When the team saw the Gaia DR3 data, however, they were disappointed to discover that the spectrometer readings were too broad to reveal individual chemical peaks. \u201cThe spectral information for about 200 million stars was released, but these are very low-resolution spectra. If you look at the spectrum it\u2019s just a bunch of wiggles,\u201d Chandra said.<\/p>\n

So the team turned to machine learning to extract the signals of heavier elements from the noisy, low-resolution spectra. They used an off-the-shelf algorithm called XGBoost, and trained it using high-quality spectral data from other surveys. With this training, the algorithm was able to reveal the stars\u2019 metallicity based solely on the low-quality Gaia wiggles. When the team double-checked their predictions against data collected by three other independent high-quality sky surveys in three unique sections of the Milky Way, they found tight agreement.<\/p>\n

Looking into the inner secrets of the algorithm, Chandra found that it decided a star\u2019s heavy-element abundance based almost exclusively on the star\u2019s calcium and magnesium absorption lines. It also corrected for potential sources of error, such as the dense tangles of cosmic dust and gas that lie between Earth and the center of the Milky Way. \u201cThe shape of those wiggles will change if there\u2019s a lot of dust in the line of sight to the star,\u201d he said. \u201cAnd that\u2019s important because we\u2019re studying the center of the galaxy, which is filled with dust.\u201d<\/p>\n