News from bottomonium spectral functions in thermal QCD

Offler, Sam; Aarts, Gert; Allton, Chris; Glesaaen, Jonas; Jäger, Benjamin; Kim, Seyong; Lombardo, Maria Paola; Ryan, Sinéad M.; Skullerud, Jon-Ivar

doi:10.22323/1.363.0076

Cited by 15 publications

(10 citation statements)

References 8 publications

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“…[54] kernel ridge regression was applied to mock data in quantum many-body physics; a first application to QCD data can be found in Ref. [55] for bottomonium correlators. More developments in the realm of machine learning are expected in the near future.…”

Section: Other Approachesmentioning

confidence: 99%

Electrical conductivity of the quark-gluon plasma: perspective from lattice QCD

Aarts

Николаев

2021

Eur. Phys. J. A

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A discussion on the electrical conductivity of the quark-gluon plasma as determined by lattice QCD is given. After a reminder of basic definitions and expectations, various methods for spectral reconstruction are reviewed, including the use of Ansätze and sum rules, the Maximum Entropy and Backus-Gilbert methods, and Tikhonov regularisation. A comprehensive overview of lattice QCD results obtained so far is given, including a comparison of the different lattice formulations. A noticeable consistency for the conductivities obtained is seen, in spite of the differences in the lattice setups and spectral reconstruction methods. It is found that in the case of quenched QCD little temperature dependence of $$\sigma /T$$ σ / T is seen in the temperature range investigated, while for QCD with dynamical quarks a reduction of $$\sigma /T$$ σ / T in the vicinity of the thermal crossover is observed, compared to its value in the QGP. Several open questions are posed at the end.

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Section: Other Approachesmentioning

confidence: 99%

Electrical conductivity of the quark-gluon plasma: perspective from lattice QCD

Aarts

Николаев

2021

Eur. Phys. J. A

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“…Together with the SOM method presented in [17], these stochastic methods have for example, been deployed in the study of nuclear matter at high temperatures in [18]. Recently, the community has seen heightened activity in exploring the use of neural networks for the solution of inverse problems, e.g., in [19][20][21][22].…”

Section: Beyond Memmentioning

confidence: 99%

Bryan’s Maximum Entropy Method—Diagnosis of a Flawed Argument and Its Remedy

Rothkopf

2020

Data

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The Maximum Entropy Method (MEM) is a popular data analysis technique based on Bayesian inference, which has found various applications in the research literature. While the MEM itself is well-grounded in statistics, I argue that its state-of-the-art implementation, suggested originally by Bryan, artificially restricts its solution space. This restriction leads to a systematic error often unaccounted for in contemporary MEM studies. The goal of this paper is to carefully revisit Bryan’s train of thought, point out its flaw in applying linear algebra arguments to an inherently nonlinear problem, and suggest possible ways to overcome it.

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“…Analysis -Physically interpretable results are extracted from observable measurements. ML applications thus far include cross-observable regression [82,83], action parameter regression [67,84], and new methods for ill-posed inverse problems [85][86][87][88][89][90]. As discussed further in Sec.…”

Section: Introductionmentioning

confidence: 99%

Applications of Machine Learning to Lattice Quantum Field Theory

Boyda¹,

Calì²,

Foreman³

et al. 2022

Preprint

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There is great potential to apply machine learning in the area of numerical lattice quantum field theory, but full exploitation of that potential will require new strategies. In this white paper for the Snowmass community planning process, we discuss the unique requirements of machine learning for lattice quantum field theory research and outline what is needed to enable exploration and deployment of this approach in the future.

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News from bottomonium spectral functions in thermal QCD

Cited by 15 publications

References 8 publications

Electrical conductivity of the quark-gluon plasma: perspective from lattice QCD

Electrical conductivity of the quark-gluon plasma: perspective from lattice QCD

Bryan’s Maximum Entropy Method—Diagnosis of a Flawed Argument and Its Remedy

Applications of Machine Learning to Lattice Quantum Field Theory

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