The spirals interact on the galaxies, and much more vera c. Rubin is seen in this small part of a very large image of the girl cluster by the Observatory. Credit: NSF-D D Vera c. Rubin observatory
Professional astronomers did not search by looking through an episle with a backyard binoculars. Instead, they collect digital pictures in large -scale cameras associated with large binoculars.
Just as you could have an endless library of digital photographs stored in your cellphone, many astronomers collected more photos, as they would have time to see at any time. Instead, astronomers like me look at some images, then build algorithms and later use the computer to combine and analyze the rest.
But how can we know that the algorithm we write will work when we do not even have time to see all images? We can practice on some images, but a new way to create the best algorithm is to simulate some fake images as accurately as possible.
With fake images, we can customize the exact properties of objects in the image. In this way, we can see if the algorithm training we are training can correctly expose those qualities.
My research groups and colleagues have found that the best way to create fake but realistic astronomical images is to simulate light and everything that it faced by it. The light is made of particles called photons, and we can simulate each photon. We have written a publicly available code to do this called Photon Simulator, or Fosim.
The Fosim project aims to create realistic fake images that help us understand where the distortions in images from the actual binoculars. Fake images help us train programs that sort through images from real telescopes. And the results of studies using Fosim can also help astronomers correct distortions and defects in their actual telescopic images.
Data deluse
But first, why is there so much astronomy data in the first place? This is mainly due to the rise of surveys devoted binoculars. A survey telescope maps an area on the sky rather than pointing to specific objects only.
All of these observatory have a large collection area, a large area of the scene and a dedicated survey mode that collects more and more light as possible. Prominent surveys for the last two decades include SDSS, Kepler, Blanco-Decam, Subaru HSC, Tess, ZTF and Euclid.
Vera Rubin Observatory in Chile has recently terminated construction and will soon join them. Its survey begins immediately after its official “First Look” event on 23 June 2025. It will have a particularly strong set of survey capabilities.
The Rubin Observatory can see a region of the sky once that is several times larger than the full moon, and it can survey the entire southern celestial hemisphere at all nights.
A survey can practically highlight every subject in astronomy.
Some of the ambitious research questions include: measuring about dark matter and dark energy, maping the distribution of Milky Way stars, finding asteroids in the solar system, creating three dimensional maps of galaxies in the universe, finding new planets outside the solar system and tracking millions of items over time, which includes supernova.
All these surveys create a large -scale data deluse. They generate tens of terbites every night – these are billions to billions of pixels collected in millions of seconds. In the extreme case of the Rubin Observatory, if you spend the whole day after seeing images equal to the size of 4K television screen for a second day, you would be looking at them 25 times very slow and you will never keep them.
At this rate, no individual human can ever see all images. But automated programs can process data.
Astronomers did not survey only an astronomical object like a planet, galaxy or supernova, either. Often we measure the size, size, glow and position of the same object in many different ways in many different circumstances.
But more measurements come with more complications. For example, measures made in some weather conditions or on a part of the camera may disagree with others at different places or in different circumstances. Astronomers can cure these errors – called systematics – with carefully calibration or algorithm, but only if we understand the cause of inconsistency between different measurements. This is where Fosim comes. Once correct, we can use all images and measure more detailed measures.
Simulation: A photon at a time
To understand the origin of these systematics, we created a phosim, which can simulate the spread of the Earth’s atmosphere and then the telescope and light particles in the camera – photon.
https://www.youtube.com/watch?v=3pc8apembs
Phosim imitates the atmosphere, including air disturbance, as well as the size of the binocular mirrors and the electrical properties of the sensor. Photons are promoted using different types of physics that predict what photons do when they face mirrors and lenses of air and binoculars.
The simulation ends by collecting electrons, which is removed by a photon into the grid of the pixel to create an image.
Representing the light in the form of photon trillions is computationally efficient and is an application of the Monte Carlo method, which uses random samples. Researchers used Fosim to verify some aspects of the design of the Rubin Observatory and estimate how its images would look.
The results are complex, but till now we have directly visible in the temperature in the telescope mirrors – the angular staining – images. We have also studied how high-high disturbance occurs in the atmosphere that can disturb the light in the way of binoculars, which changes the position of stars and galaxies in the image and causes staining patterns correlated with air. We have displayed how the electric field in the telescope sensor – which intend to be vertical – can be distorted and taunt images.
Researchers can use these new results to fix their measurement and can better benefit from all the data that collects binoculars.
Traditionally, astronomical analysis is not concerned about this level of this level, but will have to measure carefully with current and future surveys. Astronomers can avail this holocaust of data using simulation to achieve deep levels of understanding.
John Peterson is the Associate Professor of Physics and Astronomy at the University of Purdeu. He does not work, consult or receive funding from any company or organization benefiting from this article, and has not revealed any relevant affiliation beyond their educational appointment. Purdue University provides funding as a member of the US.
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