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순수물리이론연구단
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Fingerprint matching of beyond-WIMP dark matter: neural network approach

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dc.contributor.authorKyu Jung Bae-
dc.contributor.authorRyusuke Jinno-
dc.contributor.authorAyuki Kamada-
dc.contributor.authorKeisuke Yanagi-
dc.date.accessioned2020-12-22T06:53:28Z-
dc.date.accessioned2020-12-22T06:53:28Z-
dc.date.available2020-12-22T06:53:28Z-
dc.date.available2020-12-22T06:53:28Z-
dc.date.created2020-06-12-
dc.date.issued2020-03-
dc.identifier.issn1475-7516-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/8711-
dc.description.abstract© 2020 IOP Publishing Ltd and Sissa Medialab. Improving observation of galactic-scale structure provide important clues to dark matter properties. While weakly interacting massive particles (WIMPs) provide cold dark matter on galactic scales, beyond-WIMP candidates suppress the galactic-scale structure formation. Nevertheless, directly constraining microscopic model parameters from observations involves an interdisciplinary and time-consuming procedure. In practice, some parametrizations of the linear matter power spectrum are introduced. The particle physics community calculates the linear matter power spectrum for a given model parameter set, while the astrophysics community places the constraint on the power spectrum parameters. If maps between the model parameters and the power spectrum parameters and maps between the power spectrum parameters and the likelihood (or observables) are shared among the two communities, they are very useful for both communities, e.g., making a constraint plot of the model parameter space. As suggested in the literature, however, it is necessary to introduce multiple parameters to precisely describe the linear matter power spectrum in a wide range of beyond-WIMP models. It challenges us to express and share the non-linear maps between multiple parameters. In this work, we propose utilizing the neural network technique to this end. The neural network technique is known to automatically express and efficiently share non-linear maps, although it is not as simple as analytic fitting formulas if available. To demonstrate how to work with a concrete example, we consider a simplified model of light feebly interacting massive particles and simple observables for galactic-scale structure. We also reveal the obtained neural networks through the arXiv website-
dc.description.uri1-
dc.language영어-
dc.publisherIOP PUBLISHING LTD-
dc.titleFingerprint matching of beyond-WIMP dark matter: neural network approach-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.wosid000528029100043-
dc.identifier.scopusid2-s2.0-85084212883-
dc.identifier.rimsid72186-
dc.contributor.affiliatedAuthorKyu Jung Bae-
dc.contributor.affiliatedAuthorRyusuke Jinno-
dc.contributor.affiliatedAuthorAyuki Kamada-
dc.identifier.doi10.1088/1475-7516/2020/03/042-
dc.identifier.bibliographicCitationJOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS, v.2020, no.3, pp.042-
dc.citation.titleJOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS-
dc.citation.volume2020-
dc.citation.number3-
dc.citation.startPage042-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordPlusLARGE-SCALE STRUCTURE-
dc.subject.keywordPlusMILKY-WAY SATELLITES-
dc.subject.keywordPlusLYMAN-ALPHA FOREST-
dc.subject.keywordPlusDWARF GALAXIES-
dc.subject.keywordPlusLAMBDA-CDM-
dc.subject.keywordPlusTOO BIG-
dc.subject.keywordPlusHYDRODYNAMICAL SIMULATIONS-
dc.subject.keywordPlusRADIATION INTERACTIONS-
dc.subject.keywordPlusDENSITY-FLUCTUATIONS-
dc.subject.keywordPlusSTERILE NEUTRINOS-
dc.subject.keywordAuthorcosmology of theories beyond the SM-
dc.subject.keywordAuthordark matter theory-
dc.subject.keywordAuthordwarfs galaxies-
dc.subject.keywordAuthorLyman alpha forest-
Appears in Collections:
Center for Fundamental Theory(순수물리이론 연구단) > 1. Journal Papers (저널논문)
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