Publications

Order by: publication date number of citations

• Scaling laws of shrinkage induced fragmentation phenomena

  Roland Szatmári, Akio Nakahara, So Kitsunezaki, Ferenc Kun

  SciPost Phys. 19, 142 (2025) · published 1 December 2025 | Toggle abstract · pdf

  We investigate the shrinkage induced breakup of thin layers of heterogeneous materials attached to a substrate, a ubiquitous natural phenomenon with a wide range of potential applications. Focusing on the evolution of the fragment ensemble, we demonstrate that the system has two distinct phases: damage phase, where the layer is cracked, however, a dominant piece persists retaining the structural integrity of the layer, and a fragmentation phase, where the layer disintegrates into numerous small pieces. Based on finite size scaling we show that the transition between the two phases occurs at a critical damage analogous to continuous phase transitions. At the critical point a fully connected crack network emerges whose structure is controlled by the strength of adhesion to the substrate. In the strong adhesion limit, damage arises from random microcrack nucleation, resembling bond percolation, while weak adhesion facilitates stress concentration and the growth of cracks to large extensions. The critical exponents of the damage to fragmentation transition agree to a reasonable accuracy with those of two-dimensional bond percolation. Our findings provide a novel insights into the mechanism of shrinkage-induced cracking revealing generic scaling laws of the phenomenon.

• On the role of non-linear latent features in bipartite generative neural networks

  Tony Bonnaire, Giovanni Catania, Aurélien Decelle, Beatriz Seoane

  SciPost Phys. 19, 141 (2025) · published 1 December 2025 | Toggle abstract · pdf

  We investigate the phase diagram and memory retrieval capabilities of Restricted Boltzmann Machines (RBMs), an archetypal model of bipartite energy-based neural networks, as a function of the prior distribution imposed on their hidden units—including binary, multi-state, and ReLU-like activations. Drawing connections to the Hopfield model and employing analytical tools from statistical physics of disordered systems, we explore how the architectural choices and activation functions shape the thermodynamic properties of these models. Our analysis reveals that standard RBMs with binary hidden nodes and extensive connectivity suffer from reduced critical capacity, limiting their effectiveness as associative memories. To address this, we examine several modifications, such as introducing local biases and adopting richer hidden unit priors. These adjustments restore ordered retrieval phases and markedly improve recall performance, even at finite temperatures. Our theoretical findings, supported by finite-size Monte Carlo simulations, highlight the importance of hidden unit design in enhancing the expressive power of RBMs.

• Threshold improved $Z H$ production at the LHC

  Goutam Das, Chinmoy Dey, Meduri C. Kumar, Kajal Samanta

  SciPost Phys. Comm. Rep. 14 (2025) · published 1 December 2025 | Toggle abstract · pdf

  We present precise theoretical results for the $ZH$ production cross section and invariant mass distribution at the Large Hadron Collider (LHC) taking into account the effects of soft gluons. We improve both quark-initiated and gluon-initiated subprocesses through threshold resummation within the QCD framework and present combined results relevant for $13.6$ TeV LHC.

• Impurity effect and vortex cluster phase in mesoscopic type-1.5 superconductors

  Tian-Yi Han, Guo Wang, Jie Li, Hai Huang

  SciPost Phys. 19, 140 (2025) · published 28 November 2025 | Toggle abstract · pdf

  Based on two-band time-dependent Ginzburg-Landau theory, we study the electromagnetic properties of two-band mesoscopic superconductors. We perform the numerical simulations with the finite element method, and determine the minimum sample size $L_c$ for the existence of the type-1.5 superconductivity from the obtained phase diagram in the absence of impurity. Meanwhile in the presence of an isotropic impurity, our numerical results reveal that the vortex cluster state induced by the attractive defect potential will gradually appear in the mesoscopic system with the sample size $L < L_c$, and the critical defect strength is about 0.2 in the $T_c$ disorder model. In addition, we also investigate the effect of anisotropic defect structures and multiple correlated disorders on the patterns of magnetic vortex distributions. Our theoretical study thus indicates that the diversity of impurity depositions has a significant influence on the semi-Meissner state in mesoscopic type-1.5 superconductors.

• Classification of topological insulators and superconductors with multiple order-two point group symmetries

  Ken Shiozaki

  SciPost Phys. Core 8, 088 (2025) · published 28 November 2025 | Toggle abstract · pdf

  We present a method for computing the classification groups of topological insulators and superconductors in the presence of $\mathbb{Z}_2^{× n}$ point group symmetries, for arbitrary natural numbers $n$. Each symmetry class is characterized by four possible additional symmetry types for each generator of $\mathbb{Z}_2^{× n}$, together with bit values encoding whether pairs of generators commute or anticommute. We show that the classification is fully determined by the number of momentum- and real-space variables flipped by each generator, as well as the number of variables simultaneously flipped by any pair of generators. As a concrete illustration, we provide the complete classification table for the case of $\mathbb{Z}_2^{× 2}$ point group symmetry.

• Beyond Cooperation: The Role of Origin Countries in Deportation Efforts, Evidence from Mexico (1942 to 1964)

  Guadalupe Chavez

  Mig. Pol. 4, 005 (2025) · published 28 November 2025 | Toggle abstract · pdf

  For deportations to be carried out, host countries must secure inter-state cooperation with origin countries where they seek to deport noncitizens. However, origin countries may decide to cooperate or resist cooperation. This paper pushes the scholarship by exploring why some origin countries not only decide to cooperate but also become proactive actors in deportation efforts by encouraging and promoting the deportation of their citizens despite the economic and political costs. This paper unpacks this puzzle by analyzing why the Mexican government became a proactive actor in facilitating the deportation of its citizens from the 1940s to the 1960s. Drawing on state archival research in the U.S. and Mexico, I argue that the Mexican government became a proactive actor as a strategy to address its domestic challenges and to cultivate its diplomatic relations with the U.S. government and the benefits that came with such relation including the continuation of the Bracero Program, the largest guest worker program. This study makes two contributions. Empirically, this article provides an in-depth analysis on how Mexico, the country with the highest deportation flows in the Western Hemisphere, managed deportations during one of the largest U.S. removal operations. Second, by introducing the concept of proactiveness, it expands the field of migration diplomacy by unpacking the conditions that motivate origin countries to become proactive actors in deportation proceedings.

• Searching for strongly coupled AdS matter with multi-trace deformations

  Luis Apolo, Alexandre Belin, Suzanne Bintanja

  SciPost Phys. 19, 139 (2025) · published 27 November 2025 | Toggle abstract · pdf

  Holographic CFTs admit a dual emergent description in terms of semiclassical general relativity minimally coupled to matter fields. While the gravitational interactions are required to be suppressed by the Planck scale, the matter sector is allowed to interact strongly at the AdS scale. From the perspective of the dual CFT, this requires breaking large-$N$ factorization in certain sectors of the theory. Exactly marginal multi-trace deformations are capable of achieving this while still preserving a consistent large-$N$ limit. We probe the effect of these deformations on the bulk theory by computing the relevant four-point functions in conformal perturbation theory. We find a simple answer in terms of a finite sum of conformal blocks, indicating that the correlators display no bulk-point singularities. This implies that the matter of the bulk theory is made strongly coupled by boundary terms rather than local bulk interactions. Our results suggest that holographic CFTs that describe strongly coupled AdS matter must be isolated points on the CFT landscape or sit infinitely far away on the conformal manifold from conventional holographic CFTs.

• Codebase release 2.4 for ALF (Algorithms for Lattice Fermions)

  F. F. Assaad, M. Bercx, F. Goth, A. Götz, J. S. Hofmann, E. Huffman, Z. Liu, F. Parisen Toldin, J. S. E. Portela, J. Schwab

  SciPost Phys. Codebases 1-r2.4 (2025) · published 27 November 2025 | Toggle abstract · src

  The Algorithms for Lattice Fermions package provides a general code for the finite-temperature and projective auxiliary-field quantum Monte Carlo algorithm. The code is engineered to be able to simulate any model that can be written in terms of sums of single-body operators, of squares of single-body operators and single-body operators coupled to a bosonic field with given dynamics. The package includes five pre-defined model classes: SU(N) Kondo, SU(N) Hubbard, SU(N) t-V and SU(N) models with long range Coulomb repulsion on honeycomb, square and N-leg lattices, as well as $Z_2$ unconstrained lattice gauge theories coupled to fermionic and $Z_2$ matter. An implementation of the stochastic Maximum Entropy method is also provided. One can download the code from our Git instance at https://git.physik.uni-wuerzburg.de/ALF/ALF/-/tree/ALF-2.4 and sign in to file issues.

  1 citation

• The ALF (Algorithms for Lattice Fermions) project release 2.4. Documentation for the auxiliary-field quantum Monte Carlo code

  F. F. Assaad, M. Bercx, F. Goth, A. Götz, J. S. Hofmann, E. Huffman, Z. Liu, F. Parisen Toldin, J. S. E. Portela, J. Schwab

  SciPost Phys. Codebases 1-v2.4 (2025) · published 27 November 2025 · licensed under CC BY-SA (4.0) | Toggle abstract · pdf

  The Algorithms for Lattice Fermions package provides a general code for the finite-temperature and projective auxiliary-field quantum Monte Carlo algorithm. The code is engineered to be able to simulate any model that can be written in terms of sums of single-body operators, of squares of single-body operators and single-body operators coupled to a bosonic field with given dynamics. The package includes five pre-defined model classes: SU(N) Kondo, SU(N) Hubbard, SU(N) t-V and SU(N) models with long range Coulomb repulsion on honeycomb, square and N-leg lattices, as well as $Z_2$ unconstrained lattice gauge theories coupled to fermionic and $Z_2$ matter. An implementation of the stochastic Maximum Entropy method is also provided. One can download the code from our Git instance at https://git.physik.uni-wuerzburg.de/ALF/ALF/-/tree/ALF-2.4 and sign in to file issues.

  1 citation

• 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.