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We analyze 5108 AFGKM stars with at least five high-precision radial velocity points, as well as Gaia and Hipparcos astrometric data, utilizing a novel pipeline developed in previous work. We find 914 radial velocity signals with periods longer than 1000 days. Around these signals, 167 cold giants and 68 other types of companions are identified, through combined analyses of radial velocity, astrometry, and imaging data. Without correcting for detection bias, we estimate the minimum occurrence rate of the wide-orbit brown dwarfs to be 1.3%, and find a significant brown-dwarf valley around 40M_Jup. We also find a power-law distribution in the host binary fraction beyond 3 au, similar to that found for single stars, indicating no preference of multiplicity for brown dwarfs. Our work also reveals nine substellar systems (GJ 234 B, GJ 494 B, HD 13724 b, HD 182488 b, HD 39060 b and c, HD 4113 C, HD 42581 d, HD 7449 B, and HD 984 b) that have previously been directly imaged, and many others that are observable at existing facilities. Depending on their ages, we estimate that an additional 10–57 substellar objects within our sample can be detected with current imaging facilities, extending the imaged coldmore »(or old) giants by an order of magnitude.

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We introduce the idea of Citizen Scientist Amplification applying the method to data gathered from the top 10 contributing citizen scientists on the Supernova Hunters project. We take a novel approach to avail of the complementary strengths of deep learning and citizen science achieving results that are competitive with experts.

In 2017, the Muon Hunter project on the Zooniverse.org citizen science platform successfully gathered more than two million classification labels for nearly 140,000 camera images from VER- ITAS. The aim was to select and parameterize muon events for use in training convolutional neural networks. The success of this project proved that crowdsourcing labels for IACT image analy- sis is a viable avenue for further development of advanced machine-learning algorithms. These algorithms could potentially lend themselves to improving class separation between gamma-ray and hadronic event types. Nonetheless, it took two months to gather these labels from volun- teers, which could be a bottleneck for future applications of this method. Here we present Muon Hunters 2.0: the follow-on project that demonstrates the development of unsupervised clustering techniques to gather muon labels more efficiently from volunteer classifiers.