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A stochastic approach to the reconstruction of spectral functions in lattice QCD

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arxiv 1510.02901 v1 pith:IPHRCQIT submitted 2015-10-10 hep-lat nucl-th

classification hep-latnucl-th
keywords approachfunctionsobtainedboxescharmoniummethodpeaksreconstruction
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abstract

We present a Stochastic Optimization Method (SOM) for the reconstruction of the spectral functions (SPFs) from Euclidean correlation functions. In this approach the SPF is parameterized as a sum of randomly distributed boxes. By varying the width, location and height of the boxes stochastically an optimal SPF can be obtained. Using this approach we reproduce mock SPFs fairly well, which contain sharp resonance peaks, transport peaks and continuum spectra. We also analyzed the charmonium correlators obtained from $N_{\tau}$=96, 48, 32 lattices using SOM and found similar conclusion on the dissociation temperatures of charmonium ground states as that obtained using the Maximum Entropy Method.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Discovering a well-conditioned analytic continuation problem via dictionary learning

    physics.comp-ph 2026-06 unverdicted novelty 7.0 of 10

    RSOM applies dictionary learning to discover a sparse dictionary that conditions the analytic continuation inverse problem, yielding competitive results on synthetic tests and finite-temperature electron gas QMC data.

  2. Second roton feature in the strongly coupled electron liquid

    physics.chem-ph 2025-05 conditional novelty 7.0 of 10

    A second roton-like dip in the spectrum of the strongly coupled uniform electron liquid appears at twice the roton wavenumber for r_s≥100.

  3. The noiseless limit and improved-prior limit of the maximum entropy method and their implications for the analytic continuation problem

    physics.comp-ph 2025-11 conditional novelty 6.0 of 10

    Maximum-entropy analytic continuation becomes linear, Bryan's algorithm becomes valid, and MSE scaling improves in the limit where the estimator sits close to the Bayesian prior.

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