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Predicting stellar properties from spectra with neural networks

Published on:

7 November 2023

Primary Category:

Solar and Stellar Astrophysics

Paper Authors:

Sankalp Gilda

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Key Details

deep-REMAP neural network framework utilizes synthetic and observed stellar spectra

Multi-task learning enables simultaneous prediction of multiple stellar properties

Custom asymmetric loss function improves predictive accuracy

Framework shows superior performance over traditional spectral analysis methods

Has potential for automated characterization tasks with new stellar data

AI generated summary

Predicting stellar properties from spectra with neural networks

This paper introduces deep-REMAP, a neural network framework that leverages synthetic and observed stellar spectra to accurately predict key stellar properties like temperature, gravity, and metallicity. By using techniques like multi-task learning and a custom loss function, deep-REMAP shows superior performance compared to traditional methods. The results demonstrate this framework's potential for automated stellar characterization tasks, even when extending to new stellar libraries and properties.

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