Talks

Horizon-scale images from the Event Horizon Telescope (EHT) now reveal two stable features of M87⋆: a dark inner shadow and a bright, polarized ring. These two features provide clean and robust standing points for new-physics searches. In the shadow region, any model that adds microwave emission would partially illuminate it, so the observed darkness becomes a background-free intensity test that yields strong bounds, which we showcased with dark matter annihilation. On the ring, the differential linear polarization angle between observing days, predicted and observed to be nearly zero, cancels accretion flow systematics; its stability leads to leading limits on the axion–photon coupling in the ultralight mass range, assuming a superradiant origin. I will also highlight recent progress on controlling observational uncertainties, strengthening which parts of these conclusions can be regarded as robust.

The Hubble tension, recently reaching as high as 7σ, increasingly presents a challenge to the ΛCDM model. A key question is whether this tension stems from our use of the sound horizon as a standard ruler. In this talk, I will present a new, sub-2% measurement of the Hubble constant (H₀) that is independent of the sound horizon scale, using data from the first data release of the Dark Energy Spectroscopic Instrument (DESI). The analysis employs a power spectrum rescaling technique that marginalizes over the sound horizon information, drawing instead on the matter-radiation equality scale as a standard ruler. Combining DESI’s full-shape galaxy clustering with uncalibrated post-reconstruction BAO and the CMB acoustic scale θ*, along with various external sound horizon-free datasets, results in highly robust constraints that are the most precise to date from LSS. Looking ahead, this measurement offers a new window on beyond-ΛCDM physics and the physical origin of the Hubble tension.

Machine-learning-based variational Monte Carlo simulations are a promising approach for targeting quantum many-body ground states, especially in two dimensions and in cases where the ground state is known to have a non-trivial sign structure. While many state-of-the-art variational energies have been reached with these methods for finite-size systems, little work has been done to use these results to extract information about the target state in the thermodynamic limit. In this work, we employ recurrent neural networks (RNNs) as a variational ansätze, and leverage their recurrent nature to simulate the ground states of progressively larger systems through iterative retraining. This transfer learning technique allows us to simulate spin-1/2 systems on very large lattices without beginning optimization from scratch for each system size, thus reducing the demands for computational resources. We first examine the square-lattice Heisenberg antiferromagnet, where it is possible to carefully benchmark our results. We also study the more challenging, sign-problematic triangular-lattice Heisenberg antiferromagnet. In both cases, we show that we are able to systematically improve the accuracy of our simulations by increasing the training time. Furthermore, we use our finite-size results to extract accurate estimates of  ground-state properties in the thermodynamic limit. These works demonstrate that RNN wavefunctions are able to extract accurate information about quantum many-body systems in the thermodynamic limit.

In this talk, I will discuss the projects I have done in my Ph.D. https://scholar.google.com/citations?user=SiMLZi8AAAAJ&hl=en. My research lies at the intersection of quantum many-body physics and quantum information, aiming to unravel the intrinsic complexity of many-body dynamics using both analytical methods and quantum informational approaches. In the first direction, we develop several analytical frameworks, including solvable quantum circuit dynamics, tensor-network states, and non-equilibrium field-theoretic calculations, to study both non-equilibrium physics and open-system dynamics. In particular, we proposed the concept of mixed state deep thermalization, a new paradigm that extends conventional thermalization to the ensemble of conditional mixed states. In the second direction, we investigate the quantum complexity of non-Markovian open many-body systems, establishing Lieb–Robinson bounds and determining the optimal digital-simulation resources required to simulate such dynamics.