The subtle and unique imprint of dark matter substructure on extended arcs in strong lensing systems contains a wealth of information about the properties and distribution of dark matter on small scales and, consequently, about the underlying particle physics. However, teasing out this effect poses a significant challenge since the likelihood function for realistic simulations of population-level parameters is intractable. We apply recently-developed simulation-based inference techniques to the problem of substructure inference in galaxy-galaxy strong lenses. By leveraging additional information extracted from the simulator, neural networks are efficiently trained to estimate likelihood ratios associated with population-level parameters characterizing substructure. Through proof-of-principle application to simulated data, we show that these methods can provide an efficient and principled way to simultaneously analyze an ensemble of strong lenses, and can be used to mine the large sample of lensing images deliverable by near-future surveys for signatures of dark matter substructure.

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Amongst the intermediate mass pulsating stars known as δ Sct stars is a subset of high-amplitude and predominantly radial-mode pulsators known as high-amplitude δ Sct (HADS) stars. From more than 2000 δ Sct stars observed by the Kepler space mission, only two HADS stars were detected. We investigate the more perplexing of these two HADS stars, KIC 5950759. We study its variability using ground- and space-based photometry, determine its atmospheric parameters from spectroscopy and perform asteroseismic modelling to constrain its mass and evolutionary stage. From spectroscopy, we find that KIC 5950759 is a metal-poor star, which is in agreement with the inferred metallicity needed to reproduce its pulsation mode frequencies from non-adiabatic pulsation models. Furthermore, we combine ground-based WASP and Kepler space photometry, and measure a linear change in period of order $\dot{P}/P \simeq 10^{-6}$ yr−1 for both the fundamental and first overtone radial modes across a time base of several years, which is at least two orders of magnitude larger than predicted by evolution models, and is the largest measured period change in a δ Sct star to date. Our analysis indicates that KIC 5950759 is a metal-poor HADS star near the short-lived contraction phase and the terminal-age main sequence, with its sub-solar metallicity making it a candidate SX Phe star. KIC 5950759 is a unique object amongst the thousands of known δ Sct stars and warrants further study to ascertain why its pulsation modes are evolving remarkably faster than predicted by stellar evolution.

We present extensive empirical evidence showing that current Bayesian simulation-based inference algorithms are inadequate for the falsificationist methodology of scientific inquiry. Our results collected through months of experimental computations show that all benchmarked algorithms -(s)npe, (s)nre, snl and variants of abc -may produce overconfident posterior approximations, which makes them demonstrably unreliable and dangerous if one's scientific goal is to constrain parameters of interest. We believe that failing to address this issue will lead to a well-founded trust crisis in simulation-based inference. For this reason, we argue that research efforts should now consider theoretical and methodological developments of conservative approximate inference algorithms and present research directions towards this objective. In this regard, we show empirical evidence that ensembles are consistently more reliable.