arXiv subjects

Shradha Ramakrishnan

Publications and source records attributed to Shradha Ramakrishnan.

2 recordsLinked to original sources

arXiv2026

On flat space limits of $AdS_4$ black holes

With a view towards understanding flat space holography, we study the flat space limit of asymptotically $AdS_4$ black holes in gauged $\mathcal{N}=2$ supergravity. Using holographic renormalisation, we carefully analyse the flat space limit of the on-shell action of various black holes in this setting and make explicit the states that contribute to the partition function in this limit. We find several features: large black holes lead to a divergence in the partition function which can be understood as a thermodynamic volume divergence. Meanwhile, semiclassically, small black holes reproduce the partition function of their flat space counterparts, and we clarify the mapping between BPS, (near-)extremal or non-extremal black holes on both sides for charged and rotating solutions. Finally, by studying a suitable flat limit of the BPS Kerr-Newman-$AdS_4$ black hole in M-theory, we obtain a charged BPS solution in flat space. On the dual side, we determine a Carrollian-like limit on the known ABJM partition function on $S^1\times S^2$ at large $N$, and show that it reproduces the correct entropy of the BPS charged black hole in asymptotically flat space.

hep-th↗

arXiv2026

Deep neural networks as lattice gauge theories

We modify the NN/QFT duality [1] to incorporate the layerwise permutation symmetry of the network, resulting in a $(0\!+\!1)$-dimensional lattice gauge theory, in which each layer of $N$ neurons acts as an $N$-component lattice site, and the weight matrices play the role of gauge fields living on the links. In this framework, we compute the tree-level neuron-neuron propagator which describes the evolution of layer variance in the network, and develop the Feynman diagram machinery to compute interactions in the perturbative expansion in $1/N$. In particular, we obtain a recursive expression for all corrections to the exact propagator at $O(1)$, representing statistical fluctuations in the ensemble of networks, including infinitely-many loop diagrams mediating the interactions from previous layers. We also present a preliminary analysis of neuron scattering amplitudes that contribute order-by-order in $1/N$, which provides a field-theoretic framework for studying higher-point correlations, and by extension information propagation, in deep networks. We remark on some interesting directions for future work at the intersection of neural networks and quantum field theory.

hep-th↗