arXiv subjects

Jacob Cigliano

Publications and source records attributed to Jacob Cigliano.

3 recordsLinked to original sources

arXiv2025

10D Supergravity Numerical Data Sets for L & R Matrices

After reviewing the development of 10D, superspace theories, and their relations to superstring and heterotic string theories, explicit calculations are undertaken in the on-shell $\cal N$ = 1 linearized supergravity, the associated super-current is derived, and non-closure terms are explicitly given. The $L_{\rm I}$ and $R_{\rm I}$ adjacency matrices are then computed in complete numerical form as data sets. This is the preliminary step required to perform a scan to embed the on-shell matrices into off-shell ones.

hep-th↗

arXiv2025

Trapped Fermions Through Kolmogorov-Arnold Wavefunctions

We investigate a variational Monte Carlo framework for trapped one-dimensional mixture of spin-$\frac{1}{2}$ fermions using Kolmogorov-Arnold networks (KANs) to construct universal neural-network wavefunction ansätze. The method can, in principle, achieve arbitrary accuracy, limited only by the Monte Carlo sampling and was checked against exact results at sub-percent precision. For attractive interactions, it captures pairing effects, and in the impurity case it agrees with known results. We present a method of systematic transfer learning in the number of network parameters, allowing for efficient training for a target precision. We vastly increase the efficiency of the method by incorporating the short-distance behavior of the wavefunction into the ansätz without biasing the method.

nucl-th↗

arXiv2025

Kolmogorov-Arnold Wavefunctions

This work investigates Kolmogorov-Arnold network-based wavefunction ansatz as viable representations for quantum Monte Carlo simulations. Through systematic analysis of one-dimensional model systems, we evaluate their computational efficiency and representational power against established methods. Our numerical experiments suggest some efficient training methods and we explore how the computational cost scales with desired precision, particle number, and system parameters. Roughly speaking, KANs seem to be 10 times cheaper computationally than other neural network based ansatz. We also introduce a novel approach for handling strong short-range potentials-a persistent challenge for many numerical techniques-which generalizes efficiently to higher-dimensional, physically relevant systems with short-ranged strong potentials common in atomic and nuclear physics.

nucl-th↗