The sky is really, really big. How do astronomers go from giant sky surveys to knowing what specific objects warrant further study? Are there automated processes to look for things with weird spectra or light curves or other stuff?
I remember volunteering online to look at stellar light curves for planetary transits, precisely because the task was hard to automate. I saw a couple of especially weird curves which I was able to flag for follow-up. Do y'all just sort data by hand?
@starkatt In the not so distant past one hired women (because they were cheaper to hire <grumble) to look at each plate. When society finally figured out that doing things that way was wrong (the pay and treatment of the women that is) astronomy switched to grad students, who you could still underpay. 1/?
@starkatt The problem is (other than the ethics of the situation) that as technology has advanced, our ability to collect data has far surpassed our ability to analyze it in anything close to a timely fashion. 2/?
@starkatt For example, prior to the late 1980s and early 1990s, most astronomical imaging in the optical used film. Though better than the human eye, film is horribly inefficient at collecting light, the best films capturing about 10% of the light that hits them. 3/?
@starkatt modern sensors (CCDs) are much more efficient. Even the inexpensive ones are at least 60% efficient at collecting light and the high end professional ones are upwards of 97% efficient.
This efficiency is important because we see fainter and fainter things by increasing the integration time (for optical this equates to the exposure time). 4/?
@starkatt So with film if it took 1 hour to get to a certain dimness, modern CCD sensors can do the job in 6-10 minutes. This means that you can collect data 6 to 10 times faster than you could before.
This causes the problem that by hand it will take 6-10 times as long or needs 6-10 times as many people. 5/?
@starkatt There are limits to automation however. It is relatively easy to teach a computer to find galaxies on a plate as they're extended objects not points like stars. It is much harder to use automation to classify those galaxies. So it's back to the grad students doing it by hand. However there is now such a huge amount of data available that there aren't enough grad students to do the work. 7/?
@starkatt Which is why astronomy is an early adopter of citizen science. It's relatively easy to train a lay person to classify a galaxy, for example. With the internet it's now also possible to have thousands of eyes on the project for little cost.
So computers are used to locate the galaxies on a plate, then the found galaxies are then the galaxies are sent to the trained public to classify them by hand. 8/?
@starkatt As an aside it is also important to remember that astronomers collect data for one specific thing that they are researching. There is a ton of information in that data that they're throwing away because it doesn't apply to their research.
This provides an opportunity for other astronomers to comb the same data for other information. 9/?
@starkatt as an example the Kepler space telescope was built and collected data with the goal of looking for planets around other stars. When I was in grad school a fellow student was using the same data to look for star spots.
Which is why I tell my students the most important thing as scientists we leave for future generations is not our discoveries, it's our data. 10/10
@evilscientistca thank you for this answer! I feel like I understand more fully now.
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