2019
DOI: 10.1103/physrevd.99.016005
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Regularization issues for a cold and dense quark matter model in β equilibrium

Abstract: A regularization scheme that explicitly separates vacuum contributions from medium effects is applied to a Nambu-Jona-Lasinio model with diquark interactions and in β equilibrium. We perform a comparison of this proposed scheme with the more traditional one, where no separation of vacuum and medium effects is done. Our results point to both qualitative and quantitative important differences between these two methods, in particular regarding the phase structure of the model in the cold and dense nuclear matter … Show more

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Cited by 7 publications
(6 citation statements)
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“…This scheme has been applied to the NJL model and successfully shows qualitative agreement with lattice simulations and more elaborated theories, as might be seen in Refs. [57,59,60].…”
Section: A Trsmentioning
confidence: 99%
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“…This scheme has been applied to the NJL model and successfully shows qualitative agreement with lattice simulations and more elaborated theories, as might be seen in Refs. [57,59,60].…”
Section: A Trsmentioning
confidence: 99%
“…µ I ∼ Λ [57,58]. On the other hand, the Medium Separation Scheme (MSS), coined in Refs [59,60], is based arXiv:1907.09880v2 [hep-ph] 7 Aug 2019 on a proper separation of medium effects from divergent integrals, originally having explicit medium dependence. This results in the disposal of all divergent integrals into the pure vacuum part i.e.…”
Section: Introductionmentioning
confidence: 99%
“…This scheme has been applied to the NJL model and successfully shows qualitative agreement with lattice simulations and more elaborated theories, as might be seen in Refs. [53,67,70,71]. Let us start for example from integral I ∆ of ∆ gap equation:…”
Section: A Regularizationmentioning
confidence: 99%
“…Since the steps to obtain these integrals for MSS are described in detail in previous references [53,70,71], here we will just show the final results of each of these integrals:…”
Section: A Regularizationmentioning
confidence: 99%
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