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- W4387646124 abstract "In this work we used a convolutional neural network to determine the so called, large separation ( $$Delta nu $$ ), a pattern in the oscillation frequency that is a proxy for the stellar mean density, which is key to constraint the internal structure of stars, and thereby their evolution. We trained a CNN with a grid of asteroseismic models representative of intermediate-mass stars (from 1.5 to 3 times more massive than the Sun) and validated it with a sample of observed benchmark stars. We found that the CNN was around 56% accurate finding the actual values of $$Delta nu $$ , and 94% when considering multiples and/or submultiples of $$Delta nu $$ . This method will allow us to analyse massively thousands of A-F stars observed by past, present and future space missions such as CoRoT, Kepler/K2, TESS, and the upcoming PLATO mission." @default.
- W4387646124 created "2023-10-15" @default.
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- W4387646124 date "2023-01-01" @default.
- W4387646124 modified "2023-10-15" @default.
- W4387646124 title "A Convolutional Neural Network to Characterise the Internal Structure of Stars" @default.
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- W4387646124 doi "https://doi.org/10.1007/978-3-031-34167-0_20" @default.
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