Poisson-type multiplicity distribution and the Giovannini-Van Hove model

Hoang, T. F.; Cork, B.

doi:10.1007/bf01579149

Cited by 10 publications

(9 citation statements)

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“…(d) Coefficients C j emerging from the CMS data used in panel (a) compared with the NB and 2-NBD fits shown there and with the C j obtained from the MNB with modifications proposed in [9] and shown in panel (c). by some other forms of P(N) [1,[5][6][7][8]. However, as seen in Fig.…”

Section: Introductionmentioning

confidence: 86%

Intriguing feature of multiplicity distributions

2019

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Multiplicity distributions, P(N), provide valuable information on the mechanism of the production process. We argue that the observed P(N) contain more information (located in the small N region) than expected and used so far. We demonstrate that it can be retrieved by analysing specific combinations of the experimentally measured values of P(N) which we call modified combinants, C j , and which show distinct oscillatory behavior, not observed in the usual phenomenological forms of the P(N) used to fit data. We discuss the possible sources of these oscillations and their impact on our understanding of the multiparticle production mechanism. ⋆

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Section: Introductionmentioning

confidence: 86%

Intriguing feature of multiplicity distributions

2019

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“…Usually the first choice of P (N ) in fitting data is a single NBD [6]. However, with growing energy and number of produced secondaries the NBD increasingly deviates from data for large N (see [4]) and is replaced either by combinations of two [7,8], three [9], or multi-component NBDs [10], or by some other form of P (N ) [1,6,[11][12][13]. However, such a procedure only improves the agreement at large N , whereas the ratio R = data/f it deviates dramatically from unity at small N for all fits [4,5].…”

Section: Modified Combinants Combinants and Void Probabilitiesmentioning

confidence: 99%

Intriguing properties of multiplicity distributions

2019

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“…The simplest nontrivial choice is given by a linear relation to multiplicity N, and β = α/k). Usually, when searching for the best MD to fit the experimental data, one modifies accordingly the function g(N) (for example, by introducing higher order terms [32] or by using its more involved forms [33,34]).…”

Section: Introductionmentioning

confidence: 99%

How to retrieve additional information from the multiplicity distributions

Wilk,

Włodarczyk

2016

Preprint

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Multiplicity distributions P (N ) measured in multiparticle production processes are most frequently described by the Negative Binomial Distribution (NBD). However, with increasing collision energy some systematic discrepancies have become more and more apparent. They are usually attributed to the possible multi-source structure of the production process and described using a multi-NBD form of the multiplicity distribution. We investigate the possibility of keeping a single NBD but with its parameters depending on the multiplicity N . This is done by modifying the widely known clan model of particle production leading to the NBD form of P (N ). This is then confronted with the approach based on the so-called cascadestochastic formalism which is based on different types of recurrence relations defining P (N ). We demonstrate that a combination of both approaches allows the retrieval of additional valuable information from the multiplicity distributions, namely the oscillatory behavior of the counting statistics apparently visible in the high energy data.

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Poisson-type multiplicity distribution and the Giovannini-Van Hove model

Cited by 10 publications

References 10 publications

Intriguing feature of multiplicity distributions

Intriguing feature of multiplicity distributions

Intriguing properties of multiplicity distributions

How to retrieve additional information from the multiplicity distributions

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