text: 113,268,1313507579310551041,1152338625654226944,Megan Mansfield,"New paper on the arXiv today! Let's talk about eclipse mapping. <LINK> #scicomm <LINK> Before I even start, this was a group effort and it would never have happened without all my lovely colleagues: Everett Schlawin, Jake Lustig-Yaeger, Arthur Adams, @astronemly, Jacob Arcangeli, @cloudfreekat, Prashansa Gupta, Dylan Keating, @kevinbstevenson, Thomas Beatty. Crash course on eclipse mapping: by observing secondary eclipse ingress/egress, when the planet is slowly disappearing/appearing behind the star, we can construct a 2D map of the dayside of a planet. <LINK> With JWST, we can make ~spectroscopic~ eclipse mapping observations. And, since observing at different wavelengths = observing at different pressures/altitudes in a planet, this means 3D DAYSIDE MAPS <LINK> But there's degeneracies in the info we get from these maps, since we are actually just observing flux over time but we want to determine flux over latitude and longitude. How to do it? Enter the Eigenspectra Mapping Method <LINK> So there's a LOT of math in this paper, but the basic idea is we use a method developed by @astronemly (Rauscher+18) to make 2D maps, then stack them together to create a 3D spatial+spectral map. <LINK> We use a clustering algorithm to look for patterns on this 3D map. This lets us identify a few spectra that show all the variability across the map. So you can see how temperature/composition changes over the dayside, but only have to analyze a couple high-precision spectra. <LINK> So how does it work? Pretty well! We can identify large-scale features and flux gradients on dayside maps. <LINK> This method has 2 big applications: 1. To get a first look at eclipse mapping data and identify large-scale features without assuming your observations will match your GCM predictions. 2. To plan what precision of observations you need to observe certain features in your models. Finally, we want the whole JWST observer community to be able to use this tool! GitHub repo currently in development, with a planned release of a user-friendly version in the next couple of months. <LINK> PS I skipped a lot of the meat of this paper (bc who wants to read a tweet about PCA?) but if you want to know more just email/message me! @_astronomay Thanks, me too!",https://arxiv.org/abs/2010.02197,"Planetary atmospheres are inherently 3D objects that can have strong gradients in latitude, longitude, and altitude. Secondary eclipse mapping is a powerful way to map the 3D distribution of the atmosphere, but the data can have large correlations and errors in the presence of photon and instrument noise. We develop a technique to mitigate the large uncertainties of eclipse maps by identifying a small number of dominant spectra to make them more tractable for individual analysis via atmospheric retrieval. We use the eigencurves method to infer a multi-wavelength map of a planet from spectroscopic secondary eclipse light curves. We then apply a clustering algorithm to the planet map to identify several regions with similar emergent spectra. We combine the similar spectra together to construct an ""eigenspectrum"" for each distinct region on the planetary map. We demonstrate how this approach could be used to isolate hot from cold regions and/or regions with different chemical compositions in observations of hot Jupiters with the James Webb Space Telescope (JWST). We find that our method struggles to identify sharp edges in maps with sudden discontinuities, but generally can be used as a first step before a more physically motivated modeling approach to determine the primary features observed on the planet. ","Eigenspectra: A Framework for Identifying Spectra from 3D Eclipse