Terence Tao: The Cosmic Distance Ladder, UCLA
UCLA2 HN recommenders · recommended in 2 months, 2022-2025
203K views · 16 years ago · lecture · evergreen
Terence Tao's AMS Einstein Public Lecture at UCLA on the cosmic distance ladder, the chain of methods astronomers use to measure distances from the Earth to distant galaxies. It is aimed at a general audience.
HN recommends it as an accessible and elegant tour of how indirect reasoning builds up cosmic measurements, with one commenter calling it their favorite Tao work.
28:33Terence Tao on the cosmic distance ladder
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4 comments from Hacker News
Cool collab. Tao’s oft given talk about the cosmic distance ladder has always been my favorite thing he has done. ▶ this video
> Nobody had a clue In the 3rd century BC, Aristarchus calculated that the Sun was between 18 and 20 times farther away from the Earth than the Moon, and proposed the Heliocentric model as a result. The true value is instead approximately 400 times. But it's incredible given that he didn't have lenses, the value of Pi, and that the Geocentric model was considered correct until 1800 years after his death. link Nice video about the cosmic distance ladder by Terence Tao: ▶ this video
This very parallax effect. We know how far the Sun is from the Earth, and the Earth has moved twice this distance from January 1 to July 1. This in turn allows us to extrapolate distance to some of the nearest stars by their apparent motion relative to the other, "fixed" stars. link ▶ this video
My understanding of cosmology and astronomy is there is an inherent "ladder", nesting of assumptions, or indirect argument structure. Like, IF the Moon is a sphere, then by the shadows Earth leaves upon it during eclipses, Earth is a sphere. This reasoning came before Aristotle knew the Earth was sphere. ▶ this video This is still a way to do science. I'm willing to believe the experimenters dealt with the inherent novelty in a scientific way. What do you mean known celestial body?? We've only ever "been" to 2. We don't get to run all the experiments you'd like. As long as the process is self-correcting, it works. You have not shown this method to be uncorrecting. Better and better standards are used to strengthen these claims. And it is expected some degree of future corrections will occur. That's the nature of the beast.