Publications

Order by: publication date number of citations

• Phonon thermal Hall as a lattice Aharonov-Bohm effect

  Kamran Behnia

  SciPost Phys. Core 8, 061 (2025) · published 29 September 2025 | Toggle abstract · pdf

  In a growing list of insulators, experiments find that magnetic field induces a misalignment between the heat flux and the thermal gradient vectors. This phenomenon, known as the phonon thermal Hall effect, implies energy flow without entropy production along the orientation perpendicular to the temperature gradient. The experimentally-measured thermal Hall angle in various insulators does not exceed a bound and becomes maximal at the temperature of peak longitudinal thermal conductivity. The present paper aims to propose a scenario providing and explanation for these two experimental facts. It begins by noticing that at this temperature, $T_{max}$, Normal phonon-phonon collisions become most frequent in comparison with Umklapp and boundary scattering events. Furthermore, the Born-Oppenheimer approximated molecular wave functions are known to acquire a phase in the presence of a magnetic field. In an anharmonic crystal, in which tensile and compressive strain do not cancel out, this field-induced atomic phase gives rise to a phonon Berry phase and generates phonon-phonon interference. The rough amplitude of the thermal Hall angle expected in this picture is set by the phonon wavelength, $\lambda_{ph}$, and the crest atomic displacement, $\delta u_m$ at $T_{max}$. The derived expression is surprisingly close to what has been experimentally found in black phosphorus, germanium and silicon.

• Rapid measurements and phase transition detections made simple by AC-GANs

  Jiewei Ding, Ho-Kin Tang, Wing Chi Yu

  SciPost Phys. Core 7, 035 (2024) · published 11 June 2024 | Toggle abstract · pdf

  In recent years, significant attention has been paid to using end-to-end neural networks for analyzing Monte Carlo data. However, the exploration of non-end-to-end generative adversarial neural networks remains limited. Here, we investigate classical many-body systems using generative adversarial neural networks. We employ the conditional generative adversarial network with an auxiliary classifier (AC-GAN) and integrate self-attention layers into the generator. This modification enables the network learn the distribution of the two-dimensional (2D) XY model's spin configurations and the physical quantities of interest. Utilizing the symmetry of the systems, we discover that AC-GAN can be trained with a very small raw dataset. This approach allows us to obtain reliable measurements for models typically demanding large samples, such as the large-sized 2D XY and the 3D constrained Heisenberg models. Moreover, we demonstrate the capability of AC-GAN to identify the phase transition points by quantifying the distribution changes in the spin configurations of the systems.

• Quantum adiabaticity in many-body systems and almost-orthogonality in complementary subspace

  Jyong-Hao Chen, Vadim Cheianov

  SciPost Phys. Core 8, 084 (2025) · published 12 November 2025 | Toggle abstract · pdf

  We investigate why, in quantum many-body systems, the adiabatic fidelity and the overlap between the initial state and instantaneous ground states often yield nearly identical values. Our analysis suggests that this phenomenon results from an interplay between two intrinsic limits of many-body systems: the limit of small evolution parameters and the limit of large system sizes. In the former case, conventional perturbation theory provides a straightforward explanation. In the latter case, a key insight is that pairs of vectors in the Hilbert space orthogonal to the initial state tend to become nearly orthogonal as the system size increases. We illustrate these general findings with two representative models of driven many-body systems: the driven Rice-Mele model and the driven interacting Kitaev chain model.

• M5-brane prongs, string soliton bound states and wall-crossing

  Varun Gupta, K. Narayan

  SciPost Phys. Core 8, 004 (2025) · published 13 January 2025 | Toggle abstract · pdf

  We study Abelian M5-brane field configurations representing BPS bound states of self-dual string solitons whose locations correspond to the endlines of M2-branes ending on the M5-branes. The BPS equations are obtained from appropriate Bogomolny completion of the effective Abelian low energy functional with two transverse scalars, using two vectors representing the directions along which these endline strings extend. Then we impose boundary conditions on the scalars near the string soliton cores. This leads to a molecule-like equilibrium structure of two non-parallel string solitons at fixed transverse separations, with the M5-brane "prong" deformations comprising two "spikes", each shaped like a ridge. The resulting picture becomes increasingly accurate as one approaches the wall of marginal stability, on which these states decay. There are various parallels with wall-crossing phenomena for string web configurations obtained from D3-brane deformations.

• Accurate surrogate amplitudes with calibrated uncertainties

  Henning Bahl, Nina Elmer, Luigi Favaro, Manuel Haußmann, Tilman Plehn, Ramon Winterhalder

  SciPost Phys. Core 8, 073 (2025) · published 24 October 2025 | Toggle abstract · pdf

  Neural networks for LHC physics have to be accurate, reliable, and controlled. Using neural surrogates for the prediction of loop amplitudes as a use case, we first show how activation functions are systematically tested with Kolmogorov-Arnold Networks. Then, we train neural surrogates to simultaneously predict the target amplitude and an uncertainty for the prediction. We disentangle systematic uncertainties, learned by a well-defined likelihood loss, from statistical uncertainties, which require the introduction of Bayesian neural networks or repulsive ensembles. We test the coverage of the learned uncertainties using pull distributions to quantify the calibration of cutting-edge neural surrogates.

• Fractal decompositions and tensor network representations of Bethe wavefunctions

  Subhayan Sahu, Guifre Vidal

  SciPost Phys. Core 8, 067 (2025) · published 10 October 2025 | Toggle abstract · pdf

  We investigate the entanglement structure of a generic $M$-particle Bethe wavefunction (not necessarily an eigenstate of an integrable model) on a 1d lattice by dividing the lattice into $L$ parts and decomposing the wavefunction into a sum of products of $L$ local wavefunctions. Using the fact that a Bethe wavefunction accepts a fractal multipartite decomposition – it can always be written as a linear combination of $L^M$ products of $L$ local wavefunctions, where each local wavefunction is in turn also a Bethe wavefunction – we then build exact, analytical tensor network representations with finite bond dimension $\chi=2^M$, for a generic planar tree tensor network (TTN), which includes a matrix product states (MPS) and a regular binary TTN as prominent particular cases. For a regular binary tree, the network has depth $\log_{2}(N/M)$ and can be transformed into an adaptive quantum circuit of the same depth, composed of unitary gates acting on $2^M$-dimensional qudits and mid-circuit measurements, that deterministically prepares the Bethe wavefunction. Finally, we put forward a much larger class of generalized Bethe wavefunctions, for which the above decompositions, tensor network and quantum circuit representations are also possible.

• Bogoliubov phonons in a Bose-Einstein condensate from the one-loop perturbative renormalization group

  Niklas Rasch, Aleksandr N. Mikheev, Thomas Gasenzer

  SciPost Phys. Core 7, 066 (2024) · published 27 September 2024 | Toggle abstract · pdf

  Wilson's renormalization-group approach to the weakly-interacting single-component Bose gas is discussed within the symmetry-broken, condensate phase. Extending upon the work by Bijlsma and Stoof, wave-function renormalization of the temporal derivative contributions to the effective action is included in order to capture sound-like quasiparticle excitations with wave lengths larger than the healing-length scale. By means of a suitable rescaling scheme we achieve convergence of the coupling flows, which serve as a means to determine the condensate depletion in accordance with Bogoliubov theory, as well as the interaction-induced shift of the critical temperature.

• Inferring correlated distributions: Boosted top jets

  Ezequiel Alvarez, Manuel Szewc, Alejandro Szynkman, Santiago Tanco, Tatiana Tarutina

  SciPost Phys. Core 8, 087 (2025) · published 26 November 2025 | Toggle abstract · pdf

  Improving the understanding of signal and background distributions in signal-region is a valuable key to enhance any analysis in collider physics. This is usually a difficult task because –among others– signal and backgrounds are hard to discriminate in signal-region, simulations may reach a limit of reliability if they need to model non-perturbative QCD, and distributions are multi-dimensional and many times may be correlated within each class. Bayesian density estimation is a technique that leverages prior knowledge and data correlations to effectively extract information from data in signal-region. In this work we extend previous works on data-driven mixture models for meaningful unsupervised signal extraction in collider physics to incorporate correlations between features. Using a standard dataset of top and QCD jets, we show how simulators, despite having an expected bias, can be used to inject sufficient inductive nuance into an inference model in terms of priors to then be corrected by data and estimate the true correlated distributions between features within each class. We compare the model with and without correlations to show how the signal extraction is sensitive to their inclusion and we quantify the improvement due to the inclusion of correlations using both supervised and unsupervised metrics.

• Characterizing far from equilibrium states of the one-dimensional nonlinear Schrödinger equation

  Abhik Kumar Saha, Romain Dubessy

  SciPost Phys. Core 8, 028 (2025) · published 6 March 2025 | Toggle abstract · pdf

  We use the mathematical toolbox of the inverse scattering transform to study quantita- tively the number of solitons in far from equilibrium one-dimensional systems described by the defocusing nonlinear Schrödinger equation. We present a simple method to iden- tify the discrete eigenvalues in the Lax spectrum and provide a extensive benchmark of its efficiency. Our method can be applied in principle to all physical systems described by the defocusing nonlinear Schrödinger equation and allows to identify the solitons velocity distribution in numerical simulations and possibly experiments.

• Topological-insulator nanocylinders

  Michele Governale, Fabio Taddei

  SciPost Phys. Core 6, 032 (2023) · published 21 April 2023 | Toggle abstract · pdf

  Nanostructures, such a quantum dots or nanoparticles, made of three-dimensional topological insulators (3DTIs) have been recently attracting increasing interest, especially for their optical properties. In this paper we calculate the energy spectrum, the surface states and the dipole matrix elements for optical transitions with in-plane polarization of 3DTI nanocylinders of finite height $L$ and radius $R$. We first derive an effective 2D Hamiltonian by exploiting the cylindrical symmetry of the problem. We develop two approaches: the first one is an exact numerical tight-binding model obtained by discretising the Hamiltonian. The second one, which allows us to obtain analytical results, is an approximated model based on a large-$R$ expansion and on an effective boundary condition to account for the finite height of the nanocylinder. We find that the agreement between the two models, as far as eigenenergies and eigenfunctions are concerned, is excellent for the lowest absolute value of the longitudinal component of the angular momentum. Finally, we derive analytical expressions for the dipole matrix elements by first considering the lateral surface alone and the bases alone, and then for the whole nanocylinder. In particular, we focus on the two limiting cases of tall and squat nanocylinders. The latter case is compared with the numerical results finding a good agreement.