Talks

Critical fluctuations coupled to Fermi surfaces can destroy conventional Fermi liquids, giving rise to diverse non-Fermi liquids that may remain metallic or become superconducting at zero temperature. I will present a classification of non-Fermi liquids with globally convex hot Fermi surfaces based on the concept of projective fixed points, which account for the flow of the Fermi momentum under renormalization. This framework organizes non-Fermi liquids into seven super-universality classes, each characterized by distinct superconducting fluctuations. Stable non-Fermi liquids exhibit universal but non-scale-invariant pairing interactions, while unstable ones display universal constraints on pairing symmetry, superconducting transition temperature, and its scaling with the Fermi momentum. I will discuss physical examples and a toy model that captures the essential universal properties of these classes.

I will introduce factorization algebras as an approach to understanding quantum field theory. I will outline some of the benefits of this approach, as well as how it connects with other topics in mathematical physics.

This study presents the modelling of the gravitational wave (GW) bias parameter by bridging between simulated GW sources and galaxies in low redshift galaxy surveys 2MPZ, WISExSCOS (WISC) and SDSS DR7. We study this connection by creating a mock host catalog for GW events and populating galaxy surveys with binary black holes (BBHs) for different scenarios of the GW host-galaxy probability as a function of the galaxy stellar mass, SFR and metallicity. For 2MPZ, WISExSCOS (WISC) galaxy surveys with only stellar mass information, we consider a phenomenological broken power law model for the host-galaxy probability function, with a potential turnover where the star formation efficiency begins to drop. We vary the parameters of the GW host-galaxy probability function and find that generically the GW bias increases as the turnover point increases. The change in the GW bias parameter shows a maximum change of about 30% for different scenarios explored in this work in comparison to the galaxy bias. For the SDSS DR7 survey with stellar mass, SFR and metallicity information, we use a joint host-galaxy probability function defined over stellar mass, star formation rate (SFR), and metallicity. This probability is modelled as the product of three broken power-law distributions, each with a turnover point motivated by astrophysical processes governing the relation between current-day galaxy properties and BBH mergers, such as galaxy quenching and BBH delay time. In this case, our results show that GW bias is most sensitive to host-galaxy probability dependence on stellar mass, with increases of up to ∼O(10)% relative to galaxy bias as the stellar mass pivot scale rises. We also find a notable relationship between GW bias and SFR: when the host-galaxy probability favors low-SFR galaxies, the GW bias significantly increases. In contrast, we observe no strong correlation between GW bias and metallicity. These findings suggest that the spatial clustering of GW sources is primarily driven by the stellar mass and SFR of their host galaxies and shows how GW bias measurements can inform models of the host-galaxy probability function.

Neural quantum states have emerged as a powerful framework for representing classical probability distributions derived from quantum many-body systems. However, fundamental questions remain about their computational learnability, particularly for physically relevant quantum states. We identify and analyze a fundamental obstacle to the efficient learning of locally indistinguishable quantum states using neural network architectures. We introduce a restricted statistical query (SQ) learning model that captures the essential features of noisy gradient descent training in autoregressive models such as recurrent neural networks networks. Within this framework, we prove that locally indistinguishable states cannot be learned in polynomial time, establishing an inherent computational barrier. Conversely, we show that distributions with finite Markov length remain efficiently learnable in the restricted SQ model. We demonstrate the practical implications through numerical experiments on paradigmatic examples of strong-to-weak symmetry breaking, including ferromagnetic ground states and syndrome distributions of quantum error-correcting codes beyond their decoding thresholds. Our findings suggest that the hardness of neural quantum state learning could serve as a novel computational probe for identifying mixed-state phase transitions and decoding thresholds in both numerical simulations and experiments.

I will talk about effective description of quantum dynamics for systems interacting with thermal environment. I will focus on a prototypical example of Brownian Motion and show how one can leverage symmetries to build a general effective path integral description with quantum/thermal corrections using methods of Non-Equilibrium QFT.

Recent advances have argued how gravitational entropies can be computed directly from Lorentzian path integrals, avoiding the problems of Euclidean methods associated with the conformal factor problem that makes a path integral over Euclidean geometries manifestly ill defined. In particular, the de Sitter horizon entropy can be recovered from a real-time path integral that computes the dimension of the Hilbert space associated with a spatial ball. Similarly, the swap entropy of an evaporating black hole (central to the replica paradigm resolution of the black hole information paradox) can be computed in this way. Quantum Regge Calculus, a lattice-like approach to quantum gravity, enables concrete realizations of these scenarios, providing insights beyond continuum methods. This perspective clarifies aspects of gravitational path integrals and may have implications for approaches such as causal dynamical triangulations and spin foams, where Regge-like formulations play a fundamental role.

For physical problems, the well-posedness of initial-boundary-value problems is crucial. Asking this question in general relativity, is is furthermore important to understand the necessary gauge choices and constraints. As different groups and communities ask similar questions coming from different backgrounds, it is important to be able to translate between different formulations. In this talk I will explain the approach I have taken so far, based on the 3+1 Einstein system, and highlight connections to other works, hoping to spark discussions with people from other areas of research.