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Disentangling stellar atmospheric parameters in astronomical spectra using Generative Adversarial Neural Networks by Manteiga, Minia; Santoveña, Raúl; Álvarez, Marco A.; Dafonte, Carlos; Penedo, Manuel G.; Navarro, Silvana; Corral, Luis is a scholarly article available to read on EtoBox.

What is Disentangling stellar atmospheric parameters in astronomical spectra using Generative Adversarial Neural Networks about?

A method based on Generative Adversaria! Networks (GANs) is developed for disentangling the physical (effective temperature and gravity) and chemical (metallicity, overabundance of a-elements with respect to iron) atmospheric properties in astronomical spectra. Using a projection of the stellar spectra, commonly called latent space, in which the contribution dueto one or several main stellar physicochemical properties is minimised while others are enhanced, it was possible to maximise the information related to certain properties, which can then be extracted using artificial neural networks (ANN) as regressors with higher accuracy than a reference method based on the use of ANN trained with the original spectra. Methods. Our model utilises autoencoders, comprising two artificial neural networks: an encoder anda decoder which transform input data into a low-dimensional representation known as latent space. It also uses discriminators, which are additional neural networks aimed at transforming the traditional autoencoder training into an adversaria! approach, to disentangle or reinforce the astrophysical parameters from the latent space. The GANDALF tool is described. It was develope

Author

Manteiga, Minia; Santoveña, Raúl; Álvarez, Marco A.; Dafonte, Carlos; Penedo, Manuel G.; Navarro, Silvana; Corral, Luis

Published

2025

Language

EN