Talks

Current waveform models of binary black hole mergers incorporate a large amount of analytical information during the inspiral phase. In contrast, post-merger descriptions typically rely on phenomenological ansätze informed by numerical relativity, with the quasi-normal mode frequencies providing the only direct analytical input. In this seminar, I will first give a gentle introduction to black hole perturbation theory and the Green’s function approach, which is useful for describing the time-domain response of a black hole to a dynamical source, e.g., an infalling test particle. I will then present an analytical framework to study the dynamical excitation of quasi-normal modes during the plunge–merger–ringdown stages for generic orbits, including highly eccentric ones, and discuss its implications for ringdown modeling. I will conclude by presenting novel insights into the modelling of the prompt response, fundamental to correctly capture the signal emitted during the merger at the linear level. 

As a class of generative artificial intelligence frameworks inspired by statistical physics, diffusion models have shown extraordinary performance in synthesizing complicated data distributions through a denoising process gradually guided by score functions. Real-life data, like images, is often spatially structured in low-dimensional spaces. However, ordinary diffusion models ignore this local structure and learn spatially global score functions, which are often computationally expensive. In this work, motivated by recent advances in statistical physics, we develop a generic framework for defining phases of data distributions and use it to analyze the locality requirements of denoisers in diffusion models. We define two distributions as belonging to the same data distribution phase if they can be mutually connected via spatially local operations such as local denoisers. We demonstrate that the reverse denoising process consists of an early trivial phase and a late data phase, sandwiching a rapid phase transition where local denoisers must fail. We further demonstrate that the performance of local denoisers is closely tied to spatial Markovianity, which provides an operational criterion for diagnosing such phase transitions. We validate this criterion through numerical experiments on real-world datasets. Our work suggests guidance for simpler and more efficient architectures of diffusion models: far from the phase transition point, we can use small local neural networks to compute the score function; global neural networks are only necessary around the narrow time interval of phase transitions. This result also opens up new directions for studying phases of data distributions, the broader science of generative artificial intelligence, and guiding the design of neural networks inspired by physics concepts.

Motivated by the observation that 2 + 2 = 4, we consider 4d N=2 SCFTs on S^2 x Sigma. On the one hand, reduction of a 4d theory T on a Riemann surface Sigma leads to a family F[T;Sigma] of 2d (2,2) unitary SCFTs, a 2d analog of the 4d theories of class S. On the other hand, reduction on S^2 yields a non-unitary two-dimensional CFT C[T] whose chiral algebra is the same as the one associated to T by the standard SCFT/VOA correspondence. This construction upgrades the vertex operator algebra to a full-fledged 2d CFT. What's more, it leads to a novel 2d/2d correspondence, a "2 + 2 = 4" analog of the "4 + 2 = 6" AGT correspondence: the S^2 partition function of F[T;Sigma] is computed by correlation functions of C[T] on Sigma. We discuss some structural aspects of this correspondence, and present an example where T is the (A1,A2) Argyres-Douglas SCFT and Sigma a four-punctured sphere. We also show how the elliptic genus of the F[T;Sigma] theories is captured by a TQFT on Sigma.

Galaxy clusters assemble hierarchically, and their present-day dynamical state encodes information about cluster formation timescales and mass distribution. The diffuse intracluster light (ICL), a fossil record of tidal stripping and accretion, offers a complementary probe that is sensitive to a cluster’s assembly history. In this talk I will present recent works based on photometric surveys (Euclid, UNIONS, DESI Legacy, KiDS) and cosmological hydrodynamic simulations (e.g. IllustrisTNG, Hydrangea), addressing two questions: (i) how observational tracers (e.g. magnitude gaps, galaxy-stellar-mass ratios) can be used to identify cluster dynamical state and its imprint on cluster properties; and (ii) what the ICL fraction and morphology reveal about the underlying mass and the stage of assembly. If time allows, I will briefly describe ongoing work with the FLAMINGO simulations that connects high-redshift protoclusters to their descendant clusters using observational indicators of environment and assembly.