We have reported on Gaia’s incredible data collection capabilities in the past. Recently, it released DR3, its latest dataset, containing over 1.8 billion objects. That’s a lot of data to sift through, and one of the most effective ways to do this is through machine learning. A group of researchers did just that by using a supervised learning algorithm to classify a particular type of object found in the dataset. The result is one of the world’s most comprehensive catalogs of the type of astronomical object known as variables.
By definition, variables change brightness over time. And Gaia, which has surveyed large parts of the sky for long periods of time, is uniquely adept at finding them. In fact, he found something on the order of 12.4 million variable sources, of which about 9 million were stars. The more than 3 million or so were either active galactic nuclei or galaxies themselves. All of these objects have had changes in their brightness at one time or another throughout the observation of Gaia.
Admittedly, 12.4 million out of 1.8 billion represent only about 0.6% of the total objects observed in DR3. However, this is still a lot of data to work with, and it could contain information that astronomers would like to understand about the causes of certain types of variability.
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According to the researchers, these causes lead to very different types of variability – 25 different types to be precise. Their article, published on arXiv, includes categories such as pulsation, eclipse, rotation, microlensing and cataclysmic. The latter sounds exciting, and there are 7,306 of them in the dataset, although the brightness of these events varies widely, even within individual categories.
To sort the 12.4 million objects into each of these categories, the researchers turned to one of the most useful algorithms for doing so: machine learning. In particular, they used a technique called “supervised classification”. Basically, that means they had human help, an AI algorithm, to identify characteristics of a certain classification, and then provided manual feedback on whether an object met the criteria for classification in that category.
Eventually, the algorithms could pick up the defining characteristics of the different categories and sort objects that humans had never looked at into those categories relatively accurately. The specific characteristics that define each category are also defined in the document. For example, cataclysmic variables have a higher probability level of variability than other objects in the dataset.
Many manual data massages made it into the final collection, though they were also discussed at length in the 105-page article. However, there were some fundamental issues with how Gaia observes objects that could rule out some potential variables in this collection. For example, Gaia doesn’t sample the whole sky all the time, so variables whose variability lasts less than a set duration can be missed if Gaia doesn’t look their way during changes. This is unlikely to be a large number of variables, but some are definitely missing from this data set.
What the dataset represents, however, is the world’s most comprehensive catalog of variable astronomical objects and the tools to do science with them. These types of data releases are precisely the kind of milestones that keep astronomy moving forward. And Gaia has even more to come, with DR4 on the way sometime after 2025. So astronomers will have plenty of time to dig into all the DR3 data in detail before the next massive data release.
Rimoldini et al – All-sky classification of 12.4 million variable sources into 25 classes
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Artist’s rendering of Gaia in the Milky Way.
Credit – ESA / ATG medialab, background image: ESO / S. Brunier
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