The third Gaia data release (DR3) contains $\sim$170 000 astrometric orbit solutions of two-body systems located within $\sim$500 pc of the Sun. Determining component masses in these systems, in particular of stars hosting exoplanets, usually hinges on incorporating complementary observations in addition to the astrometry, e.g. spectroscopy and radial velocities. Several DR3 two-body systems with exoplanet, brown-dwarf, stellar, and black-hole components have been confirmed in this way. We developed an alternative machine learning approach that uses only the DR3 orbital solutions with the aim of identifying the best candidates for exoplanets and brown-dwarf companions. Based on confirmed substellar companions in the literature, we use semi-supervised anomaly detection methods in combination with extreme gradient boosting and random forest classifiers to determine likely low-mass outliers in the population of non-single sources. We employ and study feature importance to investigate the method's plausibility and produced a list of 22 best candidates of which four are exoplanet candidates and another five are either very-massive brown dwarfs or very-low mass stars. Three candidates, including one initial exoplanet candidate, correspond to false-positive solutions where longer-period binary star motion was fitted with a biased shorter-period orbit. We highlight nine candidates with brown-dwarf companions for preferential follow-up. One candidate companion around the Sun-like star G 15-6 could be confirmed as a genuine brown dwarf using external radial-velocity data. This new approach is a powerful complement to the traditional identification methods for substellar companions among Gaia astrometric orbits. It is particularly relevant in the context of Gaia DR4 and its expected exoplanet discovery yield.

According to various estimates, brown dwarfs (BD) should account for up to 25 percent of all objects in the Galaxy. However, few of them are discovered and well-studied, both individually and as a population. Homogeneous and complete samples of brown dwarfs are needed for these kinds of studies. Due to their weakness, spectral studies of brown dwarfs are rather laborious. For this reason, creating a significant reliable sample of brown dwarfs, confirmed by spectroscopic observations, seems unattainable at the moment. Numerous attempts have been made to search for and create a set of brown dwarfs using their colours as a decision rule applied to a vast amount of survey data. In this work, we use machine learning methods such as Random Forest Classifier, XGBoost, SVM Classifier and TabNet on PanStarrs DR1, 2MASS and WISE data to distinguish L and T brown dwarfs from objects of other spectral and luminosity classes. The explanation of the models is discussed. We also compare our models with classical decision rules, proving their efficiency and relevance.

Understanding differences between sub-stellar spectral data and models has proven to be a major challenge, especially for self-consistent model grids that are necessary for a thorough investigation of brown dwarf atmospheres. Using the supervised machine learning method of the random forest, we study the information content of 14 previously published model grids of brown dwarfs (from 1997 to 2021). The random forest method allows us to analyze the predictive power of these model grids, as well as interpret data within the framework of Approximate Bayesian Computation (ABC). Our curated dataset includes 3 benchmark brown dwarfs (Gl 570D, {\epsilon} Indi Ba and Bb) as well as a sample of 19 L and T dwarfs; this sample was previously analyzed in Lueber et al. (2022) using traditional Bayesian methods (nested sampling). We find that the effective temperature of a brown dwarf can be robustly predicted independent of the model grid chosen for the interpretation. However, inference of the surface gravity is model-dependent. Specifically, the BT-Settl, Sonora Bobcat and Sonora Cholla model grids tend to predict logg ~3-4 (cgs units) even after data blueward of 1.2 {\mu}m have been disregarded to mitigate for our incomplete knowledge of the shapes of alkali lines. Two major, longstanding challenges associated with understanding the influence of clouds in brown dwarf atmospheres remain: our inability to model them from first principles and also to robustly validate these models.

Dan Caselden was up late on November 3, 2018, playing the video game Counter-Strike, when he made astronomy history. Every time he died, he would jump on his laptop to check in on an automated search he was running of NASA space telescope images. "It was very confusing," said Caselden. "It was moving faster than anything I've discovered. It was faint and fast, which made it very weird."

'These cool worlds offer the opportunity for new insights into the formation and atmospheres of planets beyond the Solar System,' said paper author and astronomer Aaron Meisner of the National Science Foundation (NSF)'s NOIRLab. 'This collection of cool brown dwarfs also allows us to accurately estimate the number of free-floating worlds roaming interstellar space near the Sun.' Brown dwarves are the'cooling embers' of space -- to small to support the nuclear reactions that power stars, they are faint and challenging to spot, which is why astronomers have been hunting for them close by, in our galactic neighbourhood. Experts believe that brown dwarves cool as they age, starting at near-stellar temperatures but cooling until they are on a par with planets like Earth -- a hypothesis which the recent findings have provided evidence to support. The Backyard Worlds project recruited more than 100,000 citizen scientists to study trillions of pixels of telescope images looking for the subtle signs of planets and brown dwarves moving out in space. According to the astronomers, there is still no substitute for the human eye when it comes to scouring telescope images for subtle evidence of moving objects -- despite recent advances in machine learning and supercomputer hardware. The astronomical data studied was collected by the Nicholas U. Mayall 4-meter Telescope at the Kitt Peak National Observatory in Arizona and the Victor M. Blanco 4-meter Telescope at the Cerro Tololo Inter-American Observatory in Chile. Although the researchers have only published data on the coldest 95 of the finds, the volunteers have identified more than 1,500 brown dwarves in the astronomical data -- a record-breaker for any citizen science program by a factor of 20. 'It's awesome to know that our discoveries are now counted among the Sun's neighbour and will be targets of further research,' said paper co-author and citizen scientist Jim Walla added. The discoveries were part of'Backyard Worlds: Planet 9', a project which recruited more than 100,000 people to scour astronomical data for new'nearby' objects.

Artist's impression of one of this study's superlative discoveries, the oldest known wide-separation white dwarf plus cold brown dwarf pair. The small white orb represents the white dwarf (the remnant of a long-dead Sun-like star), while the brown/orange foreground object is the newly discovered brown dwarf companion. This faint brown dwarf was previously overlooked until it was spotted by citizen scientists because it lies right within the plane of the Milky Way. How complete is our census of the Sun's closest neighbors? Astronomers and a team of data-sleuthing volunteers participating in Backyard Worlds: Planet 9, a citizen science project, have discovered roughly 100 cool worlds near the Sun -- objects more massive than planets but lighter than stars, known as brown dwarfs.

Earlier this year, NASA launched a tool that would allow everyday citizens to join the search for alien worlds. Now, the space agency has confirmed there's already been a discovery. Observations with NASA's Infrared Telescope Facility in Hawaii have confirmed that an object spotted by four different users just days after the tool became available is, in fact, a'new cold world' just 100 light-years from the sun. The tool allows users to track moving objects in digital'flipbooks,' using observations from NASA's Wide Field Infrared Survey (WISE) spacecraft. To search for undiscovered worlds, visit the Backyard Worlds: Planet 9 website.