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Description
Nearby stellar streams carry unique information on the dynamical evolution and disruption of stellar systems in the Galaxy, the mass distribution in the disk, and they provide unique targets for planet formation and evolution studies. Recently, Meingast 1, a 120{deg} stellar stream with a length of at least 400pc, was discovered. We aim to revisit the Meingast 1 stream to search for new members within its currently known 400pc extent, using Gaia DR2 data and an innovative machine learning approach. We used a bagging classifier of one-class support vector machines with Gaia DR2 data to perform a 5D search (positions and proper motions) for new stream members. The ensemble was created by randomly sampling 2.4 million hyper-parameter realizations admitting classifiers that fulfill a set of prior assumptions. We used the variable prediction frequency resulting from the multitude of classifiers to estimate a stream membership criterion, which we used to select high-fidelity sources. We used the HR diagram and the Cartesian velocity distribution as test and validation tools. We find about 2000 stream members with high fidelity, or about an order of magnitude more than previously known, unveiling the stream's population across the entire stellar mass spectrum, from B stars to M stars, including white dwarfs. We find that, apart from being slightly more metal poor, the HRD of the stream is indistinguishable from that of the Pleiades cluster. For the mass range at which we are mostly complete, ~0.2M_{sun}_<M<~4M_{sun}_, we find a normal IMF, allowing us to estimate the total mass of stream to be about 2000M_{sun}_, making this relatively young stream by far the most massive one known. In addition, we identify several white dwarfs as potential stream members. The nearby Meingast 1 stream, due to its richness, age, and distance, is a new fundamental laboratory for star and planet formation and evolution studies for the poorly studied and gravitationally unbound star formation mode. We also demonstrate that one-class support vector machines can be effectively used to unveil the full stellar populations of nearby stellar systems with Gaia data.
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