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Planetary atmospheres are inherently 3D objects that can have stronggradients in latitude, longitude, and altitude. Secondary eclipse mapping is apowerful way to map the 3D distribution of the atmosphere, but the data canhave large correlations and errors in the presence of photon and instrumentnoise. We develop a technique to mitigate the large uncertainties of eclipsemaps by identifying a small number of dominant spectra to make them moretractable for individual analysis via atmospheric retrieval. We use theeigencurves method to infer a multi-wavelength map of a planet fromspectroscopic secondary eclipse light curves. We then apply a clusteringalgorithm to the planet map to identify several regions with similar emergentspectra. We combine the similar spectra together to construct an"eigenspectrum" for each distinct region on the planetary map. We demonstratehow this approach could be used to isolate hot from cold regions and/or regionswith different chemical compositions in observations of hot Jupiters with theJames Webb Space Telescope (JWST). We find that our method struggles toidentify sharp edges in maps with sudden discontinuities, but generally can beused as a first step before a more physically motivated modeling approach todetermine the primary features observed on the planet.

Eigenspectra: A Framework for Identifying Spectra from 3D Eclipse Mapping