Publications

Order by: publication date number of citations

• Codebase release 1.0 for TorchGPE

  Lorenzo Fioroni, Luca Gravina, Justyna Stefaniak, Alexander Baumgärtner, Fabian Finger, Davide Dreon, Tobias Donner

  SciPost Phys. Codebases 38-r1.0 (2024) · published 6 November 2024 | Toggle abstract · src

  TorchGPE is a general-purpose Python package developed for solving the Gross-Pitaevskii equation (GPE). This solver is designed to integrate wave functions across a spectrum of linear and non-linear potentials. A distinctive aspect of TorchGPE is its modular approach, which allows the incorporation of arbitrary self-consistent and time-dependent potentials, e.g., those relevant in many-body cavity QED models. The package employs a symmetric split-step Fourier propagation method, effective in both real and imaginary time. In our work, we demonstrate a significant improvement in computational efficiency by leveraging GPU computing capabilities. With the integration of the latter technology, TorchGPE achieves a substantial speed-up with respect to conventional CPU-based methods, greatly expanding the scope and potential of research in this field.

  1 citation

• A Python GPU-accelerated solver for the Gross-Pitaevskii equation and applications to many-body cavity QED

  Lorenzo Fioroni, Luca Gravina, Justyna Stefaniak, Alexander Baumgärtner, Fabian Finger, Davide Dreon, Tobias Donner

  SciPost Phys. Codebases 38 (2024) · published 6 November 2024 | Toggle abstract · pdf

  TorchGPE is a general-purpose Python package developed for solving the Gross-Pitaevskii equation (GPE). This solver is designed to integrate wave functions across a spectrum of linear and non-linear potentials. A distinctive aspect of TorchGPE is its modular approach, which allows the incorporation of arbitrary self-consistent and time-dependent potentials, e.g., those relevant in many-body cavity QED models. The package employs a symmetric split-step Fourier propagation method, effective in both real and imaginary time. In our work, we demonstrate a significant improvement in computational efficiency by leveraging GPU computing capabilities. With the integration of the latter technology, TorchGPE achieves a substantial speed-up with respect to conventional CPU-based methods, greatly expanding the scope and potential of research in this field.

  3 citations

• Data reduction for low energy nuclear physics experiments using data frames

  Caleb Marshall

  SciPost Phys. Codebases 37 (2024) · published 6 November 2024 | Toggle abstract · pdf

  Low energy nuclear physics experiments are transitioning towards fully digital data acquisition systems. Realizing the gains in flexibility afforded by these systems relies on equally flexible data reduction techniques. In this paper, methods utilizing data frames and in-memory techniques to work with data, including data from self-triggering, digital data acquisition systems, are discussed within the context of a Python package, sauce. It is shown that data frame operations can encompass common analysis needs and allow interactive data analysis. Two event building techniques, dubbed referenced and referenceless event building, are shown to provide a means to transform raw list mode data into correlated multi-detector events. These techniques are demonstrated in the analysis of two example data sets.

  2 citations

• Codebase release 2.0 for sauce

  Caleb Marshall

  SciPost Phys. Codebases 37-r2.0 (2024) · published 6 November 2024 | Toggle abstract · src

  Low energy nuclear physics experiments are transitioning towards fully digital data acquisition systems. Realizing the gains in flexibility afforded by these systems relies on equally flexible data reduction techniques. In this paper, methods utilizing data frames and in-memory techniques to work with data, including data from self-triggering, digital data acquisition systems, are discussed within the context of a Python package, sauce. It is shown that data frame operations can encompass common analysis needs and allow interactive data analysis. Two event building techniques, dubbed referenced and referenceless event building, are shown to provide a means to transform raw list mode data into correlated multi-detector events. These techniques are demonstrated in the analysis of two example data sets.

  1 citation

• Les Houches guide to reusable ML models in LHC analyses

  Jack Y. Araz, Andy Buckley, Gregor Kasieczka, Jan Kieseler, Sabine Kraml, Anders Kvellestad, Andre Lessa, Tomasz Procter, Are Raklev, Humberto Reyes-Gonzalez, Krzysztof Rolbiecki, Sezen Sekmen, Gokhan Unel

  SciPost Phys. Comm. Rep. 3 (2024) · published 6 November 2024 | Toggle abstract · pdf

  With the increasing usage of machine-learning in high-energy physics analyses, the publication of the trained models in a reusable form has become a crucial question for analysis preservation and reuse. The complexity of these models creates practical issues for both reporting them accurately and for ensuring the stability of their behaviours in different environments and over extended timescales. In this note we discuss the current state of affairs, highlighting specific practical issues and focusing on the most promising technical and strategic approaches to ensure trustworthy analysis-preservation. This material originated from discussions in the LHC Reinterpretation Forum and the 2023 PhysTeV workshop at Les Houches.

  3 citations

• Non-Abelian symmetry-resolved entanglement entropy

  Eugenio Bianchi, Pietro Dona, Rishabh Kumar

  SciPost Phys. 17, 127 (2024) · published 5 November 2024 | Toggle abstract · pdf

  We introduce a mathematical framework for symmetry-resolved entanglement entropy with a non-Abelian symmetry group. To obtain a reduced density matrix that is block-diagonal in the non-Abelian charges, we define subsystems operationally in terms of subalgebras of invariant observables. We derive exact formulas for the average and the variance of the typical entanglement entropy for the ensemble of random pure states with fixed non-Abelian charges. We focus on compact, semisimple Lie groups. We show that, compared to the Abelian case, new phenomena arise from the interplay of locality and non-Abelian symmetry, such as the asymmetry of the entanglement entropy under subsystem exchange, which we show in detail by computing the Page curve of a many-body system with $SU(2)$ symmetry.

  6 citations

• Bethe vectors and recurrence relations for twisted Yangian based models

  Vidas Regelskis

  SciPost Phys. 17, 126 (2024) · published 5 November 2024 | Toggle abstract · pdf

  We study Olshanski twisted Yangian based models, known as one-dimensional "soliton non-preserving" open spin chains, by means of algebraic Bethe Ansatz. The even case, when the bulk symmetry is $\mathfrak{gl}_{2n}$ and the boundary symmetry is $\mathfrak{sp}_{2n}$ or $\mathfrak{so}_{2n}$, was studied in [Ann. Henri Poincaré 20, 339 (2018)]. In the present work, we focus on the odd case, when the bulk symmetry is $\mathfrak{gl}_{2n+1}$ and the boundary symmetry is $\mathfrak{so}_{2n+1}$. We explicitly construct Bethe vectors and present a more symmetric form of the trace formula. We use the composite model approach and $Y(\mathfrak{gl}_n)$-type recurrence relations to obtain recurrence relations for twisted Yangian based Bethe vectors, for both even and odd cases.

• Building 1D lattice models with $G$-graded fusion category

  Shang-Qiang Ning, Bin-Bin Mao, Chenjie Wang

  SciPost Phys. 17, 125 (2024) · published 5 November 2024 | Toggle abstract · pdf

  We construct a family of one-dimensional (1D) quantum lattice models based on $G$-graded unitary fusion category $\mathcal{C}_G$. This family realize an interpolation between the anyon-chain models and edge models of 2D symmetry-protected topological states, and can be thought of as edge models of 2D symmetry-enriched topological states. The models display a set of unconventional global symmetries that are characterized by the input category $\mathcal{C}_G$. While spontaneous symmetry breaking is also possible, our numerical evidence shows that the category symmetry constrains the models to the extent that the low-energy physics has a large likelihood to be gapless.

  2 citations

• Topological linear response of hyperbolic Chern insulators

  Canon Sun, Anffany Chen, Tomáš Bzdušek, Joseph Maciejko

  SciPost Phys. 17, 124 (2024) · published 4 November 2024 | Toggle abstract · pdf

  We establish a connection between the electromagnetic Hall response and band topological invariants in hyperbolic Chern insulators by deriving a hyperbolic analog of the Thouless-Kohmoto-Nightingale-den Nijs (TKNN) formula. By generalizing the Kubo formula to hyperbolic lattices, we show that the Hall conductivity is quantized to $-e^2C_{ij}/h$, where $C_{ij}$ is the first Chern number. Through a flux-threading argument, we provide an interpretation of the Chern number as a topological invariant in hyperbolic band theory. We demonstrate that, although it receives contributions from both Abelian and non-Abelian Bloch states, the Chern number can be calculated solely from Abelian states, resulting in a tremendous simplification of the topological band theory. Finally, we verify our results numerically by computing various Chern numbers in the hyperbolic Haldane model.

  10 citations

• Robust independent validation of experiment and theory: Rivet version 4 release note

  Christian Bierlich, Andy Buckley, Jonathan Butterworth, Christian Gutschow, Leif Lonnblad, Tomasz Procter, Peter Richardson, Yoran Yeh

  SciPost Phys. Codebases 36 (2024) · published 4 November 2024 | Toggle abstract · pdf

  The Rivet toolkit is the primary mechanism for phenomenological preservation of collider-physics measurements, containing both a computational core and API for analysis implementation, and a large collection of more than a thousand preserved analyses. In this note we summarise the main changes in the new Rivet 4 major release series. These include a major generalisation and more semantically coherent model for histograms and related data objects, a thorough clean-up of inelegant and legacy observable-computation tools, and new systems for extended analysis-data, incorporation of preserved machine-learning models, and serialization for high-performance computing applications. Where these changes introduce backward-incompatible interface changes, existing analyses have been updated and indications are given on how to update new analysis routines and workflows.

  6 citations