4.7 Article

The nature of X(3872) from high-multiplicity pp collisions

期刊

EUROPEAN PHYSICAL JOURNAL C
卷 81, 期 7, 页码 -

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SPRINGER
DOI: 10.1140/epjc/s10052-021-09425-w

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资金

  1. Swiss National Science Foundation [200020-169696]
  2. National Center of Competence in Research SwissMAP
  3. European Union's Horizon 2020 research and innovation programme under the Marie Skodowska-Curie [754496]
  4. Ministerio de Ciencia e Innovacion of Spain [FPA2017-83814-P]
  5. Unidad de Excelencia Maria de Maetzu under project [MDM-2016-0692]
  6. Xunta de Galicia (Conselleria de Educacion)
  7. FEDER
  8. YoctoLHC [ERC-2018-ADG-835105]

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The article discusses the structure of exotic resonances that do not trivially fit the usual quark model expectations, exploring ways to estimate their size by studying their behavior in QCD matter. The authors use the comover interaction model to study the yield of an exotic X(3872) and discuss the reliability of the model in high-multiplicity pp collisions by describing the suppression of excited over ground Upsilon states.
The structure of exotic resonances that do not trivially fit the usual quark model expectations has been a matter of intense scientific debate during the last two decades. A possible way of estimating the size of these states is to study their behavior when immersed in QCD matter. Recently, LHCb has measured the relative abundance of the exotic X(3872) over the ordinary psi (2S). We use the comover interaction model to study the yield of a compact X(3872). To confirm the reliability of the model in high-multiplicity pp collisions, we describe the suppression of excited over ground Upsilon states. With this at hand, we show that the size of the compact X(3872) would be slightly larger than that of the psi (2S). If the X(3872) is instead assumed to be a meson molecule of large size, we argue that its evolution in QCD matter should be described via a coalescence model, as suggested by data on deuteron production. We show that the predictions of this model for the X(3872) are in contrast with data.

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