by In 2015, the same year a huge observatory on World having found proof of the 4D fabric of spacetime, scientists began to lie with a rather long thought: If there are intelligent aliens out there, could they try to make their own scientific megabytes? And if they did, can we get it? Really, we already?
Yes, I am being completely serious. It all starts with interesting paper titled “Planet Hunters IX. PEN 8462852 – where’s the flux?”
In this paper, a team of researchers presented their analysis of data obtained from NASA Kepler telescope. It belonged to a star that lives around 1,470 light year from where you are sitting designated PEN 8462852, or Star Boyajian as a nod to the study’s lead author. According to the team’s findings, Boyajian’s Star appeared to exhibit many very strange dips in light.
Normally, when studying a star from our vantage point in space, telescopes can naturally see dips in starlight whenever something crosses between them and the star itself. Imagine you are staring at a bright light bulb, then someone walks in front of the light bulb. Its emissions seem to be affected. Usually, as you would expect, on exoplanet orbiting its stellar host causes such attenuation – but … not for Boyajian’s Star.
“It’s not a sphere,” Daniel Giles, a postdoctoral researcher at the SETI The Institute said during the 243rd meeting of the American Astronomical Society in January. “It consists of something like a bunch of panels… it’s like what a megastructure would look like.”
Because of this, after the 2015 result, the crowd went wild. News articles, follow-up comments, opinion pieces and even general conversation began to flow through the astronomy niche. Okay, break. I’ll save you the trouble and let you know that the final consensus is: No, these weird cuts they did not cause it a giant piece of futuristic alien technology. “Probably dust,” said Giles. But here’s the thing.
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“Signals like this were really missed in the Kepler data,” Giles explained. In fact, the reason why the researchers behind the paper found the light dip anomaly was because citizen scientists accidentally spotted it while searching for something else.
Or as Giles puts it: “People weren’t looking.”
So that’s exactly what he and other researchers aim to do. Maybe, they think, the truth about aliens is just in the data — we just have to look for it. But, because Seriously appearance
List the machines
In short, Giles and his team intend to search for the confusing, mysterious, intriguing and starkly out-of-normal signals in data collected by NASA’s Transiting Exoplanet Survey Satellite, or TESS. They want to chase down drops of starlight that don’t have a defined shape, a defined depth or even a defined time frame. The cosmic outliers.
Strange dips like this can be seen through photometric curves, showing a brightness above time. “We’re counting photons,” Giles explained succinctly. The kicker, though, is exactly how the team wants to go about this anomaly-hunting quest: Machine learning.
The process is pretty much as follows.
The TESS data used in the study is based on the satellite’s view of different sectors of the sky. These sectors were looked at over 30 days at a time; during that scan, TESS took a picture of the observed area once every 30 minutes. This eventually led the team to around 60 million light curves ready for analysis, generated for stars brighter than 14 size. In the magnitude system, smaller numbers are brighter than larger numbers — an object of magnitude 0 is 100 times brighter than an object of magnitude 5, for example. A Full Moon it enters the negatives with a magnitude of around -12.6; the sun shines around magnitude -27. And so on.
The next step is to start mass organizing the light curves based on things like their shapes and periods. “We are processing 60 million different light curves so we need them to be cheap and easy to calculate,” said Giles. “We calculate these free metrics and then we run the anomaly detector on it, and this is a density-based anomaly detector – we find out what features are sticking out.”
Then, after reducing the data to a manageable size, the team manages to apply more granular techniques that usually take more computing power. The nitty gritty analyses, hard to do. “We make sure that the behavior is really there, and that it is astrophysical and not due to an instrumentation issue,” Giles said.
If something shows a recognizable pattern, well, it’s time to go back to the kill phase.
“At last, we go through by hand,” said Giles, “because there is nothing better at finding strange things than the human eye.”
To find a foreigner, you might need a person
To be quite honest, I was very happy to hear that a person can find strange things because no machine really can. I think it underlies our admittedly wild drive to try to find intelligent aliens. We are naturally curious I think, and somehow drawn back into patterns.
“There is a certain level where we can use ML methods,” Giles told Space.com, “but ultimately, we need to be able to understand why things are happening.”
Maybe it’s a pool full of even the most accurate data sets — a pool full of very accurate data sets — until someone starts parsing through it to make connections that aren’t programmed into a machine yet to be identified.
“For things like anomaly detection, there’s an extra trick,” said Giles. “There’s no absolute truth, so we can’t train something to find the weirdest stuff, or the most interesting stuff, because we don’t necessarily know what it’s going to be.”
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Even when it comes to standard robotics that aim to mimic human structure, it’s a limiting step for scientists in decoding the physical laws that dictate how we move. It is because of this that, as humans, we do not necessarily know how certain aspects of humanity work. They just work. A few years ago, for example, one team made a breakthrough in discovering how our fingerprints affect our grip. You know, when you wash slippery dishes, you instinctively realize how hard it is to hold the dishes so they don’t fall out of your hands? You’re unconsciously thinking about your fingerprints during that whole thing. But scientists literally had to do a new law in physics to convert that instinct into written truth.
ML – and artificial intelligence, for that matter – seem to have similar concerns, although both are technically trainable to find some solutions of their own. It’s hard to program a machine to find something we haven’t found before, because what would we say to look for it? It’s like how scientists support the James Webb Space Telescope as the invention that could answer some cosmic questions we never thought to ask.
“There are limits to what AI and ML can do for us, but there’s also a lot of opportunity as long as we understand what ML is doing specifically,” Giles said.
Lunch of reflection.
However, Giles says the team also wants to look for specific anomalies that are indeed coded. “We’ve put almost 2 million different artificial signals into a light curve that have no dip signatures that we know of, but still have artifacts so they’re still behaving,” he said.
Regarding the anomaly results so far? “None of these speak to us so far because they are megastructures,
“But they are certainly interesting.”
