Posted on 04/24/2018 9:06:32 PM PDT by BenLurkin
A lot of attention has been dedicated to the machine learning technique known as “deep learning”, where computers are capable of discerning patterns in data without being specifically programmed to do so. In recent years, this technique has been applied to a number of applications, which include voice and facial recognition for social media platforms like Facebook.
However, astronomers are also benefiting from deep learning, which is helping them to analyze images of galaxies and understand how they form and evolve. In a new study, a team of international researchers used a deep learning algorithm to analyze images of galaxies from the Hubble Space Telescope. This method proved effective at classifying these galaxies based on what stage they were in their evolution.
The study, titled “Deep Learning Identifies High-z Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range“, recently appeared online and has been accepted for publication in the Astrophysical Journal. The study was led by Marc Huertes-Company of the University Paris Diderot and included members from the University of California Santa Cruz (UCSC), the Hebrew University, the Space Telescope Science Institute, the University of Pennsylvania Philadelphia, MINES ParisTech and Shanghai Normal University (SNHU).
In the past, Marc Huertas-Company has already applied deep learning methods to Hubble data for the sake of galaxy classification. In collaboration with David Koo and Joel Primack, both of whom are professor emeritus’ at UC Santa Cruz (and with support from Google), Huertas-Company and the team spent the past two summers developing a neural network that could identify galaxies at different stages in their evolution.
(Excerpt) Read more at universetoday.com ...
Can it recognize people who are voting twice?
Hey. You’ve seen one galaxy, you’ve seen them all. Joe Biden
Perfect
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