Inverse moments of univariate discrete distributions via the Poisson expansion

Audenaert, Koenraad M. R.

doi:10.48550/arxiv.0809.4155

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…2 This is demonstrated in the right panel of Fig. 2, where we show results of the Poisson-Charlier expansion for a probability distribution [10] at various orders. As discussed in more detail in [7], the Poisson-Charlier expansion of order n generates a probability distribution based on the first n factorial cumulants in such a way that it reproduces these first n factorial cumulants.…”

Section: Two-component Model As a Statistics Friendly Distributionmentioning

confidence: 69%

The QCD phase diagram and statistics friendly distributions

Koch,

Bzdak,

Oliinychenko

et al. 2020

Preprint

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The preliminary STAR data for proton cumulants for central collisions at √ s = 7.7 GeV are consistent with a twocomponent proton multiplicity distribution. We show that this two-component distribution is statistics friendly in that factorial cumulants of surprisingly high orders may be extracted with a relatively small number of events. As a consequence the two-component model can be tested and verified right now with the presently available STAR data from the first phase of the RHIC beam energy scan.

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Section: Two-component Model As a Statistics Friendly Distributionmentioning

confidence: 69%

The QCD phase diagram and statistics friendly distributions

Koch,

Bzdak,

Oliinychenko

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…While a distribution is in principle fully characterized by its moments or cumulants, a straightforward Taylor expansion requires knowledge of all moments or at least of a sufficiently large number of them to keep the truncation error small. More efficient schemes have however been proposed, for example the Poisson-Charlier expansion which approximates a given distribution on the basis of its factorial cumulants by a sum of forward difference operators applied to the Poisson distribution [40,98]. In Ref.…”

Section: Appendix B: Nlo Volume Fluctuation Correctionsmentioning

confidence: 99%

Proton number fluctuations in $\sqrt{s_{NN}}$ = 2.4 GeV Au+Au collisions studied with HADES

Adamczewski-Musch,

Arnold,

Behnke

et al. 2020

Preprint

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We present an analysis of proton number fluctuations in √ sNN = 2.4 GeV 197 Au+ 197 Au collisions measured with the High-Acceptance DiElectron Spectrometer (HADES) at GSI. With the help of extensive detector simulations done with IQMD transport model events including nuclear clusters, various nuisance effects influencing the observed proton cumulants have been investigated. Acceptance and efficiency corrections have been applied as a function of fine grained rapidity and transverse momentum bins, as well as considering local track density dependencies. Next, the effects of volume changes within particular centrality selections have been considered and beyond-leadingorder corrections have been applied to the data. The efficiency and volume corrected proton number moments and cumulants Kn of orders n = 1, . . . , 4 have been obtained as a function of centrality and phase-space bin, as well as the corresponding correlators Cn. We find that the observed correlators show a power-law scaling with the mean number of protons, i.e. Cn ∝ N n , indicative of mostly long-range multi-particle correlations in momentum space. We also present a comparison of our

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Inverse moments of univariate discrete distributions via the Poisson expansion

Cited by 2 publications

References 27 publications

The QCD phase diagram and statistics friendly distributions

The QCD phase diagram and statistics friendly distributions

Proton number fluctuations in $\sqrt{s_{NN}}$ = 2.4 GeV Au+Au collisions studied with HADES

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