Convolutional Neural Networks and Stokes Response Functions

Autor:	Centeno, Rebecca, Flyer, Natasha, Mukherjee, Lipi, Egeland, Ricky, Casini, Roberto, Aleman, Tanausu del Pino, Rempel, Matthias
Rok vydání:	2021
Předmět:	Astrophysics - Instrumentation and Methods for Astrophysics Astrophysics - Solar and Stellar Astrophysics
Druh dokumentu:	Working Paper
DOI:	10.3847/1538-4357/ac402f
Popis:	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, this 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. Comment: Accepted for publication in ApJ
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2112.03802 Zobrazit plný text záznamu View this record from Arxiv