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A machine learning algorithm for direct detection of axion-like particle domain walls

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Title
A machine learning algorithm for direct detection of axion-like particle domain walls
Author(s)
Dongok Kim; Kimball, Derek F. Jackson; Masia-Roig, Hector; Smiga, Joseph A.; Wickenbrock, Arne; Budker, Dmitry; Younggeun Kim; Yunchang Shin; Yannis K. Semertzidis
Publication Date
2022-09
Journal
Physics of the Dark Universe, v.37
Publisher
Elsevier BV
Abstract
The Global Network of Optical Magnetometers for Exotic physics searches (GNOME) conducts an experimental search for certain forms of dark matter based on their spatiotemporal signatures imprinted on a global array of synchronized atomic magnetometers. The experiment described here looks for a gradient coupling of axion-like particles (ALPs) with proton spins as a signature of locally dense dark matter objects such as domain walls. In this work, stochastic optimization with machine learning is proposed for use in a search for ALP domain walls based on GNOME data. The validity and reliability of this method were verified using binary classification. The projected sensitivity of this new analysis method for ALP domain-wall crossing events is presented.
URI
https://pr.ibs.re.kr/handle/8788114/12844
DOI
10.1016/j.dark.2022.101118
ISSN
2212-6864
Appears in Collections:
Center for Axion and Precision Physics Research(액시온 및 극한상호작용 연구단) > 1. Journal Papers (저널논문)
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