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- W2100023696 abstract "Size estimation is a challenging area in the field of Ultra High Energy (UHE) showers where actual measurements are always associated with uncertainty of events and imperfections in detection mechanisms. The subtle variations resulting out of such factors incorporate certain random behaviour in the readings provided by shower detectors for subsequent processing. Field strength recorded by radio detectors may also be affected by this statistical nature. Hence there is a necessity of development of a system which can remain immune to such random behaviour and provide resilient readings to subsequent stages. Here, we propose a system based on Artificial Neural Network (ANN) which accepts radio field strength recorded by radio detectors and provides estimates of shower sizes in the UHE region. The ANN in feed-forward form is trained with a range of shower events with which it can effectively handle the randomness observed in the detector reading due to imperfections in the experimental apparatus and related set-up." @default.
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- W2100023696 date "2013-02-01" @default.
- W2100023696 modified "2023-09-25" @default.
- W2100023696 title "ANN based Estimation of Ultra High Energy (UHE) Shower Size using Radio Data" @default.
- W2100023696 cites W1981973336 @default.
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- W2100023696 doi "https://doi.org/10.1088/1742-6596/409/1/012058" @default.
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