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Active Galaxy Science in the LSST Deep-Drilling Fields: Footprints, Cadence Requirements, and Total-Depth Requirements

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arxiv 1811.06542 v2 pith:QEPN3FF3 submitted 2018-11-15 astro-ph.IM astro-ph.GAastro-ph.HE

W.N. Brandt (Penn State) , Q. Ni , G. Yang , S.F. Anderson , R.J. Assef , A.J. Barth , F.E. Bauer , A. Bongiorno
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C.-T. Chen D. De Cicco S. Gezari C.J. Grier P.B. Hall S.F. Hoenig M. Lacy J. Li B. Luo M. Paolillo B.M. Peterson L.C. Popović G.T. Richards O. Shemmer Y. Shen M. Sun J.D. Timlin J.R. Trump F. Vito Z. Yu
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classification astro-ph.IMastro-ph.GAastro-ph.HE
keywords requirementscadencetotal-depthddfsdeep-drillingfieldsfootprintslsst
verification ladder T0 review T1 audit T2 compute T3 formal
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This white paper specifies the footprints, cadence requirements, and total-depth requirements needed to allow the most-successful AGN studies in the four currently selected LSST Deep-Drilling Fields (DDFs): ELAIS-S1, XMM-LSS, CDF-S, and COSMOS. The information provided on cadence and total-depth requirements will also likely be applicable to enabling effective AGN science in any additional DDFs that are chosen.

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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 21 citations worldwide. Full citation record

  1. AGN radiative feedback as the main regulator of [O III] outflow activity and obscuration in X-ray AGN

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    Combining three accretion disk states for binary massive black holes, the paper predicts a spectrum 'notch' and a mass-ratio evolution attractor at q~1e-3 for extreme-mass-ratio binaries.

  3. VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features

    astro-ph.GA 2026-07 conditional novelty 6.0 of 10

    VAR-PZnn combines ZTF g-band variability features with optical, MIR and NIR photometry in a mixture-density network to estimate AGN photometric redshifts with σ_NMAD=0.058 and an 8.2% outlier fraction on a 72,728-sour...

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