Simulation and Analysis of the Properties of Linear Structures in the Mass Distribution of Nuclear Reaction Products by Machine Learning Methods
This paper is devoted to the analysis of manifestations of clustering in rare multibody decays of heavy nuclei. A computer model of the fine structure was developed jointly with the physicists of FLNR JINR; it was found based on experiments with the transuranium element Californium. To test the hypo...
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Veröffentlicht in: | Physics of particles and nuclei letters 2021-09, Vol.18 (5), p.559-569 |
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description | This paper is devoted to the analysis of manifestations of clustering in rare multibody decays of heavy nuclei. A computer model of the fine structure was developed jointly with the physicists of FLNR JINR; it was found based on experiments with the transuranium element Californium. To test the hypothesis that the structure really exists and is not a noise artifact, it was proposed to use a deep convolution network as a binary classifier trained on a large sample of model and noise images. Preliminary results of using the developed neuroclassifier show the prospects for this approach. |
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A. ; Pyatkov, Yu. V. ; Rudenko, M. O.</creator><creatorcontrib>Ososkov, G. A. ; Pyatkov, Yu. V. ; Rudenko, M. O.</creatorcontrib><description>This paper is devoted to the analysis of manifestations of clustering in rare multibody decays of heavy nuclei. A computer model of the fine structure was developed jointly with the physicists of FLNR JINR; it was found based on experiments with the transuranium element Californium. To test the hypothesis that the structure really exists and is not a noise artifact, it was proposed to use a deep convolution network as a binary classifier trained on a large sample of model and noise images. 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Russian Text © The Author(s), 2021, published in Pis’ma v Zhurnal Fizika Elementarnykh Chastits i Atomnogo Yadra, 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c268t-57372cb4ba8b2ec4c5ba6133a4fd9ef5a6e082928c83e2b6e20a5dfb6bf086da3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1134/S1547477121050083$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1134/S1547477121050083$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Ososkov, G. A.</creatorcontrib><creatorcontrib>Pyatkov, Yu. V.</creatorcontrib><creatorcontrib>Rudenko, M. O.</creatorcontrib><title>Simulation and Analysis of the Properties of Linear Structures in the Mass Distribution of Nuclear Reaction Products by Machine Learning Methods</title><title>Physics of particles and nuclei letters</title><addtitle>Phys. Part. Nuclei Lett</addtitle><description>This paper is devoted to the analysis of manifestations of clustering in rare multibody decays of heavy nuclei. A computer model of the fine structure was developed jointly with the physicists of FLNR JINR; it was found based on experiments with the transuranium element Californium. To test the hypothesis that the structure really exists and is not a noise artifact, it was proposed to use a deep convolution network as a binary classifier trained on a large sample of model and noise images. Preliminary results of using the developed neuroclassifier show the prospects for this approach.</description><subject>Californium</subject><subject>Clustering</subject><subject>Fine structure</subject><subject>Heavy nuclei</subject><subject>Machine learning</subject><subject>Mass distribution</subject><subject>Nuclear reactions</subject><subject>Particle and Nuclear Physics</subject><subject>Physicists</subject><subject>Physics</subject><subject>Physics and Astronomy</subject><subject>Physics of Elementary Particles and Atomic Nuclei. 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A computer model of the fine structure was developed jointly with the physicists of FLNR JINR; it was found based on experiments with the transuranium element Californium. To test the hypothesis that the structure really exists and is not a noise artifact, it was proposed to use a deep convolution network as a binary classifier trained on a large sample of model and noise images. Preliminary results of using the developed neuroclassifier show the prospects for this approach.</abstract><cop>Moscow</cop><pub>Pleiades Publishing</pub><doi>10.1134/S1547477121050083</doi><tpages>11</tpages></addata></record> |
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subjects | Californium Clustering Fine structure Heavy nuclei Machine learning Mass distribution Nuclear reactions Particle and Nuclear Physics Physicists Physics Physics and Astronomy Physics of Elementary Particles and Atomic Nuclei. Experiment Reaction products Transuranium elements |
title | Simulation and Analysis of the Properties of Linear Structures in the Mass Distribution of Nuclear Reaction Products by Machine Learning Methods |
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