1. LSST AGN TODO Items Gordon Richards (Drexel University)

2. Precursor Observations Lots of photometric quasar/AGN catalogs out there (2 more coming) with ~4x the number of objects as SDSS has spectroscopically confirmed. Lots of photometric quasar/AGN catalogs out there (2 more coming) with ~4x the number of objects as SDSS has spectroscopically confirmed. Might be good to sparsely target some of these as a function of L, z, color, etc. for guidance for classification from LSST photometry Might be good to sparsely target some of these as a function of L, z, color, etc. for guidance for classification from LSST photometry O/IR Spectroscopy in DDFs to enable early science (e.g. photometric RM) O/IR Spectroscopy in DDFs to enable early science (e.g. photometric RM) Pre-plan for future follow-up observations (TDEs, high-z, etc.) Pre-plan for future follow-up observations (TDEs, high-z, etc.)

3. Synergies with Other Facilities Going the other way – make sure to announce our schedule for the benefit of other facilities Going the other way – make sure to announce our schedule for the benefit of other facilities Build multi-wavelength databases for catalog matching Build multi-wavelength databases for catalog matching  DES/Pan-STARRS  WISE  VLA Develop SED band-merging algorithms Develop SED band-merging algorithms Build databases of existing spectroscopic data Build databases of existing spectroscopic data

4. LSST-targeted Simulations Overcome disconnect between DM and science groups Overcome disconnect between DM and science groups More complete simulations (including u-band) over a wider area More complete simulations (including u-band) over a wider area Simulation checking Simulation checking  Colors  Variability  Astrometry (DCR & PM)  Morphology  Star-galaxy separation

5. R&D of Techniques or Algorithms Photo-z Algorithm Development Photo-z Algorithm Development  Create/test new algorithms that work across the full luminosity range sampled AGN Selection AGN Selection  Develop new algorithms (e.g. color+var)  Test on sims  Test on DES and Pan-STARRS data

6. Photo-z’s for AGNs At high luminosity, empirical methods work best (using the emissions line features). At low luminosity, template fitting works best (using the 4000A break) Need to figure out how to decide which to use and/or how to transition between them. Assef et al. 2010

7. Multi-parameter Selection colors variability astrometry

8. Software Development Clustering/LF development Clustering/LF development  Create algorithms appropriate to data volume/type/format  Write papers based on simulations that show what errors will be

9. Database and Data Services Liaise with other working groups (e.g., identify objects with high probability classes from other groups) Liaise with other working groups (e.g., identify objects with high probability classes from other groups) Photo-z as PDFs Photo-z as PDFs

10. Other Stripe 82 Work Stripe 82 Work  Anything that can be done with the sims Deep Drilling Fields Deep Drilling Fields  How to use as truth tables Variability Characterization Variability Characterization  A/gamma vs. DRW  Finding vs. physics (observed vs. rest)  How/if to combine ugriz?