Presentation #119.06 in the session “Evolved and Variable Stars”.
We present a new method for detecting variable astrophysical objects and transient phenomena using variability metrics defined upon Gaia DR2 and Zwicky Transient Facility (ZTF) DR3 photometry. First, we define a quick, global proxy for variability by employing only Gaia DR2 photometry, identifying objects with excess G-band flux uncertainty. To build a more robust variability detection procedure, we then rank objects by summing the Gaia metric, VG, with the ZTF variability metric, VZTF, defined by the presence of excess scatter in the ZTF g- and r-band fluxes, and then boost this sum by the number of ZTF alerts, NA. As a proof of concept, we applied this latter technique to a sample of 12,073 known and candidate white dwarf stars centered on the ZZ Ceti instability strip. Investigating the top 1% ranked by these metrics, 121 objects in all, we report the detection of 39 white dwarfs previously known to vary. For 32 of the remaining 82 candidate variables, we obtained high-speed follow-up photometry from the McDonald Observatory 2.1m Telescope confirming their variability, motivating follow-up for the final 50 candidates. With this method we detect variability from dozens of ZZ Cetis, along with cataclysmic variables, eclipsing binaries, and potential spot-modulated variables, underscoring our method’s broad sensitivity across timescales and amplitudes. Most significantly, our top 1% sample includes both currently known white dwarfs with transiting planetesimal debris and has identified several new objects with transit-like flux dips in their ZTF and McDonald light curves. Accordingly, our method may be used to exponentially grow the known population of these systems lying at the intersection of both planetary system and stellar evolution. Before adapting this method for the Vera C. Rubin Observatory’s Legacy Survey of Space and Time, our decontamination routine should be optimized to further guard against spurious detections and to correct magnitude biases.