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titleComparisons with other Observatories


ThumbnailWorking NameDescriptionCreditFile TypeFile SizeFile ExtensionFilenameFile LocationSource Location



Comparison of Hubble, Webb, and Roman, including their unique strengths and synergies.Credit:  A. James (STScI)Image32.4 MBPNGRoman-Hubble-Webb_comparison

https://stsci.box.com/s/s2f0es38bw4kex10lhks2x58pubfo0k3

https://hubblesite.org/hubble-30th-anniversary/resources


Comparison of observatories in the 2020s and their windows into the electromagnetic spectrum.  



Credit: A. James (STScI)Image1.3 MBJPGmany_observatories_wavelength_graphhttps://stsci.box.com/s/1nbv554hcym2yjth9a5etnm9ebfzwqnq


NASA Astrophysics Fleet Mission ChartCredit: NASAimage3.5 MBPDFAstro Fleet2019-Feb21_TAGGEDhttps://stsci.box.com/s/ferl1fvl5q9j3nypo5t1lw17vcj4hm3ghttps://smd-prod.s3.amazonaws.com/science-pink/s3fs-public/atoms/files/Astro%20Fleet2019-Feb21_TAGGED.pdf


Comparison of Hubble and expected Rubin Observatory LSST data resolution. (L) BVz color image from the Hubble CANDELS field with θ = 0.1” and r ≈ 28.5. Roman will have comparable resolution. (R) Simulated Rubin LSST image made by degrading Hubble data to Rubin resolution of θ = 0.6”. In the Rubin image, the galaxy is blended with surrounding objects. Correlating overlapping Rubin and Roman imagery would make it possible to develop machine learning algorithms to deblend Rubin imagery that does not overlap with Roman.


Clean version.

Image from B.E. Robertson, et al. 2019, Nat Rev Phys, 1, 450



image2 MBPNGRoman_Deblending_clean

Box Live Link
urlhttps://stsci.box.com/s/6xeqaipjpgslru12lf9mrrmdelcns77k




Comparison of Hubble and expected Rubin Observatory LSST data resolution. (L) BVz color image from the Hubble CANDELS field with θ = 0.1” and r ≈ 28.5. Roman will have comparable resolution. (R) Simulated Rubin LSST image made by degrading Hubble data to Rubin resolution of θ = 0.6”. In the Rubin image, the galaxy is blended with surrounding objects. Correlating overlapping Rubin and Roman imagery would make it possible to develop machine learning algorithms to deblend Rubin imagery that does not overlap with Roman.


Annotated version.

Image from B.E. Robertson, et al. 2019, Nat Rev Phys, 1, 450image2 MBPNGRoman_Deblending_annotated

Box Live Link
urlhttps://stsci.box.com/s/l09w4eytnx41ej84jqrbzfxoiuogz28f