Journal Article

GSI-2022-00884

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png A machine learning algorithm for direct detection of axion-like particle domain walls

Kim, D. ; Kimball, D. F. J. ; Masia-Roig, H.HIM* ; Smiga, J. A.HIM* ; Wickenbrock, A.GSI*HIM* ; Budker, D.HIM* ; Kim, Y. ; Shin, Y. C. E. (Corresponding author) ; Semertzidis, Y. K.

2022
Elsevier Amsterdam [u.a.]

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Please use a persistent id in citations: doi:10.1016/j.dark.2022.101118

Note: The authors thank to Vincent Dumont and Chris Pankow for early contribution to thedata analysis, and to all the members of GNOME collaboration for helpful insights anddiscussions.This work was supported by the Institute for Basic Science under grant No. IBS-R017-D1-2021-a00. The work of Derek F. Jackson Kimball was supported by the U.S. NationalScience Foundation under grant No. PHY-1707875 and PHY-2110388. The work of DmitryBudker was supported by the European Research Council under the European Union’s Horizon 2020 Research and Innovative Program under Grant agreement No. 695405, the Cluster of Excellence “Precision Physics, Fundamental Interactions, and Structure of Matter”(PRISMA+ EXC 2118), DFG Reinhart Koselleck (Project ID 390831469), Simons Foundation, and Heising-Simons Foundation.

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Contributing Institute(s):

1. HIM / MAM (MAS)

Research Program(s):

1. 612 - Cosmic Matter in the Laboratory (POF4-612) (POF4-612)
2. Dark-OsT - Experimental Searches for Oscillating and Transient effects from the Dark Sector (695405) (695405)

Experiment(s):

1. External experiment at external facility/ no experiment at GSI (POF3; other)

Appears in the scientific report 2022

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Database coverage:
Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; Essential Science Indicators ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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