New Observations of Unusual Star Reveal A Remarkable Find
A very red star that’s been found in the distant constellation of Antlia has been observed by astronomers using the Atacama Large Millimeter/submillimeter Array (ALMA), and what they’ve discovered about it is rather exciting.
A very red star that’s been found in the distant constellation of Antlia has been observed by astronomers using the Atacama Large Millimeter/submillimeter Array (ALMA), and what they’ve discovered about it is rather exciting. The star in question is called U Antliae, and as well as varying in brightness from week to week, the fascinating phenomenon also appears to have a thin spherical shell around it.
U Antliae is a carbon star that went through a short period (a few hundred years) of rapid mass loss around 2700 years ago. As it went through this period, the shell material was ejected at high speeds and is what can be seen today. Upon closer examination of the shell, astronomers also discovered evidence of thin gas clouds.
Only through the use of the ALMA radio telescope could experts get a glimpse of this spectacular view and is the clearest observation to date that we’ve had of the U Antliae shell. It’s not just a single image either. The images that ALMA produces are three-dimensional with each individual part being observed at a different wavelength. Because of a phenomenon called the Doppler Effect, each part will show images of gas traveling at different speeds either towards or away from the person observing.
Another amazing feature of this shell is how symmetrically round it is. It’s also incredibly thin. By observing the shell’s different velocities, experts can get to understand the chemical composition of this cosmic bubble. In doing this, they’ll gain a much deeper understanding as to how these shells form by mass loss, and how early stars formed in the Universe. It will also help explain how galaxies came to evolve into what they are today.
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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