arXiv subjects

Gert Aarts

Publications and source records attributed to Gert Aarts.

At least 37 records · Page 2Linked to original sources

arXiv2025

NRQCD Bottomonium at non-zero temperature using time-derivative moments

A well-known challenge for the lattice community is calculating the spectral function from the Euclidean correlator. We have approximated the spectral function and derived the mass and thermal width of particles through the time derivatives of the lattice correlator moments. We have focused on extracting the properties of bottomonium states, specifically $Υ$ and $χ_{b1}$. We will give an overview of the time-derivative moments approach and present results for the temperature dependence of the mass and width of both bottomonium states. The zero temperature results are consistent with experimental values, while results at higher temperatures are similar to those obtained using other methods.

hep-lat↗

arXiv2025

The NRQCD $Υ$ spectrum at non-zero temperature using Backus-Gilbert regularisations

Understanding how the properties of heavy mesons change as temperature increases is crucial for gaining valuable insights into the quark-gluon plasma. Information about meson masses and decay widths is encoded in the meson spectral function, which, in principle, can be extracted from Euclidean correlation functions via generalised Laplace transformations. However, this inverse problem is ill-posed for lattice correlation functions and requires regularisation. In this work, we present the latest results for bottomonium spectral functions obtained within the lattice NRQCD framework using the Backus-Gilbert regularisation, along with two other variants, one of which is commonly referred to as the HLT method. Our analysis employs Generation 2L anisotropic lattice configurations produced by the \textsc{Fastsum} collaboration.

hep-lat↗

arXiv2025

Anisotropic excited bottomonia from a basis of smeared operators

Bottomonia play a crucial role in our understanding of the quark gluon plasma. We present lattice non-relativistic QCD calculations of bottomonia at temperatures in the range $T \in [47, 380]$ MeV using the Fastsum Generation 2L anisotropic $N_f = 2 + 1$ ensembles. The use of a basis of smeared operators allows the extraction of excited-state masses at zero temperature and an investigation of their thermal properties at non-zero temperature. We find that the ground state signal is substantially improved by this variational approach at finite temperature. We also apply the time-derivative moments approach to the projected or optimal correlation functions at finite temperature.

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arXiv2025

Exploring Generative Networks for Manifolds with Non-Trivial Topology

The expressive power of neural networks in modelling non-trivial distributions can in principle be exploited to bypass topological freezing and critical slowing down in simulations of lattice field theories. Some popular approaches are unable to sample correctly non-trivial topology, which may lead to some classes of configurations not being generated. In this contribution, we present a novel generative method inspired by a model previously introduced in the ML community (GFlowNets). We demonstrate its efficiency at exploring ergodically configuration manifolds with non-trivial topology through applications such as triple ring models and two-dimensional lattice scalar field theory.

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arXiv2025

Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics

The integration of deep learning techniques and physics-driven designs is reforming the way we address inverse problems, in which accurate physical properties are extracted from complex data sets. This is particularly relevant for quantum chromodynamics (QCD), the theory of strong interactions, with its inherent limitations in observational data and demanding computational approaches. This perspective highlights advances and potential of physics-driven learning methods, focusing on predictions of physical quantities towards QCD physics, and drawing connections to machine learning(ML). It is shown that the fusion of ML and physics can lead to more efficient and reliable problem-solving strategies. Key ideas of ML, methodology of embedding physics priors, and generative models as inverse modelling of physical probability distributions are introduced. Specific applications cover first-principle lattice calculations, and QCD physics of hadrons, neutron stars, and heavy-ion collisions. These examples provide a structured and concise overview of how incorporating prior knowledge such as symmetry, continuity and equations into deep learning designs can address diverse inverse problems across different physical sciences.

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arXiv2024

Random Matrix Theory for Stochastic Gradient Descent

Investigating the dynamics of learning in machine learning algorithms is of paramount importance for understanding how and why an approach may be successful. The tools of physics and statistics provide a robust setting for such investigations. Here we apply concepts from random matrix theory to describe stochastic weight matrix dynamics, using the framework of Dyson Brownian motion. We derive the linear scaling rule between the learning rate (step size) and the batch size, and identify universal and non-universal aspects of weight matrix dynamics. We test our findings in the (near-)solvable case of the Gaussian Restricted Boltzmann Machine and in a linear one-hidden-layer neural network.

hep-lat↗

arXiv2024

Diffusion models and stochastic quantisation in lattice field theory

Diffusion models are currently the leading generative AI approach used for image generation in e.g. DALL-E and Stable Diffusion. In this talk we relate diffusion models to stochastic quantisation in field theory and employ it to generate configurations for scalar fields on a two-dimensional lattice. We end with some speculations on possible applications.

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arXiv2024

Stochastic weight matrix dynamics during learning and Dyson Brownian motion

We demonstrate that the update of weight matrices in learning algorithms can be described in the framework of Dyson Brownian motion, thereby inheriting many features of random matrix theory. We relate the level of stochasticity to the ratio of the learning rate and the mini-batch size, providing more robust evidence to a previously conjectured scaling relationship. We discuss universal and non-universal features in the resulting Coulomb gas distribution and identify the Wigner surmise and Wigner semicircle explicitly in a teacher-student model and in the (near-)solvable case of the Gaussian restricted Boltzmann machine.

cond-mat.dis-nn↗

arXiv2024

Diffusion models learn distributions generated by complex Langevin dynamics

The probability distribution effectively sampled by a complex Langevin process for theories with a sign problem is not known a priori and notoriously hard to understand. Diffusion models, a class of generative AI, can learn distributions from data. In this contribution, we explore the ability of diffusion models to learn the distributions created by a complex Langevin process.

hep-lat↗

arXiv2024

Thermal lattice QCD results from the FASTSUM collaboration

The FASTSUM Collaboration has developed a comprehensive research programme in thermal lattice QCD using 2+1 flavour ensembles. We review our recent hadron spectrum analyses of open charm mesons and charm baryons at non-zero temperature. We also detail our determination of the interquark potential in the bottomonium system using NRQCD quarks. All of our work uses anisotropic lattices where the temporal lattice spacing is considerably finer than the spatial one allowing better resolution of temporal correlation functions.

hep-lat↗

arXiv2024

Dyson Brownian motion and random matrix dynamics of weight matrices during learning

During training, weight matrices in machine learning architectures are updated using stochastic gradient descent or variations thereof. In this contribution we employ concepts of random matrix theory to analyse the resulting stochastic matrix dynamics. We first demonstrate that the dynamics can generically be described using Dyson Brownian motion, leading to e.g. eigenvalue repulsion. The level of stochasticity is shown to depend on the ratio of the learning rate and the mini-batch size, explaining the empirically observed linear scaling rule. We verify this linear scaling in the restricted Boltzmann machine. Subsequently we study weight matrix dynamics in transformers (a nano-GPT), following the evolution from a Marchenko-Pastur distribution for eigenvalues at initialisation to a combination with additional structure at the end of learning.

cond-mat.dis-nn↗

arXiv2024

Diffusion models for lattice gauge field simulations

We develop diffusion models for lattice gauge theories which build on the concept of stochastic quantization. This framework is applied to $U(1)$ gauge theory in $1+1$ dimensions. We show that a model trained at one small inverse coupling can be effectively transferred to larger inverse coupling without encountering issues related to topological freezing, i.e., the model can generate configurations corresponding to different couplings by introducing the Boltzmann factors as physics conditions, while maintaining the correct physical distributions without any additional training. This demonstrates the potential of physics-conditioned diffusion models for efficient and flexible lattice gauge theory simulations.

hep-lat↗

arXiv2024

Diffusion Models as Stochastic Quantization in Lattice Field Theory

In this work, we establish a direct connection between generative diffusion models (DMs) and stochastic quantization (SQ). The DM is realized by approximating the reversal of a stochastic process dictated by the Langevin equation, generating samples from a prior distribution to effectively mimic the target distribution. Using numerical simulations, we demonstrate that the DM can serve as a global sampler for generating quantum lattice field configurations in two-dimensional $ϕ^4$ theory. We demonstrate that DMs can notably reduce autocorrelation times in the Markov chain, especially in the critical region where standard Markov Chain Monte-Carlo (MCMC) algorithms experience critical slowing down. The findings can potentially inspire further advancements in lattice field theory simulations, in particular in cases where it is expensive to generate large ensembles.

hep-lat↗

arXiv2024

Non-zero temperature study of spin 1/2 charmed baryons using lattice gauge theory

We study the behaviour of spin $1/2$ charmed baryons as the temperature increases. We make use of anisotropic lattice QCD simulations with $N_f = 2 + 1$ dynamical flavours. After determining the positive and negative parity ground state masses at the lowest temperature, we investigate the effect of rising temperature using ratios of thermal lattice correlators with both so-called reconstructed correlators and with simple model correlators. This avoids difficulties associated with non-zero temperature fitting or spectral reconstruction. We find that temperature effects are prominent throughout the hadronic phase for all negative parity channels considered and for some positive parity channels. Subsequently and where possible, we determine the masses of the ground states as a function of temperature. Finally we consider the effect of chiral symmetry restoration and extract an estimate of the pseudocritical temperature from singly charmed baryonic correlators.

hep-lat↗

arXiv2024

Scalar field Restricted Boltzmann Machine as an ultraviolet regulator

Restricted Boltzmann Machines (RBMs) are well-known tools used in Machine Learning to learn probability distribution functions from data. We analyse RBMs with scalar fields on the nodes from the perspective of lattice field theory. Starting with the simplest case of Gaussian fields, we show that the RBM acts as an ultraviolet regulator, with the cutoff determined by either the number of hidden nodes or a model mass parameter. We verify these ideas in the scalar field case, where the target distribution is known, and explore implications for cases where it is not known using the MNIST data set. We also demonstrate that infrared modes are learnt quickest.

hep-lat↗

arXiv2023

Reconstructed (charm) baryon methods at finite temperature on anisotropic lattices

Reconstructed-correlator methods have been used to investigate thermal effects in mesonic correlation functions in a fit-independent manner. This technique has recently been extended to the baryonic sector. In this work different ways of implementing this approach for baryon correlators are examined. Using both real and synthetic data it is found that for heavy baryons, such as the $Ξ_{cc}(ccu)$ baryon, different choices are equivalent and that for the lighter nucleon the effect of different implementations is minimal. Further comparison to the so-called "double ratio" using the FASTSUM Generation 2L thermal ensembles shows that reconstructed-correlator ratios and double ratios contain nearly identical quantitative information.

hep-lat↗

arXiv2023

Generative Diffusion Models for Lattice Field Theory

This study delves into the connection between machine learning and lattice field theory by linking generative diffusion models (DMs) with stochastic quantization, from a stochastic differential equation perspective. We show that DMs can be conceptualized by reversing a stochastic process driven by the Langevin equation, which then produces samples from an initial distribution to approximate the target distribution. In a toy model, we highlight the capability of DMs to learn effective actions. Furthermore, we demonstrate its feasibility to act as a global sampler for generating configurations in the two-dimensional $ϕ^4$ quantum lattice field theory.

hep-lat↗

arXiv2023

Phase Transitions in Particle Physics -- Results and Perspectives from Lattice Quantum Chromo-Dynamics

Phase transitions in a non-perturbative regime can be studied by ab initio Lattice Field Theory methods. The status and future research directions for LFT investigations of Quantum Chromo-Dynamics under extreme conditions are reviewed, including properties of hadrons and of the hypothesized QCD axion as inferred from QCD topology in different phases. We discuss phase transitions in strong interactions in an extended parameter space, and the possibility of model building for Dark Matter and Electro-Weak Symmetry Breaking. Methodological challenges are addressed as well, including new developments in Artificial Intelligence geared towards the identification of different phases and transitions.

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