adsabs/AstroCLIMB huggingface dataset:
Paper: Panchromatic Photometry of Low-redshift, Massive Galaxies Selected from SDSS Stripe 82
Authors: Yang A. Li, Luis C. Ho, Jinyi Shangguan, Ming-Yang Zhuang, Ruancun Li
Figure caption: The [O III]/Hβ vs. [N II]/Hα diagnostic diagram for objects with S/N > 2 in their optical emission-line fluxes. The extreme starburst boundary (solid line; Kewley et al. 2001) and the pure star formation line (dashed line; Kauffmann et al. 2003) classify sources into star-forming galaxies (blue), AGNs (red), and composite (star forming and AGN) systems.
Paper: Panchromatic Photometry of Low-redshift, Massive Galaxies Selected from SDSS Stripe 82
Authors: Yang A. Li, Luis C. Ho, Jinyi Shangguan, Ming-Yang Zhuang, Ruancun Li
Figure caption: Example uncertainty estimation for the aperture photometry. (a) The target is enclosed by the aperture represented by the blue ellipse. The cyan dashed annuli indicate the area used to estimate the background noise. The red dashed ellipses, which are required to overlap with each other less than 20%, mark the footprints that randomly sample the confusion noise. (b) The mask instead of the image is displayed. We require that the red ellipses contain <20% of the masked pixels.
Paper: Panchromatic Photometry of Low-redshift, Massive Galaxies Selected from SDSS Stripe 82
Authors: Yang A. Li, Luis C. Ho, Jinyi Shangguan, Ming-Yang Zhuang, Ruancun Li
Figure caption: Examples of SED fitting for (a) a star-forming galaxy, (b) a typical active galaxy with a moderate AGN fraction (f AGN = 7.1%), and (c) an active galaxy with a larger AGN fraction (f AGN = 26.1%). The observed data (open green circles) are compared with model photometry calculated from the model SED (black curve), which consists of an unattenuated stellar continuum (dashed blue curve), dust emission (dashed red curve), and AGN emission (dashed magenta curve). The nebular emission model is not shown for clarity. The ﹩{\chi }_{\nu }^{2}﹩ of the fit is shown in the upper-left corner. The lower panels give the relative residuals between the data and the model.