The Euclid telescope, launched in 2023 by the European Space Agency, has finished overwhelming its first great task.
Designed to make a map in detail of the universe that helps us understand dark matter and energy, Euclid has been analyzing three regions of heaven from point L2 of Lagrange.
Despite being only the first mission data set, which will be extended at least until 2030, the European space telescope has already detected 26 million galaxies, some of which are 10,500 million light years.
Now the team, aided by volunteers and learning algorithms for reinforcement, has begun to publish the most complete and detailed map of the distribution of objects of the universe, which includes huge clusters of galaxies, bright quasars fed by supermassive black holes and gravitational lenses that divert the light of farthest objects.
A first look at the Cosmic Network
A deep field image of the Euclid space telescope
Galaxies are not randomly distributed. They form a structure called Cosmic Network, similar to a web, whose filaments are made of ordinary matter and dark matter. Dark matter does not emit light, but affects the way galaxies are formed and evolved.
X -ray telescopes have revealed an unimaginable object: a superstructure of more than one billion light years” width=”375″ height=”142″ src=”https://i.blogs.es/da8adc/1366_2000/375_142.jpeg”/>
Euclid accurately measures the shape, size and distance of galaxies to understand how the cosmic network is organized. The map that is making, whose first three pieces have just completed, will be key to find out what dark matter and dark energy are really.
We know that dark matter exists because it gravitationally affects galaxies (it turns them faster than expected). And we know that there is a dark energy responsible for accelerating the universe. But they are a mystery.
Different types of galaxies classified by human volunteers and the AI of ESA
Since its deployment, Euclid has sent 100 GB of data daily. It is impossible to manually classify each image, so scientists resorted to artificial intelligence and citizen science to classify 380,000 galaxies.
10,000 human volunteers collaborate at Galaxyzoo.org to teach a so -called Zooboot to identify the different forms of galaxies. The volunteers classify the objects (“spirals”, “with arms”) and their responses are used to reset the AI or readjust the accuracy of the algorithm.
5,000 strong gravitational lenses
Gravitational lenses detected by Euclid
One of the aspects that most excite scientists in the first Euclid data set are gravitational lenses. Euclid has already detected about 5,000 possible strong gravitational lenses, very rare objects that form evident visual effects, such as arches or Einstein rings.
These curvatures of space-time, predicted by the theory of general relativity, allow to observe distant objects that would otherwise be invisible, helping to understand how dark matter is distributed. It is expected that at the end of the mission, Euclid has identified about 100,000 strong lenses, multiplying the amount we currently know.
Questions to solve
The Euclid space telescope had a complicated deployment due to the accumulation of ice in its lens, but the ESA engineers managed to solve any setback. The results published today demonstrate the very high sensitivity of the telescope.
During the next few years, Euclid will observe between 30 and 50 times these regions of the cosmos, accumulating more and more information and discovering new galaxies and phenomena. In addition to the observations of the newly released NASA Spherex Telescope, the information we will have of the Universe will increase exponentially.
With the most detailed map of the universe, scientists will try to understand the nature of dark matter and dark energy, which make up 95% of the universe, and how they relate to each other, while solving other transcendental questions: what is the structure and history of the cosmic network? How has the expansion of the universe changed over time? Is the theory of the gravity of Einstein complete, or does it need large -scale modifications?
Images | THAT
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