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相关论文: On the extraction of spectral densities from latti…

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In this talk we discuss a novel method, that we have presented in Ref. [1], to extract hadronic spectral densities from lattice correlators by using deep learning techniques. Hadronic spectral densities play a crucial role in the study of…

高能物理 - 格点 · 物理学 2024-01-12 Michele Buzzicotti , Alessandro De Santis , Nazario Tantalo

Spectral densities encode non-perturbative information that enters the calculation of a plethora of physical observables in strongly coupled field theories. Phenomenological applications encompass aspects of standard-model hadronic physics,…

We expand the treatment of the problem of the extraction of smeared spectral densities from Euclidean correlators introduced in [Phys. Rev. D 99, 094508], providing an alternative which does not rely on the Backus-Gilbert regularization.…

高能物理 - 格点 · 物理学 2026-05-15 Alessandro Lupo , Nazario Tantalo

Hadronic spectral densities play a pivotal role in particle physics, a prime example being the R-ratio defined from electron-positron scattering into hadrons. To predict them from first principles using Lattice QCD, we face a numerically…

高能物理 - 格点 · 物理学 2024-01-15 Mattia Bruno , Leonardo Giusti , Matteo Saccardi

In quantum field theories, spectral densities are directly related to relevant physical observables. In Lattice QCD, their non-perturbative extraction from first principles requires the Inverse Laplace transform of Euclidean-time…

高能物理 - 格点 · 物理学 2025-01-29 Matteo Saccardi , Mattia Bruno , Leonardo Giusti

Spectral densities connect correlation functions computed in quantum field theory to observables measured in experiments. For strongly-interacting theories, their non-perturbative determinations from lattice simulations are therefore of…

高能物理 - 格点 · 物理学 2024-07-08 Mattia Bruno , Leonardo Giusti , Matteo Saccardi

We describe a new approach for evaluating hadronic correlation functions which combines Laplacian-Heaviside quark smearing with a stochastic estimator of quark propagators. This method utilizes noise dilution in a new way to reduce the…

高能物理 - 格点 · 物理学 2010-11-04 Justin Foley , Chik Him Wong , John Bulava , Keisuke Jimmy Juge , David Lenkner , Colin Morningstar , Michael Peardon

The inclusive hadronic decay-rates of the $\tau$ lepton are particularly interesting from the phenomenological point of view since they give access to the CKM matrix elements $V_{ud}$ and $V_{us}$. In this talk, we discuss how a recent…

A strategy to compute inclusive hadronic processes in lattice QCD is discussed. The key idea is to view the inclusive decay or scattering rate as a smeared spectrum. The Euclidean time dependence of correlators obtained on the lattice can…

高能物理 - 格点 · 物理学 2025-04-01 Shoji Hashimoto

Estimating decay parameters in lattice simulations is a computationally demanding problem, requiring several volumes and momenta. We explore an alternative approach, where the transition amplitude can be extracted from the spectral…

高能物理 - 格点 · 物理学 2025-04-22 Benoît Blossier , Jochen Heitger , Jan Neuendorf , Teseo San José

The lattice technique of studying the strong interaction of matter is used to obtain predictions of the hadronic spectrum. These simulations were performed by the UKQCD collaboration using full (unquenched) QCD. Details of the results, a…

高能物理 - 格点 · 物理学 2009-10-31 C. R. Allton

We present our sparse modeling study to extract spectral functions from Euclidean-time correlation functions. In this study covariance between different Euclidean times of the correlation function is taken into account, which was not done…

高能物理 - 格点 · 物理学 2023-11-28 Junichi Takahashi , Hiroshi Ohno , Akio Tomiya

This chapter provides a pedagogical introduction to theoretical studies of hadrons based on the fundamental theory of strong interactions - Quantum ChromoDynamics. A perturbative expansion in the strong coupling is not applicable at…

高能物理 - 格点 · 物理学 2025-05-16 Sasa Prelovsek

We present spectral functions extracted from Euclidean-time correlation functions by using sparse modeling. Sparse modeling is a method that solves inverse problems by considering only the sparseness of the solution we seek. To check…

高能物理 - 格点 · 物理学 2024-11-01 Junichi Takahashi , Hiroshi Ohno , Akio Tomiya

The extraction of spectral densities from Euclidean correlators evaluated on the lattice is an important problem, as these quantities encode physical information on scattering amplitudes, finite-volume spectra, inclusive decay rates, and…

高能物理 - 格点 · 物理学 2023-12-01 Luigi Del Debbio , Alessandro Lupo , Marco Panero , Nazario Tantalo

Spectral densities encode non-perturbative information crucial in computing physical observables in strongly coupled field theories. Using lattice gauge theory data, we perform a systematic study to demonstrate the potential of recent…

The extraction of decay parameters using lattice techniques is a computationally expensive task, requiring several volumes and group irreps to relate the spectrum on a lattice simulation to the infinite volume scattering. In this project we…

高能物理 - 格点 · 物理学 2024-12-23 Benoît Blossier , Jochen Heitger , Jan Neuendorf , Teseo San José

Recent progresses of lattice QCD studies for hadron spectroscopy and interactions are briefly reviewed. Some emphasis are given on a new proposal for a method, which enable us to calculate potentials between hadrons. As an example of the…

高能物理 - 格点 · 物理学 2014-02-14 Sinya Aoki

An extended multi-hadron operator is developed to extract the spectra of irreducible representations in the finite volume. The irreducible representations of the cubic group are projected using a coordinate-space operator. The correlation…

高能物理 - 格点 · 物理学 2018-04-18 Jia-jun Wu , Waseem Kamleh , Derek B. Leinweber , Gerrit Schierholz , Ross D. Young , James M. Zanotti

Several new developments in the calculation and interpretation of hadron density-density correlation functions are presented. The asymptotic behavior of correlation functions is determined from a tree diagram path integral. A method is…

高能物理 - 格点 · 物理学 2009-09-25 J. W. Negele , M. Burkardt , J. M. Grandy
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