"A pair of astronomers at the European Space Agency (ESA) developed a neural network that searches through space images for anomalies. The results were far beyond what human experts could have done. In two and a half days, it sifted through nearly 100 million image cutouts, discovering 1,400 anomalous objects. The creators of the AI model, David O'Ryan and Pablo Gómez, call it AnomalyMatch. The pair trained it on (and applied it to) the Hubble Legacy Archive, which houses tens of thousands of datasets from Hubble's 35-year history."
"After less than three days of scanning, AnomalyMatch returned a list of likely anomalies. It still requires human eyes at the end: Gómez and O'Ryan reviewed the candidates to confirm which were truly abnormal. Among the 1,400 anomalous objects the pair confirmed, more than 800 were previously undocumented. Most of the results showed galaxies merging or interacting, which can lead to odd shapes or long tails of stars and gas. Others were gravitational lenses. (That's where the gravity of a foreground galaxy bends spacetime so that the light from a background galaxy is warped into a circle or arc.) Other discoveries included planet-forming disks viewed edge-on, galaxies with huge clumps of stars and jellyfish galaxies. Adding a bit of mystery, there were even "several dozen objects that defied classification altogether.""
AnomalyMatch is a neural network trained on the Hubble Legacy Archive, which contains tens of thousands of datasets spanning Hubble's 35-year history. In two and a half days it scanned nearly 100 million image cutouts and produced 1,400 anomalous candidates, of which human reviewers confirmed over 800 previously undocumented objects. Most detections were interacting or merging galaxies, gravitational lenses, edge-on planet-forming disks, clumpy galaxies, and jellyfish galaxies. Several dozen detections defied existing classification and may represent unusual or novel phenomena requiring further study.
Read at Engadget
Unable to calculate read time
Collection
[
|
...
]