The interstellar clouds of distant, ancient galaxies are often filled with carbon. This means, if astronomers can trace and detect these patches of carbon, which they call neutral carbon absorbers, they can learn quite a lot about how galaxies evolve.Yet, actually detecting neutral carbon absorbers — a process that typically involves finding the distinctive fingerprint of carbon’s absorption lines in the spectrum of light emitted by a galaxy — is tedious. It’s also very difficult. Across millions of galaxies, astronomers only know of a few dozen that contain these absorbers.Related: Machine learning could help track down alien technology. Here’s howSounds like a job for AI. Or, to be more precise, it sounds like a job for a deep neural network.Specifically, researchers recently set a neural network to work on spectroscopic data of galaxies taken more than a decade ago — and discovered more than a hundred new galaxies with neutral carbon absorbers.How did they do this? Well, before using a neural network, you first have to train it. Unfortunately, as we’ve discussed, there aren’t enough known neutral carbon absorbers to adequately do that. So, instead of using real data, the researchers generated a batch of 5 million fictitious spectra and used them to teach the neural network about what to look for: patterns often too subtle for a human eye to spot.Then, the researchers set their neural network loose on data from the Sloan Digital Sky Survey III. When they did so, they pinpointed neutral carbon absorbers in 107 galaxies previously …
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[mwai_chat context=”Let’s have a discussion about this article:nnThe interstellar clouds of distant, ancient galaxies are often filled with carbon. This means, if astronomers can trace and detect these patches of carbon, which they call neutral carbon absorbers, they can learn quite a lot about how galaxies evolve.Yet, actually detecting neutral carbon absorbers — a process that typically involves finding the distinctive fingerprint of carbon’s absorption lines in the spectrum of light emitted by a galaxy — is tedious. It’s also very difficult. Across millions of galaxies, astronomers only know of a few dozen that contain these absorbers.Related: Machine learning could help track down alien technology. Here’s howSounds like a job for AI. Or, to be more precise, it sounds like a job for a deep neural network.Specifically, researchers recently set a neural network to work on spectroscopic data of galaxies taken more than a decade ago — and discovered more than a hundred new galaxies with neutral carbon absorbers.How did they do this? Well, before using a neural network, you first have to train it. Unfortunately, as we’ve discussed, there aren’t enough known neutral carbon absorbers to adequately do that. So, instead of using real data, the researchers generated a batch of 5 million fictitious spectra and used them to teach the neural network about what to look for: patterns often too subtle for a human eye to spot.Then, the researchers set their neural network loose on data from the Sloan Digital Sky Survey III. When they did so, they pinpointed neutral carbon absorbers in 107 galaxies previously …nnDiscussion:nn” ai_name=”RocketNews AI: ” start_sentence=”Can I tell you more about this article?” text_input_placeholder=”Type ‘Yes'”]
