AI model's insight helps astronomers propose new theory for observing far-off worlds | ... - 0 views
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Machine learning models are increasingly augmenting human processes, either performing repetitious tasks faster or providing some systematic insight that helps put human knowledge in perspective.
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Astronomers at UC Berkeley were surprised to find both happen after modeling gravitational microlensing events, leading to a new unified theory for the phenomenon.
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Gravitational lensing occurs when light from far-off stars and other stellar objects bends around a nearer one directly between it and the observer, briefly giving a brighter — but distorted — view of the farther one.
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Ambiguities are often reconciled with other observed data, such as that we know by other means that the planet is too small to cause the scale of distortion seen.
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“The two previous theories of degeneracy deal with cases where the background star appears to pass close to the foreground star or the foreground planet. The AI algorithm showed us hundreds of examples from not only these two cases, but also situations where the star doesn’t pass close to either the star or planet and cannot be explained by either previous theory,” said Zhang in a Berkeley news release.
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But without the systematic and confident calculations of the AI, it’s likely the simplified, less correct theory would have persisted for many more years.
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As a result — and after some convincing, since a grad student questioning established doctrine is tolerated but perhaps not encouraged — they ended up proposing a new, “unified” theory of how degeneracy in these observations can be explained, of which the two known theories were simply the most common cases.
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“People were seeing these microlensing events, which actually were exhibiting this new degeneracy but just didn’t realize it. It was really just the machine learning looking at thousands of events where it became impossible to miss,” said Scott Gaudi
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But Zhang seemed convinced that the AI had clocked something that human observers had systematically overlooked.
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Just as people learned to trust calculators and later computers, we are learning to trust some AI models to output an interesting truth clear of preconceptions and assumptions — that is, if we haven’t just coded our own preconceptions and assumptions into them.