Neural Network Cleans Stellar Noise from the Search for Distant Worlds express
For six straight years, the HARPS-N telescope on La Palma island stared at the Sun as if it were a distant star — taking spectra, tracking brightness, and recording every quiver of spectral lines. All this was to understand how to distinguish a star's subtle sway from a genuine planetary signal.
A star doesn't sit still: its surface boils like a pot of soup. Hot bubbles rise, spots appear, and magnetic fields shift. All of this slightly displaces spectral lines, mimicking the gentle wobble caused by a planet. Previously, astronomers tried to subtract this 'stellar noise' manually, but it's too complex and irregular.
The neural network took over this task. It was trained on six years of solar data, with hints about which changes in brightness, magnetic field, and spectral line shapes are linked to surface boiling. It turned out that not all hints are equally useful. For instance, line tilt or the width of the sodium band barely help. But magnetic flux, overall brightness and its rate of change, and the shape of the hydrogen line are highly informative.
What remains is just under a meter per second, and this appears to be the fundamental limit set by giant convective cells on the Sun, each the size of entire continents. To catch an Earth-like planet, even this noise must be removed. So next time, the neural network will learn from even subtler traces of boiling.
A speed of 93 centimeters per second is slower than a garden snail's crawl (about 1.5 cm/s, but a snail can't sustain that pace for long, while a star 'trembles' continuously).
In the movie 'Interstellar', the heroes search for planets around a black hole, but even around a calm star like the Sun, its own noise hinders spotting Earth — now a neural network helps 'hear' the quiet signal.