in a single-particle density of states, and explain how we can use two-particle quantities to recover key 'hidden' information.  I'll then back up these claims with numerical results for the 2D Hubbard model.  For the second half of my talk I'll hope to keep your attention by introducing a parallel but MUCH more whimsical idea, unparticle physics. I'll at least TRY to explain what an unparticle is and how it might impact strongly correlated condensed matter systems.

There seem to be no good examples of UV complete theories that have low-lying massive higher spin states isolated by a large gap, despite the relative ease of constructing effective field theories describing such states.  We discuss constraints from analytic dispersion relations and subluminality of eikonal scattering that may help to explain this and provide insight into the possible interactions of massive higher spins.

First I will introduce the concept of the shadow of a black hole and what it means for the shadows of two observers to be degenerate. I will then present
our results showing that no continuous
degenerations exist between the shadows of observers at any point in the exterior region of any Kerr-Newman black hole spacetime of unit mass. Therefore an observer can, by measuring the black holes shadow, in principle determine the angular momentum and the charge of the black hole under observation, as well as his radial distance from the black hole and his angle of elevation above the equatorial plane.