Training Programs (TEOSP)

This talk will be a brief and interactive introduction to my current research and the cosmological concepts that go along with it. I will assume limited prior knowledge of GR and cosmology so that even students in unrelated fields can learn something interesting.

When dealing with QFTs or with quantum mechanical systems in the thermodynamic limit, one encounters uncountable Hilbert spaces. These are mathematically wilder and physically less realistic than their countable counterparts. In this talk, we will explore how vacua encode a choice to reduce the Hilbert Space in size. In spite of the mathematically sounding intro, we’ll see this has interesting physical implications.

The connected wedge theorem states that in order to have a scattering process in the bulk, it is necessary to have O(1/G_N) mutual information between certain “decision” regions in the boundary theory. While this large mutual information is not generally sufficient to imply scattering, previous literature showed that for a certain class of geometries, bulk scattering is implied by a certain relation between two (possibly non-minimal) Ryu–Takayanagi surfaces. In this work, we show that the 2-to-2 version of the theorem becomes an equivalence in pure AdS3: large mutual information between appropriate boundary subregions is both necessary and sufficient for bulk scattering. This result allows us to extend previous findings to a broader class of asymptotically AdS3 spacetimes, which we illustrate with the spinning conical defect geometry. In contrast, we find that matter sources can disrupt this converse relation, and that the n-to-n version of the theorem with n>2 lacks a converse even in the AdS3 vacuum.

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.

We present the first measurement of primordial non-Gaussianity from the cross-correlation between 1.2 million spectroscopically confirmed quasars from the first data release (DR1) of the Dark Energy Spectroscopic Instrument (DESI) and the Planck PR4 CMB lensing maps. The analysis is performed in three tomographic redshift bins covering 0.8 < z < 3.5, covering a sky fraction of ~20%. We adopt a catalog-based pseudo-C_l estimator and apply linear imaging weights validated on noiseless mocks. Compared to previous analyses using photometric quasar samples, our results benefit from the high purity of the DESI spectroscopic sample, the reduced noise of PR4 lensing, and the absence of excess large-scale power in the spectroscopic quasar auto-correlation. Fitting simultaneously for the non-Gaussianity parameter f_NL and the linear bias amplitude in each redshift bin, we obtain f_NL} = 2^{+28}_{-34} for a response parameter p=1.6, and f_NL = 6^{+20}_{-24} for p=1.0. These results improve the constraints on f_NL by $\sim 35\%$ compared to the previous analysis based on the Legacy Imaging Survey DR9. Moreover, despite relying solely on cross-correlations over a smaller sky area, our constraints are competitive with the best current limits from photometric quasar catalogs, coming from an analysis performed with the \emph{Quaia} sample. Our results demonstrate the statistical power of DESI quasars for probing inflationary physics, and highlight the promise of future DESI data releases.

In the first part of the session I'll give my thoughts on why you should make the effort needed to give great talks, some strategies and advice on how to do so, and some common pitfalls to avoid. In the second part I'll open up to your questions about giving talks, writing papers, or other aspects of scientific communication.