Most nearby young star clusters formed in three massive complexes.

Efforts to unveil the structure of the local interstellar medium and its recent star-formation history have spanned the past 70 years (refs. 1-6). Recent studies using precise data from space astrometry missions have revealed nearby, newly formed star clusters with connected origins7-12. Nonetheless, mapping young clusters across the entire sky back to their natal regions has been hindered by a lack of clusters with precise radial-velocity data. Here we show that 155 out of 272 (57%) high-quality young clusters13,14 within 1 kiloparsec of the Sun arise from three distinct spatial volumes. This conclusion is based on the analysis of data from the third Gaia release15 and other large-scale spectroscopic surveys. At present, dispersed throughout the solar neighbourhood, their past positions more than 30 million years ago reveal that these families of clusters each formed in one of three compact, massive star-forming complexes. One of these families includes all of the young clusters near the Sun-the Taurus and Scorpius-Centaurus star-forming complexes16,17. We estimate that more than 200 supernovae were produced from these families and argue that these clustered supernovae produced both the Local Bubble18 and the largest nearby supershell GSH 238+00+09 (ref. 19), both of which are clearly visible in modern three-dimensional dust maps20-22.

Uncover: Toward Interpretable Models for Detecting New Star Cluster Members.

In this design study, we present Uncover, an interactive tool aimed at astronomers to find previously unidentified member stars in stellar clusters. We contribute data and task abstraction in the domain of astronomy and provide an approach for the non-trivial challenge of finding a suitable hyper-parameter set for highly flexible novelty detection models. We achieve this by substituting the tedious manual trial and error process, which usually results in finding a small subset of passable models with a five-step workflow approach. We utilize ranges of a priori defined, interpretable summary statistics models have to adhere to. Our goal is to enable astronomers to use their domain expertise to quantify model goodness effectively. We attempt to change the current culture of blindly accepting a machine learning model to one where astronomers build and modify a model based on their expertise. We evaluate the tools' usability and usefulness in a series of interviews with domain experts.