Abstract: Targeting Quasars for Cosmology: SDSS to BOSS to BigBOSS

Quasars are events that probe large volumes of the Universe and will thus be key cosmological tracers over the next few decades. Quasars are sparse events, however, and targeting them in large numbers is a difficult problem. This problem can be ameliorated by using sophisticated statistical techniques (neural nets, kernel density estimation, decision trees) and by constantly finding new, evolving sources of targeting information to improve survey returns. Such an approach has its own issues, however, some of which will seem familiar to high energy experimental physicists, e.g., the application of advanced statistical techniques to overwhelmingly large data sets, sometimes in the time domain. Most worryingly for some cosmological applications, an evolving approach to quasar selection imprints a complicated selection function on the survey that can be difficult to convincingly disentangle. In this talk I will discuss how the evolution of quasar targeting affected some cosmological measurements in the Sloan Digital Sky Survey, how an evolving approach approach to quasar targeting can be turned into a positive for certain cosmological measurements in BOSS and the prospects for targeting quasars in large numbers in a next generation survey beyond BOSS such as BigBOSS.