edu.ucar.opensky::articles:25072 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-24T22:51:43.340672 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d7f47sn7 https://doi.org/10.5065/D6RX99HX https Related Service #1 Cheyenne: SGI ICE XA Cluster Convolutional neural networks and Stokes response functions 2022-02-01T00:00:00Z publication https://n2t.org/ark:/85065/d7f47sn7 https://doi.org/10.3847/1538-4357/ac402f Centeno, Rebecca author Flyer, N. author Mukherjee, Lipi author Egeland, Ricky author Casini, Roberto author del Pino Alemán, T. author Rempel, Matthias author UCAR/NCAR - Library publisher 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. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. None eng geoscientificInformation