Publications

Order by: publication date number of citations

• Codebase release 0.1 for infstat

  Kyoungchul Kong, Konstantin T. Matchev, Stephen Mrenna, Prasanth Shyamsundar

  SciPost Phys. Codebases 14-r0.1 (2023) · published 7 July 2023 | Toggle abstract · src

  We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoStatic Networks (ISN) method, to model the score and likelihood ratio estimators in cases when the probability density can be sampled but not computed directly. The ISN uses a backend neural network that models a scalar function called the inferostatic potential $\varphi$. In addition, we introduce new strategies, respectively called Kernel Score Estimation (KSE) and Kernel Likelihood Ratio Estimation (KLRE), to learn the score and the likelihood ratio functions from simulated data. We illustrate the new techniques with some toy examples and compare to existing approaches in the literature. We mention en passant some new loss functions that optimally incorporate latent information from simulations into the training procedure.

  1 citation

• Codebase release 1.3 for SymTensor

  Yang Gao, Phillip Helms, Garnet Kin-Lic Chan, Edgar Solomonik

  SciPost Phys. Codebases 10-r1.3 (2023) · published 24 February 2023 | Toggle abstract · src

  Tensor contractions are ubiquitous in computational chemistry and physics, where tensors generally represent states or operators and contractions express the algebra of these quantities. In this context, the states and operators often preserve physical conservation laws, which are manifested as group symmetries in the tensors. These group symmetries imply that each tensor has block sparsity and can be stored in a reduced form. For nontrivial contractions, the memory footprint and cost are lowered, respectively, by a linear and a quadratic factor in the number of symmetry sectors. State-of-the-art tensor contraction software libraries exploit this opportunity by iterating over blocks or using general block-sparse tensor representations. Both approaches entail overhead in performance and code complexity. With intuition aided by tensor diagrams, we present a technique, irreducible representation alignment, which enables efficient handling of Abelian group symmetries via only dense tensors, by using contraction-specific reduced forms. This technique yields a general algorithm for arbitrary group symmetric contractions, which we implement in Python and apply to a variety of representative contractions from quantum chemistry and tensor network methods. As a consequence of relying on only dense tensor contractions, we can easily make use of efficient batched matrix multiplication via Intel's MKL and distributed tensor contraction via the Cyclops library, achieving good efficiency and parallel scalability on up to 4096 Knights Landing cores of a supercomputer.

  1 citation

• Central dileption production in proton-proton collisions with rapidity gap and with forward protons

  Antoni Szczurek, Barbara Linek, Marta Luszczak

  SciPost Phys. Proc. 8, 083 (2022) · published 12 July 2022 | Toggle abstract · pdf

  We discuss photon-photon fusion mechanisms of dilepton production in proton-proton collisions with rapidity gap in the main detector and one forward proton in the forward proton detectors. This is relevant for the LHC measurements by ATLAS+AFP and CMS+PPS. Transverse momenta of the intermediate photons are taken into account and photon fluxes are expressed in terms of proton electromagnetic form factors and structure functions. Both double-elastic and single-dissociative processes are included in the analysis. Different parametrizations of the structure functions are used. Some differential distributions are presented. Some differences with respect to the results without proton measurement are discussed.

  1 citation

• The all-particle energy spectrum of cosmic rays from 10 TeV to 1 PeV measured with HAWC

  Jorge A. Morales-Soto, Juan C. Arteaga-Velázquez on behalf of the HAWC Collaboration

  SciPost Phys. Proc. 13, 039 (2023) · published 29 September 2023 | Toggle abstract · pdf

  The HAWC observatory is an air-shower detector, which is designed to study both astrophysical gamma-rays in the TeV region and galactic cosmic rays in the energy interval from 1 TeV to 1 PeV. This energy regime is interesting for cosmic ray research, since indirect observations overlap with direct measurements, which offers the opportunity for cross calibration and studies of experimental systematic errors in both techniques. One quantity that could help for this purpose is the all-particle energy spectrum of cosmic rays. In this work, we present an update of HAWC measurements on the total cosmic-ray energy spectrum between 10 TeV and 1 PeV. The spectrum was obtained from an unfolding analysis of almost two years of HAWC's data, which was collected from January, 2018 to December, 2019. For the energy estimation, we employed the high-energy hadronic interaction model QGSJET-II-04. As in a previous work of HAWC, published in 2017, we observed the presence of a knee-like feature in the region of tens of TeV.

  1 citation

• Asymmetric power dissipation in electronic transport through a quantum point contact

  Carmen Blaas-Anselmi, Félix Helluin, Rodolfo A. Jalabert, Guillaume Weick, Dietmar Weinmann

  SciPost Phys. 12, 105 (2022) · published 25 March 2022 | Toggle abstract · pdf

  We investigate the power dissipated by an electronic current flowing through a quantum point contact in a two-dimensional electron gas. Based on the Landauer-B\"uttiker approach to quantum transport, we evaluate the power that is dissipated on the two sides of the constriction as a function of the Fermi energy, temperature, and applied voltage. We demonstrate that an asymmetry appears in the dissipation, which is most pronounced when the quantum point contact is tuned to a conductance step where the transmission strongly depends on energy. At low temperatures, the asymmetry is enhanced when the temperature increases. An estimation for the position of the maximum dissipation is provided.

  1 citation

• Probing chaos in the spherical p-spin glass model

  Lorenzo Correale, Anatoli Polkovnikov, Marco Schirò, Alessandro Silva

  SciPost Phys. 15, 190 (2023) · published 13 November 2023 | Toggle abstract · pdf

  We study the dynamics of a quantum $p$-spin glass model starting from initial states defined in microcanonical shells, in a classical regime. We compute different chaos estimators, such as the Lyapunov exponent and the Kolmogorov-Sinai entropy, and find a marked maximum as a function of the energy of the initial state. By studying the relaxation dynamics and the properties of the energy landscape we show that the maximal chaos emerges in correspondence with the fastest spin relaxation and the maximum complexity, thus suggesting a qualitative picture where chaos emerges as the trajectories are scattered over the exponentially many saddles of the underlying landscape. We also observe hints of ergodicity breaking at low energies, indicated by the correlation function and a maximum of the fidelity susceptibility.

  1 citation

• Codebase release 3.44 for xSPDE

  Simon Kiesewetter, Ria R. Joseph, Peter D. Drummond

  SciPost Phys. Codebases 17-r3.44 (2023) · published 2 October 2023 | Toggle abstract · src

  The xSPDE toolbox treats stochastic partial and ordinary differential equations, with applications in biology, chemistry, engineering, medicine, physics and quantum technologies. It computes statistical averages, including time-step and/or sampling error estimation. xSPDE can provide higher order convergence, Fourier spectra and probability densities. The toolbox has graphical output and $\chi^{2}$ statistics, as well as weighted, projected, or forward-backward equations. It can generate input-output quantum spectra. All equations may have independent periodic, Dirichlet, and Neumann or Robin boundary conditions in any dimension, for any vector field component, and at either end of any interval.

  1 citation

• Logarithmic correlation functions for critical dense polymers on the cylinder

  Alexi Morin-Duchesne, Jesper Lykke Jacobsen

  SciPost Phys. 7, 040 (2019) · published 30 September 2019 | Toggle abstract · pdf

  We compute lattice correlation functions for the model of critical dense polymers on a semi-infinite cylinder of perimeter $n$. In the lattice loop model, contractible loops have a vanishing fugacity whereas non-contractible loops have a fugacity $\alpha \in (0,\infty)$. These correlators are defined as ratios $Z(x)/Z_0$ of partition functions, where $Z_0$ is a reference partition function wherein only simple half-arcs are attached to the boundary of the cylinder. For $Z(x)$, the boundary of the cylinder is also decorated with simple half-arcs, but it also has two special positions $1$ and $x$ where the boundary condition is different. We investigate two such kinds of boundary conditions: (i) there is a single node at each of these points where a long arc is attached, and (ii) there are pairs of adjacent nodes at these points where two long arcs are attached. We find explicit expressions for these correlators for finite $n$ using the representation of the enlarged periodic Temperley-Lieb algebra in the XX spin chain. The resulting asymptotics as $n\to \infty$ are expressed as simple integrals that depend on the scaling parameter $\tau = \frac {x-1} n \in (0,1)$. For small $\tau$, the leading behaviours are proportional to $\tau^{1/4}$, $\tau^{1/4}\log \tau$, $\log \tau$ and $\log^2 \tau$. We interpret the lattice results in terms of ratios of conformal correlation functions. We assume that the corresponding boundary changing fields are highest weight states in irreducible, Kac or staggered Virasoro modules, with central charge $c=-2$ and conformal dimensions $\Delta = -\frac18$ or $\Delta = 0$. With these assumptions, we obtain differential equations of order two and three satisfied by the conformal correlation functions, solve these equations in terms of hypergeometric functions, and find a perfect agreement with the lattice results. We use the lattice results to compute structure constants and ratios thereof which appear in the operator product expansions of the boundary condition changing fields. The fusion of these fields is found to be non-abelian.

  1 citation

• An exact method for bosonizing the Fermi surface in arbitrary dimensions

  Takamori Park, Leon Balents

  SciPost Phys. 16, 069 (2024) · published 8 March 2024 | Toggle abstract · pdf

  Inspired by the recent work by Delacretaz et. al. [Phys. Rev. Res. 4, 033131 (2022)], we rigorously derive an exact and simple method to bosonize a non-interacting fermionic system with a Fermi surface starting from a microscopic Hamiltonian. In the long-wavelength limit, we show that the derived bosonized action is exactly equivalent to the action obtained by Delacretaz et. al. In addition, we propose diagrammatic rules to evaluate correlation functions using our bosonized theory and demonstrate these rules by calculating the three- and four-point density correlation functions. We also consider a general density-density interaction and show that the simplest approximation in our bosonic theory is identical to RPA results.

  1 citation

• Learning Summary Statistics for Bayesian Inference with Autoencoders

  Carlo Albert, Simone Ulzega, Firat Ozdemir, Fernando Perez-Cruz, Antonietta Mira

  SciPost Phys. Core 5, 043 (2022) · published 1 September 2022 | Toggle abstract · pdf

  For stochastic models with intractable likelihood functions, approximate Bayesian computation offers a way of approximating the true posterior through repeated comparisons of observations with simulated model outputs in terms of a small set of summary statistics. These statistics need to retain the information that is relevant for constraining the parameters but cancel out the noise. They can thus be seen as thermodynamic state variables, for general stochastic models. For many scientific applications, we need strictly more summary statistics than model parameters to reach a satisfactory approximation of the posterior. Therefore, we propose to use the inner dimension of deep neural network based Autoencoders as summary statistics. To create an incentive for the encoder to encode all the parameter-related information but not the noise, we give the decoder access to explicit or implicit information on the noise that has been used to generate the training data. We validate the approach empirically on two types of stochastic models.

  1 citation