arXiv subjects

Balázs Pál

Publications and source records attributed to Balázs Pál.

5 recordsLinked to original sources

arXiv2026

Cylindrical cosmological simulations with StePS

The global topology of the Universe can affect long-range gravitational forces via boundary conditions. Detailed studies of non-trivial topologies require simulations that natively adopt such geometries. Cosmological $N$-body simulations typically evolve matter in a periodic cubic box. While numerically convenient, this imposes a non-trivial three-torus topology that affects long-range gravitational forces, potentially biasing large-scale statistics. We introduce a compactified simulation framework that is only periodic along a single axis, characterised by an infinite topology with isotropic boundary conditions towards the perpendicular directions, namely, a $\mathrm{S}^1\times\mathbb{R}^2$ (slab) topology. This new simulation geometry is ideal for simulating systems with cylindrical symmetries such as filaments or certain anisotropic cosmological models. We compactified the comoving space via an inverse stereographic projection along the radial direction of a periodic cylinder. Then, we evolved the particles based on Newtonian dynamics. A smoothly varying spatial and mass resolution with radius suppresses edge artefacts at the free outer boundary. Our implementation in the StePS (STEreographically Projected cosmological Simulations) framework uses a direct force calculation that maps efficiently to GPUs, as well as an Octree force calculation for use on large CPU clusters. The cylindrical domain's topology enables fully self-consistent simulations to be run in the $\mathrm{S}^1\times\mathbb{R}^2$ manifold, while mitigating any periodic-image artefacts with respect to targets whose symmetries are mismatched to a cubic box. The main trade-off is a radially varying resolution with distinct systematics and analysis requirements. Finally, we demonstrate the accuracy of the new simulation method via a standard lambda cold dark matter cosmological simulation.

astro-ph.CO↗

arXiv2026

STEPSIC: Initial condition generator for stereographic cosmological simulations

Conventional cosmological initial condition generators are designed exclusively for fully periodic cubic domains and cannot produce the non-periodic, observer-centric configurations required by stereographically projected N-body codes such as StePS. We present STEPSIC, an open-source initial condition generator that extends Lagrangian perturbation theory-based initial conditions to the spherical and cylindrical geometries used by StePS, while also supporting cuboid domains with arbitrary aspect ratios. The code constructs Gaussian random density fields on anisotropy-free Fourier grids with cubic voxels, applies first- and second-order LPT to obtain displacement and velocity fields, and interpolates these onto particles via B-spline mass-assignment kernels with Fourier-space deconvolution. For stereographic geometries, a multiresolution scheme maps displacement fields across the radially varying particle mass resolution intrinsic to the projection. Both standard and paired-and-fixed variance-reduced realizations are supported. In periodic cubic boxes, the recovered matter power spectrum agrees with the input linear theory prediction to better than 0.5% up to half the Nyquist wavenumber, independent of box aspect ratio (tested up to 10:1). Cross-validation against monofonic using identical white noise fields yields sub-percent power spectrum agreement, with a small residual offset consistent with differences between two independent implementations. Full N-body evolution of matched cylindrical StePS runs confirms that second-order LPT correctly suppresses the 2-3% transient power excess present in first-order initial conditions.

astro-ph.CO↗

arXiv2025

Denoising medium resolution stellar spectra with U-Net convolutional neural networks

We investigated the use of a U-Net convolutional neural network for denoising simulated medium-resolution spectroscopic observations of stars. Simulated spectra were generated under realistic observational conditions resembling the Subaru Prime Focus Spectrograph (PFS). We found that our U-Net model effectively captured spectral features, achieving an average relative error of around $1\%$ across a broad range of stellar parameters, despite a limited training set of only $1000$ observations and a relatively short training period. Although U-Net did not reach the performance previously demonstrated by fully-connected denoising autoencoders (DAEs) consisting of dense layers and trained extensively on larger datasets, it outperformed dense networks trained under similarly constrained conditions. These results indicate that the U-Net architecture offers rapid, robust feature learning and may be particularly advantageous in scenarios involving initial denoising, subsequently refined by more accurate, but otherwise slower deep-learning models.

astro-ph.IM↗

arXiv2024

Simulating Rotating Newtonian Universes

We present the results of a novel type of numerical simulation that realizes a rotating Universe with a shear-free, rigid body rotation inspired by a Gödel-like metric. We run cosmological simulations of unperturbed glasses with various degrees of rotation in the Einstein-de Sitter and the $Λ$CDM cosmologies. To achieve this, we use the StePS N-body code capable of simulating the infinite Universe, overcoming the technical obstacles of classical toroidal (periodic) topologies that would otherwise prevent us from running such simulations. Results show a clear anisotropy between the polar and equatorial expansion rates with more than $1\%$ deviation from the isotropic case for maximal rotation without closed timeline curves within the horizon, $ω_{0} \approx 10^{-3}$ Gyr$^{-1}$; a considerable effect in the era of precision cosmology.

astro-ph.CO↗

arXiv2024

Denoising medium resolution stellar spectra with neural networks

We trained denoiser autoencoding neural networks on medium resolution simulated optical spectra of late-type stars to demonstrate that the reconstruction of the original flux is possible at a typical relative error of a fraction of a percent down to a typical signal-to-noise ratio of 10 per pixel. We show that relatively simple networks are capable of learning the characteristics of stellar spectra while still flexible enough to adapt to different values of extinction and fluxing imperfections that modifies the overall shape of the continuum, as well as to different values of Doppler shift. Denoised spectra can be used to find initial values for traditional stellar template fitting algorithms and - since evaluation of pre-trained neural networks is significantly faster than traditional template fitting - denoiser networks can be useful when a fast analysis of the noisy spectrum is necessary, for example during observations, between individual exposures.

astro-ph.IM↗