arXiv subjects

Péter Kovács

Publications and source records attributed to Péter Kovács.

At least 19 recordsLinked to original sources

arXiv2026

Glueballs and fractional anomalous determinants at nonzero $θ$, and the decays of the X(2370)

Using fractional anomalous determinants, we construct a model of the trace and axial anomalies of a $SU(3)$ gauge theory at nonzero $θ$ angle through the couplings to scalar and pseudoscalar glueballs. In the pure gauge theory, we reproduce the $θ$ dependence of the vacuum energy and the scalar-glueball mass from the lattice, and estimate the jump in the topological charge density for the first-order transition at $θ= π$. In QCD, the coupling of a pseudoscalar glueball to fractional anomalous determinants describes all of the four three-pseudoscalar decay channels of the $0^{-+}$ glueball candidate $X(2370)$ measured by the BESIII collaboration. Measuring the decay rates for $ηηη$ and $ηηη'$ can provide a stringent test of the model.

hep-ph↗

arXiv2026

Classification and detection of multiple UAVs using rational Gaussian wavelet neural networks

The detection of unmanned aerial vehicles (UAVs) is important for the protection of civilian and military infrastructure. In this paper we propose a cost effective UAV detection system using sound signals obtained from microphones. The recorded signals are passed through a signal processing pipeline which employs interpretable adaptive feature extractors using so-called rational Gaussian wavelets. These adaptive wavelet transformations are embedded into and trained together with an underlying small neural network which detects and classifies UAVs based on the obtained features. This leads to a physically interpretable machine learning algorithm that in addition to classifying UAVs is also capable of detecting UAV swarms. We demonstrate our results using data collected in indoor studio and noisy outdoor environments. We conclude that the proposed method outperforms traditional machine learning approaches for detecting and classifying single UAVs as well as drone swarms, while retaining a high degree of interpretability. Our implementation of the proposed methods is made publicly available for reproducibility.

cs.LG↗

arXiv2026

Chiral anomaly: from vacuum to Columbia plot

We use a low-energy effective approach, the extended linear sigma model, to study realizations of the $U(1)_A$ anomaly with different operators, linear and quadratic in the 't Hooft determinant. After discussing the parameterization in agreement with vacuum's phenomenology, we investigate the influence of these different anomaly terms on the Columbia plot: the square of the 't Hooft determinant favors a cross-over for small quark masses. Finally, we also discuss the extension of the 't Hooft determinant to cases in which different mesonic multiplets interact with each other. Novel chiral anomalous interaction terms involving excited (pseudo)scalar states, pseudovector, and pseudotensor mesons are expressed via a mathematical extension of the determinant, denoted as a polydeterminant.

hep-ph↗

arXiv2025

Hybrid Rendering for Multimodal Autonomous Driving: Merging Neural and Physics-Based Simulation

Neural reconstruction models for autonomous driving simulation have made significant strides in recent years, with dynamic models becoming increasingly prevalent. However, these models are typically limited to handling in-domain objects closely following their original trajectories. We introduce a hybrid approach that combines the strengths of neural reconstruction with physics-based rendering. This method enables the virtual placement of traditional mesh-based dynamic agents at arbitrary locations, adjustments to environmental conditions, and rendering from novel camera viewpoints. Our approach significantly enhances novel view synthesis quality -- especially for road surfaces and lane markings -- while maintaining interactive frame rates through our novel training method, NeRF2GS. This technique leverages the superior generalization capabilities of NeRF-based methods and the real-time rendering speed of 3D Gaussian Splatting (3DGS). We achieve this by training a customized NeRF model on the original images with depth regularization derived from a noisy LiDAR point cloud, then using it as a teacher model for 3DGS training. This process ensures accurate depth, surface normals, and camera appearance modeling as supervision. With our block-based training parallelization, the method can handle large-scale reconstructions (greater than or equal to 100,000 square meters) and predict segmentation masks, surface normals, and depth maps. During simulation, it supports a rasterization-based rendering backend with depth-based composition and multiple camera models for real-time camera simulation, as well as a ray-traced backend for precise LiDAR simulation.

cs.GR↗

arXiv2025

Generalized rational Prony and Bernoulli methods

The generalized operator-based Prony method is an important tool for describing signals which can be written as finite linear combinations of eigenfunctions of certain linear operators. On the other hand, Bernoulli's algorithm and its generalizations can be used to recover the parameters of rational functions belonging to finite-dimensional subspaces of $H_2$ Hardy-Hilbert spaces. In this work, we discuss several results related to these methods. We discuss a rational variant of the generalized operator-based Prony method and show that in fact, any Prony problem can be treated this way. This realization establishes the connection between Prony and Bernoulli methods and allows us to address some well-known numerical pitfalls. Several numerical experiments are provided to showcase the usefulness of the introduced methods. These include problems related to the identification of time-delayed linear systems and parameter recovery problems in reproducing kernel Hilbert spaces.

math.NA↗

arXiv2025

Transformer Encoder and Multi-features Time2Vec for Financial Prediction

Financial prediction is a complex and challenging task of time series analysis and signal processing, expected to model both short-term fluctuations and long-term temporal dependencies. Transformers have remarkable success mostly in natural language processing using attention mechanism, which also influenced the time series community. The ability to capture both short and long-range dependencies helps to understand the financial market and to recognize price patterns, leading to successful applications of Transformers in stock prediction. Although, the previous research predominantly focuses on individual features and singular predictions, that limits the model's ability to understand broader market trends. In reality, within sectors such as finance and technology, companies belonging to the same industry often exhibit correlated stock price movements. In this paper, we develop a novel neural network architecture by integrating Time2Vec with the Encoder of the Transformer model. Based on the study of different markets, we propose a novel correlation feature selection method. Through a comprehensive fine-tuning of multiple hyperparameters, we conduct a comparative analysis of our results against benchmark models. We conclude that our method outperforms other state-of-the-art encoding methods such as positional encoding, and we also conclude that selecting correlation features enhance the accuracy of predicting multiple stock prices.

cs.LG↗

arXiv2025

Ordinary and exotic mesons in the extended Linear Sigma Model

The extended Linear Sigma Model (eLSM) is a hadronic model based on the global symmetries of QCD and the corresponding explicit, anomalous, and spontaneous breaking patterns. In its basic three-flavor form, its mesonic part contains the dilaton/glueball as well as the nonets of (pseudo)scalar and (axial-)vector mesons, thus chiral symmetry is linearly realized. In the chiral limit and neglecting the chiral anomaly, only one term -- within the dilaton potential -- breaks dilatation invariance. Spontaneous symmetry breaking is implemented by a generalization of the Mexican-hat potential, with explicit symmetry breaking responsible for its tilting. The overall mesonic phenomenology up to 2 GeV is in agreement with the PDG compilation of masses and decay widths. The eLSM was enlarged to include other conventional quark-antiquark nonets (pseudovector and orbitally excited vector mesons, (axial-)tensor mesons, etc.), as well as two nonets of hybrid mesons, the lightest one with exotic quantum numbers $J^{PC} = 1^{-+}$ not allowed for $\bar{q}q$ objects such as the resonance $π_1(1600)$ and the recently discovered $η_1(1855)$. In doing so, different types of chiral multiplets are introduced: heterochiral and homochiral multiplets, which differ in the way they transform under chiral transformations. Moreover, besides the scalar glueball, the tensor, the pseudoscalar and the vector glueballs were coupled to the eLSM: the scalar $f_0(1710)$ turns out to be mostly gluonic, the tensor glueball couples strongly to vector mesons, and the pseudoscalar glueball couples can be assigned to $X(2370)$ or $X(2600)$. The eLSM contains chiral partners on an equal footing and is therefore well suited for studies of chiral symmetry restoration at nonzero temperature and densities. The QCD phase diagram and the location of the critical endpoint were investigated within this framework.

hep-ph↗

arXiv2025

Naive Gaussian approximation in a quark-meson model

A sequence of approximations is derived to go beyond the mean-field level in the case of a simple linear sigma model. A naive, local version of the Gaussian approximation is discussed for the $2+1$ flavor extended Polyakov quark-meson model at finite temperature and chemical potential. Although at small chemical potential the pion mass has an unphysical thermal behavior in the usual parameterizations, it is shown that the pseudocritical temperature decreases in the presence of the thermal pion fluctuations. Compared to the mean-field level, the location of the CEP is only slightly modified, while the critical scaling around the CEP is unaffected.

hep-ph↗

arXiv2025

Observability of dynamical tides in merging eccentric neutron star binaries

While dynamical tides only become relevant during the last couple of orbits for circular inspirals, orbital eccentricity can increase their impact during earlier phases of the inspiral by exciting tidal oscillations at each close encounter. We investigate the effect of dynamical tides on the orbital evolution of eccentric neutron star binaries using post-Newtonian numerical simulations and construct an analytic stochastic model that reproduces the numerical results. Our study reveals a strong dependence of dynamical tides on the pericenter distance, with the fractional energy transferred to dynamical tides over that dissipated in gravitational waves (GWs) exceeding $\sim1\%$ at separations $r_\mathrm{p}\lesssim50$ km for large eccentricities. We demonstrate that the effect of dynamical tides on orbital evolution can manifest as a phase shift in the GW signal. We show that the signal-to-noise ratio of the GW phase shift can reach the detectability threshold of $8$ with a single aLIGO detector at design densitivity for eccentric neutron star binaries at a distance of $40$ Mpc. This requires a pericenter distance of $r_\mathrm{p0}\lesssim68$ km ($r_\mathrm{p0}\lesssim76$ km) at binary formation with eccentricity close to $1$ for a reasonable tidal deformability and f-mode frequency of $500$ and $1.73$ kHz ($700$ and $1.61$ kHz), respectively. The observation of the phase shift will enable measuring the f-mode frequency of neutron stars independently from their tidal deformability, providing significant insights into neutron star seismology and the properties of the equation of state. We also explore the potential of distinguishing between equal-radius and twin-star binaries, which could provide an opportunity to reveal strong first-order phase transitions in the nuclear equation of state.

astro-ph.HE↗

arXiv2025

Rational Gaussian wavelets and corresponding model driven neural networks

In this paper we consider the continuous wavelet transform using Gaussian wavelets multiplied by an appropriate rational term. The zeros and poles of this rational modifier act as free parameters and their choice highly influences the shape of the mother wavelet. This allows the proposed construction to approximate signals with complex morphology using only a few wavelet coefficients. We show that the proposed rational Gaussian wavelets are admissible and provide numerical approximations of the wavelet coefficients using variable projection operators. In addition, we show how the proposed variable projection based rational Gaussian wavelet transform can be used in neural networks to obtain a highly interpretable feature learning layer. We demonstrate the effectiveness of the proposed scheme through a biomedical application, namely, the detection of ventricular ectopic beats (VEBs) in real ECG measurements.

stat.ML↗

arXiv2025

Anomalous $U(1)_A$ couplings and the Columbia plot

When the quark masses are lighter than those in QCD, the standard lore is that a chiral transition of first order must emerge for three, light flavors. Recently, however, numerical simulations on the lattice suggest that the chiral transition is of second order in the chiral limit. Using an extended linear sigma model in the mean field approximation, we study the relation between terms which break the anomalous, $U(1)_A$ symmetry and the order of the chiral phase transition, especially how a chiral transition of second order can arise for three, massless flavors. We note that in an (unphysical) region of the "Columbia" phase diagram, when the strange quark mass is light and negative, corresponding to topological angle $θ=π$, the $CP$ symmetry is spontaneously broken.

hep-ph↗

arXiv2024

Learning ECG Signal Features Without Backpropagation Using Linear Laws

This paper introduces LLT-ECG, a novel method for electrocardiogram (ECG) signal classification that leverages concepts from theoretical physics to automatically generate features from time series data. Unlike traditional deep learning approaches, LLT-ECG operates in a forward manner, eliminating the need for backpropagation and hyperparameter tuning. By identifying linear laws that capture shared patterns within specific classes, the proposed method constructs a compact and verifiable representation, enhancing the effectiveness of downstream classifiers. We demonstrate LLT-ECG's state-of-the-art performance on real-world ECG datasets from PhysioNet, underscoring its potential for medical applications where speed and verifiability are crucial.

cs.LG↗

arXiv2024

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

Finite volume effects are studied both with low-momentum cutoff and with momentum discretization in the framework of an (axial)vector meson extended quark-meson model with Polyakov-loop variables. In the momentum cutoff scenario, the CEP moves to lower temperatures and larger quark chemical potentials as the characteristic system size is reduced, however, the treatment of the vacuum term significantly affects its trajectory. The size dependence of the baryon fluctuations is also studied by the kurtosis and the skewness, both of which show moderate dependence on temperature and some dependence on quark chemical potential. The order of the phase transition is also studied near the chiral limit at finite system size and found to be second-order only at vanishing explicit breaking. The implementation of the finite size effect with momentum discretization is more complicated and shows peculiar behavior due to the different modes dropping below the Fermi surface and strong dependence on the type of the boundary condition chosen. We found that both the different boundary conditions and the treatment of the vacuum term cause significant changes in the trajectory of the CEP as the characteristic system size is changed.

hep-ph↗

arXiv2024

Phenomenology of isospin-symmetry breaking with vector mesons

We study the effect of isospin-symmetry breaking in the framework of the extended Linear Sigma Model (eLSM) in vacuum. In this model, several particles mix with each other at tree level, due to the three non-zero scalar condensates (non-strange, strange, isospin). We resolve these mixings with the help of various field transformations. We compute all possible meson mixings and decay widths at tree level and perform a $χ^2$ fit to PDG data. A very good fit is found if we exclude the (very small $\sim 130$~keV) $ω\to ππ$ decay. We also investigate the violation of Dashen's theorem.

hep-ph↗

arXiv2023

Finite volume effects in the extended linear sigma model via low momentum cutoff

Contrary to field theoretical calculations in the thermodynamic limit where the volume is assumed to be infinitely large, the heavy-ion collisions always carry the effects of finite size. A sufficiently small system size is expected to affect the thermodynamic quantities and the phase diagram of the strongly interacting matter. To study these effects one can take into account the finite spatial extent of the system within the framework of an effective model too, via the restriction of the momentum integrals using discretization or in a simplified case using a low momentum cutoff. We investigated the effects of the finite volume in a vector meson extended Polyakov quark-meson model and found a remarkable change in the thermodynamics and the phase transition, especially in the location of the critical endpoint.

hep-ph↗

arXiv2023

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

The existence of quark matter inside the heaviest neutron stars has been the topic of numerous recent studies, many of them suggesting that a phase transition to strongly interacting conformal matter inside neutron stars is feasible. Here we examine this hybrid star scenario using a soft and a stiff hadronic model, a constituent quark model with three quark flavours, and applying a smooth crossover transition between the two. Within a Bayesian framework, we study the effect of up-to-date constraints from neutron star observations on the equation-of-state parameters and various neutron star observables. Our results show that a pure quark core is only possible if the maximum mass of neutron stars is below $\sim2.35~M_\odot$. However, we also find, consistently with other studies, that a peak in the speed of sound, exceeding $1/3$, is highly favoured by astrophysical measurements, which might indicate the percolation of hadrons at $\sim3-4n_0$. Even though our prediction for the phase transition parameters varies depending on the specific astrophysical constraints utilized, the position of the speed of sound peak only changes slightly, while the existence of pure quark matter below $\sim4 n_0$, using our parameterization, is disfavoured. On the other hand, the preferred range for the EoS shows signs of conformality above $\sim4n_0$. Additionally, we present the difference in the upper bounds of radius estimates using the full probability density data and sharp cut-offs, and stress the necessity of using the former.

astro-ph.HE↗

arXiv2022

Simulation of the isotropic ultra-high energy photons flux in the solar magnetic field

Both the lack of observation of ultra-high energy (UHE) photons and the limitations of the state-of-the-art methodology being applied for their identification motivate studies on alternative approaches to the relevant simulations and the related observational strategies. One of such new approaches is proposed in this report and it concerns new observables allowing indirect identification of UHE photons through cosmic ray phenomena composed of many spatially correlated extensive air showers or primary cosmic rays observed at one time. The study is based on simulations of interactions of UHE photons with the magnetic field of the Sun using the PRESHOWER program with some essential modifications. One of the expected results of such interactions is a generation of cosmic ray ensembles (CRE) in the form of very thin and very elongated cascades of secondary photons of energies spanning the whole cosmic ray energy spectrum. Upon entering the Earth's atmosphere, these cascades or their parts may generate uniquely characteristic walls of spatially correlated extensive air showers, and the effect is expected also in cases when primary UHE photons are not directed towards the Earth. Particle distributions in these multi-primary UHE photon footprints are expected to have thicknesses of the order of meters and elongations reaching even hundreds of millions kilometers, making them potentially observable with a global, multi-experiment approach, including re-exploring of the historical data, with the expected event rate exceeding the capabilities of even very large cosmic ray observatories. The methods described in this report allow for simulating potentially observable quantities related to CRE induced by UHE photons: densities, energy spectra and geographical orientations of particles at the top of the Earth's atmosphere.

astro-ph.HE↗

arXiv2022

Diagnostic Quality Assessment for Low-Dimensional ECG Representations

There have been several attempts to quantify the diagnostic distortion caused by algorithms that perform low-dimensional electrocardiogram (ECG) representation. However, there is no universally accepted quantitative measure that allows the diagnostic distortion arising from denoising, compression, and ECG beat representation algorithms to be determined. Hence, the main objective of this work was to develop a framework to enable biomedical engineers to efficiently and reliably assess diagnostic distortion resulting from ECG processing algorithms. We propose a semiautomatic framework for quantifying the diagnostic resemblance between original and denoised/reconstructed ECGs. Evaluation of the ECG must be done manually, but is kept simple and does not require medical training. In a case study, we quantified the agreement between raw and reconstructed (denoised) ECG recordings by means of kappa-based statistical tests. The proposed methodology takes into account that the observers may agree by chance alone. Consequently, for the case study, our statistical analysis reports the "true", beyond-chance agreement in contrast to other, less robust measures, such as simple percent agreement calculations. Our framework allows efficient assessment of clinically important diagnostic distortion, a potential side effect of ECG (pre-)processing algorithms. Accurate quantification of a possible diagnostic loss is critical to any subsequent ECG signal analysis, for instance, the detection of ischemic ST episodes in long-term ECG recordings.

eess.SP↗