Bibcode
Centeno, Rebecca; Flyer, Natasha; Mukherjee, Lipi; Egeland, Ricky; Casini, Roberto; del Pino Alemán, Tanausú; Rempel, Matthias
Bibliographical reference
The Astrophysical Journal
Advertised on:
2
2022
Journal
Citations
3
Refereed citations
2
Description
In this work, we study the information content learned by a convolutional neural network (CNN) when trained to carry out the inverse mapping between a database of synthetic Ca II intensity spectra and the vertical stratification of the temperature of the atmospheres used to generate such spectra. In particular, we evaluate the ability of the neural network to extract information about the sensitivity of the spectral line to temperature as a function of height. By training the CNN on sufficiently narrow wavelength intervals across the Ca II spectral profiles, we find that the error in the temperature prediction shows an inverse relationship to the response function of the spectral line to temperature, that is, different regions of the spectrum yield a better temperature prediction at their expected regions of formation. This work shows that the function that the CNN learns during the training process contains a physically meaningful mapping between wavelength and atmospheric height.
Related projects
Magnetism, Polarization and Radiative Transfer in Astrophysics
Magnetic fields pervade all astrophysical plasmas and govern most of the variability in the Universe at intermediate time scales. They are present in stars across the whole Hertzsprung-Russell diagram, in galaxies, and even perhaps in the intergalactic medium. Polarized light provides the most reliable source of information at our disposal for the
Tanausú del
Pino Alemán