Publications

Order by: publication date number of citations

• Nonlocal order parameter of pair superfluids

  Nitya Cuzzuol, Luca Barbiero, Arianna Montorsi

  SciPost Phys. Core 8, 020 (2025) · published 11 February 2025 | Toggle abstract · pdf

  Order parameters represent a fundamental resource to characterize quantum matter. We show that pair superfluids can be rigorously defined in terms of a nonlocal order parameter, named odd parity, which derivation is experimentally accessible by local density measurements. As a case of study, we first investigate a constrained Bose-Hubbard model at different densities, both in one and two spatial dimensions. Here, our analysis finds pair superfluidity for relatively strong attractive interactions. The odd parity operator acts as the unique order parameter for such phase irrespectively to the density of the system and its dimensionality in regimes of total particle number conservation. In order to enforce our finding, we confirm the generality of our approach also on a two-component Bose-Hubbard Hamiltonian, which experimental realization represents a timely topic in ultracold atomic systems. Our results shed new light on the role of correlated density fluctuations in pair superfluids. In addition, they provide a powerful tool for the experimental detection of such exotic phases and the characterization of their transition to the atomic superfluid phase.

  1 citation

• Inferring flavor mixtures in multijet events

  Ezequiel Alvarez, Yuling Yao

  SciPost Phys. Core 7, 076 (2024) · published 28 November 2024 | Toggle abstract · pdf

  Multijet events with heavy-flavors are of central importance at the LHC since many relevant processes–such as $t\bar t$, $hh$, $t\bar t h$ and others–have a preferred branching ratio for this final state. Current techniques for tackling these processes use hard-assignment selections through $b$-tagging working points, and suffer from systematic uncertainties because of the difficulties in Monte Carlo simulations. We develop a flexible Bayesian mixture model approach to simultaneously infer $b$-tagging score distributions and the flavor mixture composition in the dataset. We model multidimensional jet events, and to enhance estimation efficiency, we design structured priors that leverages the continuity and unimodality of the $b$-tagging score distributions. Remarkably, our method eliminates the need for a parametric assumption and is robust against model misspecification–It works for arbitrarily flexible continuous curves and is better if they are unimodal. We have run a toy inferential process with signal $bbbb$ and backgrounds $ccbb$ and $cccc$, and we find that with a few hundred events we can recover the true mixture fractions of the signal and backgrounds, as well as the true $b$-tagging score distribution curves, despite their arbitrariness and nonparametric shapes. We discuss prospects for taking these findings into a realistic scenario in a physics analysis. The presented results could be a starting point for a different and novel kind of analysis in multijet events, with a scope competitive with current state-of-the-art analyses. We also discuss the possibility of using these results in general cases of signals and backgrounds with approximately known continuous distributions and/or expected unimodality.

  1 citation

• Identifying Chern numbers of superconductors from local measurements

  Paul Baireuther, Marcin Płodzień, Teemu Ojanen, Jakub Tworzydło, Timo Hyart

  SciPost Phys. Core 6, 087 (2023) · published 15 December 2023 | Toggle abstract · pdf

  Fascination in topological materials originates from their remarkable response properties and exotic quasiparticles which can be utilized in quantum technologies. In particular, large-scale efforts are currently focused on realizing topological superconductors and their Majorana excitations. However, determining the topological nature of superconductors with current experimental probes is an outstanding challenge. This shortcoming has become increasingly pressing due to rapidly developing designer platforms which are theorized to display very rich topology and are better accessed by local probes rather than transport experiments. We introduce a robust machine learning protocol for classifying the topological states of two-dimensional (2D) chiral superconductors and insulators from local density of states (LDOS) data. Since the LDOS can be measured with standard experimental techniques, our protocol contributes to overcoming the almost three decades standing problem of identifying the topological phase of 2D superconductors with broken time-reversal symmetry.

  1 citation

• Unifying description of the damping regimes of a stochastic particle in a periodic potential

  Antonio Piscitelli, Massimo Pica Ciamarra

  SciPost Phys. 3, 018 (2017) · published 4 September 2017 | Toggle abstract · pdf

  We analyze the classical problem of the stochastic dynamics of a particle confined in a periodic potential, through the so called Il'in and Khasminskii model, with a novel semi-analytical approach. Our approach gives access to the transient and the asymptotic dynamics in all damping regimes, which are difficult to investigate in the usual Brownian model. We show that the crossover from the overdamped to the underdamped regime is associated with the loss of a typical time scale and of a typical length scale, as signaled by the divergence of the probability distribution of a certain dynamical event. In the underdamped regime, normal diffusion coexists with a non Gaussian displacement probability distribution for a long transient, as recently observed in a variety of different systems. We rationalize the microscopic physical processes leading to the non-Gaussian behavior, as well as the timescale to recover the Gaussian statistics. The theoretical results are supported by numerical calculations and are compared to those obtained for the Brownian model.

  1 citation

• Fast Perfekt: Regression-based refinement of fast simulation

  Moritz Wolf, Lars O. Stietz, Patrick L. S. Connor, Peter Schleper, Samuel Bein

  SciPost Phys. Core 8, 021 (2025) · published 14 February 2025 | Toggle abstract · pdf

  The availability of precise and accurate simulation is a limiting factor for interpreting and forecasting data in many fields of science and engineering. Often, one or more distinct simulation software applications are developed, each with a relative advantage in accuracy or speed. The quality of insights extracted from the data stands to increase if the accuracy of faster, more economical simulation could be improved to parity or near parity with more resource-intensive but accurate simulation. We present Fast Perfekt, a machine-learned regression to refine the output of fast simulation that employs residual neural networks. A deterministic morphing model is trained using a unique schedule that makes use of the ensemble loss function MMD, with the option of an additional pair-based loss function such as the MSE. We explore this methodology in the context of an abstract analytical model and in terms of a realistic particle physics application featuring jet properties in hadron collisions at the CERN Large Hadron Collider. The refinement makes maximum use of existing domain knowledge, and introduces minimal computational overhead to production.

  1 citation

• Cracking hadron and nuclear collisions open with ropes and string shoving in PYTHIA8

  Smita Chakraborty

  SciPost Phys. Proc. 10, 017 (2022) · published 10 August 2022 | Toggle abstract · pdf

  Enhancement in strangeness yields in heavy-ion collisions is regarded as one of the attributes of the Quark-Gluon Plasma (QGP) along with jet quenching and final-state collectivity. To reproduce these effects in the MC implementation of the Lund string model in \pythia, the inclusion of non-perturbative mechanisms like string shoving and rope hadronization is crucial. In this proceeding, we discuss our latest implementation of these mechanisms and the preliminary results for the strangeness enhancement in high-multiplicity p-p, p-Pb and Xe-Xe. We conclude with remarks on the ongoing development of the model.

  1 citation

• A benchmark study of dioxygen complexes based on coupled cluster and density functional theory

  Marcel Swart, Marc Reimann

  SciPost Chem. 3, 001 (2024) · published 13 August 2024 | Toggle abstract · pdf

  A set of five compounds containing peroxo, superoxo or bis-μ-oxo moieties has been studied in the gas phase using CCSD(T)/aug-cc-pVQZ, also in combination with Goodson’s continued fraction approach. The corresponding analytical frequencies corroborate assignments of bands from experiments, and thus provide a consistent set of reference data that can be used for benchmarking a range of density functional approximations. A total of 100 density functionals have been checked for the general bond lengths, the specific peroxo/superoxo bond lengths, angles, and vibrational frequencies. There is not one density functional that performs equally well for all of these properties, not even within one class of density functionals.

  1 citation

• An exact local mapping from clock-spins to fermions

  Simone Traverso, Christoph Fleckenstein, Maura Sassetti, Niccolò Traverso Ziani

  SciPost Phys. Core 6, 055 (2023) · published 15 August 2023 | Toggle abstract · pdf

  Clock-spin models are attracting great interest, due to both their rich phase diagram and their connection to parafermions. In this context, we derive an exact local mapping from clock-spin to fermionic partition functions. Such mapping, akin to techniques introduced by Fedotov and Popov for spin $\frac{1}{2}$ chains, grants access to well established numerical tools for the perturbative treatment of fermionic systems in the clock-spin framework. Moreover, in one dimension, it allows to use bosonization to access the low energy properties of clock-spin models. Finally, aside from the direct application in clock-spin models, this new mapping enables the conception of interesting fermionic models, based on the clock-spin counterparts.

  1 citation

• Quantum-inspired tempering for ground state approximation using artificial neural networks

  Tameem Albash, Conor Smith, Quinn Campbell, Andrew D. Baczewski

  SciPost Phys. 14, 121 (2023) · published 19 May 2023 | Toggle abstract · pdf

  A large body of work has demonstrated that parameterized artificial neural networks (ANNs) can efficiently describe ground states of numerous interesting quantum many-body Hamiltonians. However, the standard variational algorithms used to update or train the ANN parameters can get trapped in local minima, especially for frustrated systems and even if the representation is sufficiently expressive. We propose a parallel tempering method that facilitates escape from such local minima. This methods involves training multiple ANNs independently, with each simulation governed by a Hamiltonian with a different "driver" strength, in analogy to quantum parallel tempering, and it incorporates an update step into the training that allows for the exchange of neighboring ANN configurations. We study instances from two classes of Hamiltonians to demonstrate the utility of our approach using Restricted Boltzmann Machines as our parameterized ANN. The first instance is based on a permutation-invariant Hamiltonian whose landscape stymies the standard training algorithm by drawing it increasingly to a false local minimum. The second instance is four hydrogen atoms arranged in a rectangle, which is an instance of the second quantized electronic structure Hamiltonian discretized using Gaussian basis functions. We study this problem in a minimal basis set, which exhibits false minima that can trap the standard variational algorithm despite the problem's small size. We show that augmenting the training with quantum parallel tempering becomes useful to finding good approximations to the ground states of these problem instances.

  1 citation

• Two-dimensional topological paramagnets protected by $\mathbb{Z}_3$ symmetry: Properties of the boundary Hamiltonian

  Hrant Topchyan, Vasilii Iugov, Mkhitar Mirumyan, Tigran Hakobyan, Tigran A. Sedrakyan, Ara G. Sedrakyan

  SciPost Phys. 18, 068 (2025) · published 25 February 2025 | Toggle abstract · pdf

  We systematically study gapless edge modes corresponding to $\mathbb{Z}_3$ symmetry-protected topological (SPT) phases of two-dimensional three-state Potts paramagnets on a triangular lattice. First, we derive microscopic lattice models for the gapless edge and, using the density-matrix renormalization group (DMRG) approach, investigate the finite-size scaling of the low-lying excitation spectrum and the entanglement entropy. Based on the obtained results, we identify the universality class of the critical edge, namely the corresponding conformal field theory and the central charge. Finally, we discuss the inherent symmetries of the edge models and the emergent winding number symmetry. As a result, one-dimensional chains with this symmetry form a model that supports gapless excitations due to its tricritical symmetry. Numerically, we show that low-energy states in the continuous limit of the edge model can be described by conformal field theory (CFT) with central charge $c=1$, given by the coset $SU_k(3)/SU_k(2)$ CFT at level k=1.

  1 citation