Balázs Kacskovics\(^{1,2}\), Dániel Barta\(^{1}\) (2024.01.01 - 05.31)

\(^{1}\) HUN-REN Wigner Research Centre for Physics
\(^{2}\) Pécs University

Publication: Comparing eccentric waveform models based on post-Newtonian and effective-one-body approaches

Abstract: In the framework of this project, we compare two numerical models, namely CBWaves and SEOBNRE, which use the post-Newtonian and effective one-body approaches to model eccentric binaries. To explore the discrepancy between the two models, 260,000 simulations are performed on a grid stretched by the chosen parameter space - 20,000 for non-spinning configurations and 240,000 for spinning configurations. Each point on this grid, denoted by \(i\), is determined by the mass ratio \(\nu \equiv m_1/m_2\in [0.1,1]\), the gravitational mass \(m_i \in [10M_\odot, 100M_\odot]\), the corresponding spin magnitude \(S_i \in [0,0.6]\) and a constant initial orbital eccentricity \(e_{0}\). We will perform a comprehensive study to determine whether there is a discrepancy between the waveforms generated by the two codes.

Győző Kovács, Péter Kovács, György Wolf (2023.01.01 - 11.30)

Wigner Research Centre for Physics

Grant: NKFIH FK 131982

Publication: Phenomenology of isospin-symmetry breaking with vector mesons

Abstract: The extended linear sigma model (eLSM) is an advanced quarkmeson model that can be used to study meson phenomenology at zero temperature and the QCD phase diagram at finite temperature and/or baryon chemical potential. In order to make predictions, parameters of the model should be determined by fitting calculated physical quantities (like masses and decay widths) to their experimental values. The best fit and global minimum can be found by choosing some random starting points in the k-dimensional parameter space (in our case \(k > 14\)) and run a \(\chi^2\) multiparametric minimization. This procedure is numerically quite expensive and time-consuming. However, a large number of starting points (\(n ∼ 10^7\) ) is required to achieve a reasonable statistics and resolution in the parameter values. In order to gain a good understanding of the model, its parameter dependence, and parameterization, it is also necessary to perform the fitting also under variation of the physical quantities involved.

Győző Kovács [1], Péter Kovács [1], György Wolf [1], Pok Man Lo [2] (2023.01.01 - 11.30)

[1] Wigner Research Centre for Physics
[2] University of Wroclaw, Wroclaw

Grant: NKFIH FK 131982

Publication: Sensitivity of finite size effects to the boundary conditions and the vacuum term

Abstract: The effect of finite size/volume in effective field theory models is usually accounted for by some form of constrain in the momentum space, for example by low momentum cut-off or discretization. The latter results in a summation for momentum modes depending on the boundary condition used, rather than integrals. We study the size dependence of the baryon fluctuations in and around the critical endpoint in an improved quark-meson model, with particular attention to how they are affected by different modifications of the momentum space and the inclusion of the vacuum contribution. To determine the cumulant ratios describing the fluctuations, the higher order derivatives of the pressure are calculated. For this purpose, the finite difference method can be used, but this requires solving the field equations at several points for the derivative at only one point. This demands considerable computational power if the phase diagram and the size dependence are to be computed densely enough. On the other hand these calculations can be easily parallelised.

Emese Forgács-Dajka*, István Ballai** (2021.05.01-2021.12.31)

* Eötvös University, Dept. of Astronomy
** Solar Physics and Space Plasma Research Centre (SP2RC), Department of Applied Mathematics, The University of Sheffield

Publication: Parametric resonance of Alfvén waves driven by ionization-recombination waves in the weakly ionized solar atmosphere

Abstract: We investigate the nature and properties of shock waves propagating in an oblique direction to the ambient magnetic field in a partially ionised plasma modelling the plasma of solar prominences. In particular, we aim to analyse the observational signature of these shocks and investigate how our results can explain the recent observations of propagating bright blobs in solar prominences by Lin et al. (2012).

The equations of compressional single-fluid magnetohydrodynamic (MHD) equations are reduced with the help of a multiple scaling method to a well-known Burgers equation whose coefficients depend on the propagation angle of shock waves, plasma-β and the ionisation degree of the plasma. Our model is well-adapted for the separate discussion of shock waves arising from the nonlinear steepening of slow or fast magnetoacoustic waves. Using the standard jump conditions across the shock front (assuming a weak dissipation) we determine the jump in thermodynamic quantities that will be useful for comparison with observations.

Using the Cole-Hopf transform we solve the governing equation as an initial value problem of a diffusion-like equation and investigate the time necessary for a Gaussian initial wave profile to evolve into a shock, whose thickness is of the order of a few ion mean free path.

János Takátsy [1] Péter Kovács [1], György Wolf [1], Juergen Schaffner-Bielich [2] (2023.01.01 - 11.30)

[1] Wigner Research Centre for Physics
[2] University of Frankfurt

Grant: NKFIH FK 131982

Publication: What neutron stars tell about the hadron-quark phase transition: A Bayesian study

Abstract: The investigation of the phase diagram of Quantum Chromodynamics (QCD) at high densities is currently only possible through effective theories. Neutron stars are among the densest objects in our universe, possibly containing chirally symmetric matter as well. We calculate neutron star properties from our model with different parameterizations and confront the results with recent astrophysical observations. These astrophysical observations include mass measurements of neutron stars, NICER measurements and tidal deformability measurements of GW170817. We utilize a Bayesian framework to determine the most probable regions of the parameter space of our model, which requires high computing capacities in order to obtain the necessary statistics.

Neelkamal Mallick [1], Suraj Prasad [1], Aditya Nath Mishra [2,4], Raghunath Sahoo [1] and Gergely Gábor Barnaföldi [3] (2023.01.01 - 2023.03.31)

[1] Department of Physics, Indian Institute of Technology Indore
[2] Department of Physics, School of Applied Sciences, REVA University
[3] Wigner Research Center for Physics
[4] Department of Physics, University Centre For Research & Development (UCRD), Chandigarh University

Publication: Deep learning predicted elliptic flow of identified particles in heavy-ion collisions at the RHIC and LHC energies

Abstract Recent developments of a deep learning feed-forward network for estimating elliptic flow \((v_2)\) coefficients in heavy-ion collisions have shown the prediction power of this technique. The success of the model is mainly the estimation of \(v_2\) from final-state particle kinematic information and learning the centrality and transverse momentum \((p_T)\) dependence of \(v_2\). The deep learning model is trained with Pb-Pb collisions at \(\sqrt{s_{NN}} = 5.02 TeV\) minimum bias events simulated with a multiphase transport model. We extend this work to estimate \(v_2\) for light-flavor identified particles such as \(π^\pm\), \(K^\pm\), and \(p + \bar{p}\) in heavy-ion collisions at RHIC and LHC energies. The number-of-constituent-quark scaling is also shown. The evolution of the \(p_T\)-crossing point of \(v_2(p_T)\), depicting a change in baryon-meson elliptic flow at intermediate \(p_T\) , is studied for various collision systems and energies. The model is further evaluated by training it for different \(p_T\) regions. These results are compared with the available experimental data wherever possible.

Mira Gergácz, Ákos Keresztúri (2023.09.01-12.31.)

Publication:Surveying the ice condensation period at southern polar Mars using a CNN

Abstract: The aim of this study is to survey the surface ice condensation periods of Martian ice with automatized methods using a Convolutional Neural Network (CNN) applied to High Resolution Imaging Science Experiment (HiRISE) imagery from the Mars Reconnaissance Orbiter (MRO) mission. Using a CNN trained to distinguish small (diameter ranging from \(1.5-300\) meters) ice patches from other surface forms turned out to be a feasible automatic method for this purpose. The surveyed region is the latitude band between \(-40°\) and \(-60°\) during the solar longitude range of \(0-90°\), before the southern polar cap spreads in the given latitude band.

Antal Jakovác, Anna Horváth, Bence Dudás

Abstract: Infrasound sample analysis using artificial intelligence methods for applied research.

Anna Horváth [1,2], Gergely Gábor Barnaföldi [1], Emese Forgács-Dajka [2] (2023.09.01-2023.12.31 )

[1] Wigner Research Centre for Physics
[2] Eötvös Loránd University

Abstract: We are investigating compact stars within a static, spherically symmetric Kaluza-Klein-like theory that encompasses extra compactified spatial dimensions. We produced an equation of state that can be used to model neutron stars together with the Tolman-Oppenheimer-Volkoff equation. Simulating the structure of these objects (calculating their main observables, the mass and the radius) and carrying out a thorough analysis requires us to use computational-heavy programming. Stars with various boundary conditions (such as the central energy density) and theoretical parameters (like the size of the extra dimension) are considered. For this type of calculations it is essentially useful to utilise parallelism, which is best executed on multi-core processors. This project tests theories beyond the standard model of particle physics, with an emphasis on the possibility of giving constraints on the size of one extra compactified spatial dimension.

Mira Gergácz, Ákos Keresztúri (2023.06.01 - 08.31)

Publication: Analysing high resolution digital Mars images using machine learning

Abstract: The search for ephemeral liquid water on Mars is an ongoing activity. After the recession of the seasonal polar ice cap on Mars, small water ice patches may be left behind in shady places due to the low thermal conductivity of the Martian surface and atmosphere. During late spring and early summer, these patches may be exposed to direct sunlight and warm up rapidly enough for the liquid phase to emerge. Previously a manual image analysis was conducted on 110 images from the southern hemisphere, captured by the HiRISE camera onboard the Mars Reconnaissance Orbiter space mission. Out of these, 37 images were identified with smaller ice patches, which were distinguishable by their brightness, colour and strong connection to local topographic shading.

In this study, a convolutional neural network (CNN) is applied to find further images with potential water ice patches in the latitude band between -40° and -60°. Previously analysed HiRISE images are used to train the model, expanding the training dataset to 6240 images. A test run conducted on 38 new HiRISE images indicates that the program can generally recognise small bright patches, however further training might be needed for more precise predictions.

Using a CNN model may make it realistic to analyse all available surface images, aiding us in selecting areas for further investigation.

Mátyás Koniorcyzk [1], Péter Naszvadi [1], Milkós Pintér [2] (2023.04.18 - 07.18)

[1] Wigner Reseach Centre for Physics
[2] Corvinus University of Budapest

Abstract: We investigate quadratic binary unconstrained optimization problems (QUBOs) in the framework of the project. These are hard computational tasks equivalent to the Ising model, and can also be solved with quantum annelaers. They can be rewritten into a mixed integer linear problem by the introduction of auxiliary variables; this is the standard (Fortet) linearization. In our research we investigate the extent to which the Fortet linearization can be used to improve on a solution obtained from heuristics (like, e.g. quantum annealing or simulated bifurcation), or the verification of their optimality with duality conditions. Meanwhile we solve small but hard QUBO instances, which contributes also to the better understanding of their structural properties.

Péter Rakyta (ELTE, Wigner RCP), Gregory Morse (ELTE), Jakab Nádori (ELTE), Oskar Mencer (Maxeler Technologies), Zoltán Zimborás (Wigner RCP) (2022.05.01 - 2022.12.31)

Grant: NKFIH 2020-2.1.1-ED-2021-00179

Publication: Highly optimized quantum circuits synthesized via data-flow engines

Abstract: The formulation of quantum programs in terms of the fewest number of gate operations is crucial to retrieve meaningful results from the noisy quantum processors accessible these days. In this work we demonstrate a use case for Field Programmable Gate Array (FPGA) based data-flow engines (DFEs) to scale up optimization based quantum compilers to synthesize circuits up to 9-qubit programs. The developed DFE quantum computer simulator was designed to simulate arbitrary quantum circuit consisting of single qubit rotations and controlled two-qubit gates on FPGA chips. In our benchmark with the QISKIT package, the depth of the circuits produced by the SQUANDER package (with the DFE accelerator support) were less by 97% on average, while the fidelity of the circuits was still close to unity by an error of ∼ 10−4. I

Ágoston Kaposi (ELTE), Zoltán Kolarovszki (ELTE), Tamás Kozsik (ELTE), Zoltán Zimborás (Wigner FK) and Péter Rakyta (ELTE)

(2022.05.01 - 2022.12.31)

Grant: NKFIH 2020-2.1.1-ED-2021-00179

Abstract: Evaluating the Torontonian function is a central computational challenge in the simulation of Gaussian Boson Sampling (GBS) with threshold detection. In this work, we propose a recursive algorithm providing a polynomial speedup in the exact calculation of the Torontonian compared to state-of-the-art algorithms. According to our numerical analysis the complexity of the algorithm is proportional to N1.06912N/2 with N being the size of the problem. We also show that the recursive algorithm can be scaled up to HPC use cases making feasible the simulation of threshold GBS up to 35−40 photon clicks without the needs of large-scale computational capacities.

Publications: Ágoston Kaposi, Zoltán Kolarovszki, Tamás Kozsik, Zoltán Zimborás, Péter Rakyta: Polynomial speedup in Torontonian calculation by a scalable recursive algorithm ArXiv:2109.04528

Anna Horváth, Balázs Bámer, Gergely Gábor Barnaföldi and Dávid Légrády (2022.01.01 - 2022.07.30)

Wigner Research Centre for Physics

Grant: NKFIH 2020-2.1.1-ED-2021-00179

Abstract: We investigate the optical trajectories in non-linear optical medium applying standard description. We apply modern machine learning techniques for the image reconstruction.

Mihály András Pocsai, Imre Ferenc Barna, Gábor Bíró, Gergely Gábor Barnaföldi (2021.10.01 - 2022.08.31)

Wigner Research Centre for Physics

Grant: NKFIH 2020-2.1.1-ED-2021-00179

Abstract: In the concept of plasma based particle acceleration, the particles are accelerated in the wakefield generated by a driver pulse, which may be either a beam of charged particles or a short, intense laser pulse, instead of guiding,collimating and them in vacuum with strong electromagnetic fields [1]. The witness bunch is usually injected in the plasma from an external source, but in case of electron acceleration using a laser pulse as a driver pulse, at sufficiently high laser intensities, some of the plasma electrons are being trapped in the wakefield generated by the laser pulse. This phenomenon is referred as self-injection. In the schemes mentioned above, the driver bunches transfer their energy to the witness bunches through the plasma waves they generate. In the CERN–AWAKE experiment the wakefield is generated by a train of proton microbunches, produced from the SPS proton beam via the self-modulation instability [2]. In this experiment it is essential for the plasma to be ultrahomogeneous, furthemore, at prescribed points of the plasma, the plasma density has to follow the prescribed density ramps accurately. The plasma itself is produced by photoionising the rubidium vapour with a 120 fs long, intense, infra-red laser pulse. Therefore studying the corresponding photoionisation phenomena is a relevant sub-topic of the CERN–AWAKE experiment.

The processes in question have been already studied earlier via quantum mechanical simulations [3]. In our approach, we expanded the solution of the time-dependent Schrödinger-equaion (TDSE) on the basis of the eigenfunctions of the free Hamiltonian-operator of the Rubidium atom. The expansion coeffitiens are timedependent. Substituting this Ansatz into the TDSE, one obtains a first order, linear ODE system, referred as Coupled Channel Equations. Every channel, i. e. every time-dependent expansion coeffitient gives the occupation amplitude of the corresponding bound or continuum state of the rubidium atom. From the final state wave function, the total photoinisation probabilities, the photoelectron energy spectra, angular distributions and energy-and-angle resolved spectra can be obtained.

References:
[1] T. Tajima, J.M. Dawson: „Laser electron accelerator". Phys. Rev. Lett 43, 267–270 (1979).
[2] C. Petit-Jean-Genaz, G. Arduini, P. Michel, V. R. W. Schaa, (eds.), Proceedings, 5th International Particle Accelerator Conference (IPAC2014): Dresden, Germany, June 15–20, 2014, JACoW Conferences (CERN, Geneva, Switzerland, 2014).
[3] M.A. Pocsai, I.F. Barna and K. Tőkési: „Photoionisation of Rubidium in strong laser fields". Eur. Phys. J. D 73, 74 (2019).

Márk Margóczi, Dávid Légrády (2022.08.01 - 2022.12.31)

Budapest University of Technology and Economics

Grant: NKFIH 2020-2.1.1-ED-2021-00179

Abstract: Neutron transport calculations of dynamic Monte-Carlo method is emerging new area of research in the nuclear science. The dynamic Monte-Carlo calculation cannot only be used to foretell neutron kinetics, but to enable more complex dynamic simulations, it can also be coupled with thermal-hydraulic codes. The coupled calculation method raises questions of stability and convergence.

GUARDYAN (GPU Assisted Reactor Dynamic Analysis) is a dynamic Monte Carlobased neutron transport code. The GUARDYAN is able to calculate neutron kinetics, but for more complex reactor physics calculations thermal-hydraulic feedback becomes necessary. To achieve the desired calculations the GUARDIAN has been coupled with a SUBCHANFLOW sub-channel flow simulation code. The simplest tool for testing stability and convergence is the stochastic calculus, within the framework of which neutron kinetics can be approximated with a stochastic differential equation. If the variance contribution term of the equation is defined with Monte-Carlo assumptions, then the stochastic differential equation approximates the dynamic Monte-Carlo method simulation. If the problem is sufficiently simple, the neutron kinetics and thermal-hydraulic of the rector can be derived using analytical formulas. In order to calculate expected value, standard deviation and variance with adequate statistical uncertainty corresponding to the method, these equations must be compared with the solutions of practical problems which require numerous simulation.

Application of GPU cluster supports the publication of scientific journal articles, topic of which is the mapping of the relationship between stochastic differential equations and time-dependent Monte-Carlo-based neutron transport and the investigation of the spread of variance of stochastic neutron kinetics to variance of thermal-hydraulic.

Erzsébet Suhajda, Tamás Hegedűs (2022.08.01 - 2022.10.31)

Semmelweis University

Publication: Comprehensive Collection and Prediction of ABC Transmembrane Protein Structures in the AI Era of Structural Biology
Poster

Grant: NKFIH 2020-2.1.1-ED-2021-00179

Abstract: Membrane proteins play crucial roles in cells’ life. They can bind substrates, transport ions, molecules or even drugs in and out of the cell. Their structural role is also important, as they can anchor cells to each other or to different surfaces.

In addition to their function, their structure is also unique. Membrane proteins span the cell membrane, part of their structure is located in the hydrophobic interior of the membrane, forming a transmembrane domain, and other parts are located inside the cell (intracellular) or outside the cell (extracellular). Thus, experimental determination of the structure of transmembrane proteins is a difficult task. For crystallization, membrane proteins must be removed from the membrane bilayer, therefore their native structure is often destroyed, making experimental procedures lengthy, expensive and uncertain.

Therefore, despite their vital role, only about 5% of experimentally resolved protein structures belong to membrane proteins, whereas about 50% of currently marketed drugs act through membrane proteins. Our aim is to investigate the structure and dynamics of transmembrane protein complexes with ATPase activity (e.g. ABC transporters responsible for multidrug resistance in tumor cells or calcium pumps) using both an artificial intelligence (deep learning) based structure determination method (AlphaFold) and molecular dynamics (MD) simulations. Our results will make a contribution to the performance testing of these modelling methods on membrane proteins, to the better understanding of the structures of biologically relevant complexes, and therefore can serve as a basis for future drug developments.

Péter Rakyta, Zoltán Zimborás (2022.01.01 - 2022.06.31)

Eötvös University, Wigner Research Centre for Physics

Grant: NKFIH 2020-2.1.1-ED-2021-00179

Abstract: In this work we propose a novel numerical approach to decompose general quantum programs in terms of single- and two-qubit quantum gates with a CNOT gate count very close to the current theoretical lower bounds. In particular, it turns out that 15 and 63 CNOT gates are sufficient to decompose a general 3 and 4-qubit unitary, respectively, with high numerical accuracy. Our approach is based on a sequential optimization of parameters related to the single-qubit rotation gates involved in a pre-designed quantum circuit used for the decomposition. In addition, the algorithm can be adopted to sparse inter-qubit connectivity architectures provided by current mid-scale quantum computers, needing only a few additional CNOT gates to be implemented in the resulting quantum circuits.

Publications:
[1] Péter Rakyta, Zoltán Zimborás: Approaching the theoretical limit in quantum gate decomposition, Quantum 6 (2022) 710
DOI: 10.22331/q-2022-05-11-710

Gábor Tolnai, Dávid Légrády (2022.08.01 - 2022.12.31)

Budapest University of Technology and Economics

Publication: Adjoint-based Path Length Stretching in a Woodcock Framework with SIR Angular Biasing

Grant: NKFIH 2020-2.1.1-ED-2021-00179

Kivonat: The GUARDYAN (GPU Assisted Reactor Dynamic Analysis) code developed at the Budapest University of Technology and Economics Institute of Nuclear Techniques directly models the time-dependent phenomena occurring in nuclear reactors. In contrast to conventional reactor dynamics modelling methods, GUARDYAN applies little to no approximations at simulating the physical processes. Price to pay for ultimate accuracy is running time, a real second translates to 6-24h calculation time depending on the complexity of the reactor geometry.

This project aims at increasing the computation efficiency by applying variance reduction techniques. This is done by the importance (a.k.a. the adjoint) function used for biasing the interaction laws, for which calculation schemes are being developed in the form of nonanalog Woodcock tracking for free path sampling and scouting samples (sampling importance resampling - SIRS ) for the angular bias.

An accurately pre-calculated adjoint function is needed for the proper biasing, this is computed by GUARDYAN specifically for the problem at hand. Large computation effort is needed for producing the sufficiently detailed adjoint for a certain problem, but it can be used for the whole transient scenario. For demonstrating the usefulness of the new variance reduction scheme under development, several test cases of varying complexity should be analysed and the corresponding adjoint function generated demanding large GPU capacity.