Search Talks

Motivated by the frame independence of the Bekenstein-Hawking black hole entropy, and the possibility of it being fundamentally entanglement entropy, I will present a spacetime definition for the entanglement entropy of free quantum fields. This definition is a spectral formulation, conducive to frame independent regularization. I will then go on to show an example calculation in Rindler spacetime, and comment on more general applications.

This study presents the modeling of the gravitational wave (GW) bias parameter by bridging a connection between simulated GW sources and galaxies in low redshift galaxy surveys 2MPZ and WISExSCOS (WISC). We study this connection by creating a mock GW catalog, 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. We probe the observable consequences of this connection by exploring the spatial clustering of the GW sources in terms of the GW bias parameter. We consider a phenomenological broken power law model for the host-galaxy probability function, with a potential turnover M_K at high stellar mass (10^{11} solar mass in the fiducial model) 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 M_K increases (and gets suppressed as M_K decreases). 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. Future measurements of the GW bias can help constrain M_K and the slopes of the host-galaxy probability function and thus offer insights into the underlying astrophysical processes.

We present a tensorization algorithm for constructing tensor train representations of functions, drawing on sketching and cross interpolation ideas. The method only requires black-box access to the target function and a small set of sample points defining the domain of interest. Thus, it is particularly well-suited for machine learning models, where the domain of interest is naturally defined by the training dataset. We show that this approach can be used to enhance the privacy and interpretability of neural network models. Specifically, we apply our decomposition to (i) obfuscate neural networks whose parameters encode patterns tied to the training data distribution, and (ii) estimate topological phases of matter that are easily accessible from the tensor train representation. Additionally, we show that this tensorization can serve as an efficient initialization method for optimizing tensor trains in general settings, and that, for model compression, our algorithm achieves a superior trade-off between memory and time complexity compared to conventional tensorization methods of neural networks.

In this talk, I will explain the concept of fault tolerance, which ensures reliable quantum computation. Building on recent advancements in mixed-state phases of matter, I introduce a new diagnostic called the spacetime Markov length. The divergence of this length scale signals the intrinsic breakdown of fault tolerance.

In the panel discussion, each panelist will showcase examples of well-written papers and share one essential "do" and one important "don't" for crafting effective scientific papers. The session will include an open Q&A segment to encourage audience engagement.