We study the fraction of stars in and around the δ Scuti instability strip that are pulsating, using Gaia DR2 parallaxes to derive precise luminosities. We classify a sample of over 15 000 Kepler A and F stars into δ Sct and non-δ Sct stars, paying close attention to variability that could have other origins. We find that 18 per cent of the δ Sct stars have their dominant frequency above the Kepler long-cadence Nyquist frequency (periods < 1 hr), and 30 per cent have some super-Nyquist variability. We analyse the pulsator fraction as a function of effective temperature and luminosity, finding that many stars in the δ Sct instability strip do not pulsate. The pulsator fraction peaks at just over 70 per cent in the middle of the instability strip. The results are insensitive to the amplitude threshold used to identify the pulsators. We define a new empirical instability strip based on the observed pulsator fraction that is systematically hotter than theoretical strips currently in use. The stellar temperatures, luminosities, and pulsation classifications are provided in an online catalogue.

PHOEBE 2 is a Python package for modeling the observables of eclipsing star systems, but until now has focused entirely on the forward-model -that is, generating a synthetic model given fixed values of a large number of parameters describing the system and the observations. The inverse problem, obtaining orbital and stellar parameters given observational data, is more complicated and computationally expensive as it requires generating a large set of forward-models to determine which set of parameters and uncertainties best represent the available observational data. The process of determining the best solution and also of obtaining reliable and robust uncertainties on those parameters often requires the use of multiple algorithms, including both optimizers and samplers. Furthermore, the forward-model of PHOEBE has been designed to be as physically robust as possible, but is computationally expensive compared to other codes. It is useful, therefore, to use whichever code is most efficient given the reasonable assumptions for a specific system, but learning the intricacies of multiple codes presents a barrier to doing this in practice. Here we present the 2.3 release of PHOEBE (publicly available from http://phoebe-project.org) which introduces a general framework for defining and handling distributions on parameters, and utilizing multiple different

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