Solar energy paper index
Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network
One-line summary
A solar energy research paper on Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network.
Engineering notes
Engineering notes will be added by the Power for Solar editorial team.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。
Original abstract
We assess the scope of a Convolutional Neural Network (CNN) in characterizing potential signals of two-component Dark Matter (DM) arising at the Large Hadron Collider (LHC) from mono-jet and mono-Z probes. We show that such a CNN has the ability of not only inferring the presence of two DM particles but also of extracting their mass and spin, the latter being either 0 or 1/2, following detector level analysis. However, such result represents a conceptual proof-of-concept, as we have not entertained a signal-to-background analysis.
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