https://doi.org/10.1140/epjc/s10052-024-13486-y
Regular Article - Theoretical Physics
ToMCCA: a Toy Monte Carlo Coalescence Afterburner
1
Physics Department, TUM School of Natural Sciences, Technical University of Munich, James-Franck-Straße 1, 85748, Garching b. München, Germany
2
European Organisation for Nuclear Research CERN, Geneva, Switzerland
Received:
24
April
2024
Accepted:
12
October
2024
Published online:
2
November
2024
Antinuclei in our Galaxy may stem either from annihilation or decay of dark matter, or from collisions of cosmic rays with the interstellar medium, which constitute the background of indirect dark matter searches. Understanding the formation mechanism of (anti)nuclei is crucial for setting limits on their production in space. Coalescence models, which describe the formation of light nuclei from final-state interaction of nucleons, have been widely employed in high-energy collisions. In this work, we introduce ToMCCA (Toy Monte Carlo Coalescence Afterburner), which allows for detailed studies of the nuclear formation processes without the overload of general-purpose event generators. ToMCCA contains parameterizations of the multiplicity dependence of the transverse momentum distributions of protons and of the baryon-emitting source size, extracted from ALICE measurements in pp collisions at TeV, as well as of the event multiplicity distributions, taken from the EPOS event generator. ToMCCA provides predictions of the deuteron transverse momentum distributions, with agreement of
with the experimental data. The results of ToMCCA show that the coalescence mechanism in pp collisions depends only on the event multiplicity, not on the collision system or its energy. This allows the model to be utilized for predictions at lower center-of-mass collision energies, which are the most relevant for the production of antinuclei from processes related to dark matter. This model can also be extended to heavier nuclei as long as the target nucleus wavefunction and its Wigner function are known.
© The Author(s) 2024
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Funded by SCOAP3.