Enhance Space Telescope Data Analysis with the Innovative Artifact InSPECtor Tool

How do scientists ensure the authenticity of the celestial observations made by telescopes located hundreds of thousands of miles away? A new initiative by NASA, known as Artifact InSPECtor, offers a unique opportunity for individuals to engage in this fascinating field while assisting missions such as Euclid and the forthcoming Nancy Grace Roman Space Telescope in addressing fundamental questions about our universe.

“It’s really cool that we can help teach computers new skills,” remarked nine-year-old Maeve F. after participating in Artifact InSPECtor. Individuals of all ages, including young explorers like Maeve, are encouraged to visit the project to learn how they can contribute to scientific advancement by training artificial intelligence to identify and rectify errors in telescope data.

The Role of Advanced Space Telescopes

The Euclid space telescope, a sophisticated observatory developed by the European Space Agency (ESA) with significant input from NASA, is currently capturing light from millions of galaxies scattered across the cosmos. Shortly, it will be joined by NASA’s Nancy Grace Roman Space Telescope, which will also gather data on a comparable number of galaxies, albeit from varying distances and densities within the sky. Collectively, these telescopes are poised to provide critical insights into the universe’s expansion and the enigmatic force known as dark energy, which drives this expansion.

To gather data that will unlock these mysteries, each telescope is equipped with a specialised instrument called a spectrograph. This device functions much like a prism, separating the light emitted by each galaxy, even those located at vast distances, into a spectrum of colours. By examining these spectral patterns—referred to as spectra—scientists can ascertain the distance of each galaxy, the types of stars it contains, and even details regarding the supermassive black holes situated at their cores.

The Challenge of Data Artifacts

Despite the advanced capabilities of these telescopes, a significant challenge remains to be addressed. Telescope data often harbours numerous “artifacts,” a term used to describe signals that originate from sources other than genuine astronomical objects like galaxies or stars. These artifacts can arise from various factors, including reflections from the telescope’s housing, cosmic rays impacting the detector, idiosyncrasies within the camera or electronics, or other external influences. It’s akin to the blurring that occurs when a smudge appears on your phone’s camera lens or when glare from the Sun obstructs a part of your photograph.

To identify and eliminate these artifacts, astronomers have developed artificial intelligence (AI) tools capable of learning to recognise them, much like how your smartphone detects faces in images. However, distinguishing artifacts within data from relatively new instruments poses a significant challenge for AI, which does not always achieve accurate recognition.

Get Involved with Artifact InSPECtor

This is where your contribution becomes invaluable. As a volunteer with Artifact InSPECtor, you will have the chance to examine genuine space telescope data from Euclid and, starting in early 2027, the Nancy Grace Roman Space Telescope. The project will equip you with the knowledge necessary to identify artifacts in the data collected by these telescopes. The insights you provide will subsequently enhance the guidelines that govern the AI tool. Together, you, the AI, the scientists, and these formidable space telescopes will deepen our understanding of the workings of our universe.

If you are eager to impart new skills to computers and assist in unveiling the secrets of dark energy, you can begin your journey by visiting Artifact InSPECtor today via your smartphone, tablet, or computer: https://go.nasa.gov/3Uyrguy.

Illustrative examples of potential artifacts in space telescope data are available. The blue areas highlight pixels that the AI model has identified as invalid. Volunteers participating in Artifact InSPECtor will learn to verify the accuracy of the AI’s assessments.

Credit: Image data from the ESA/Euclid Q1 Data release. Image processing by Aimee Schechter and Bharath C. Nagam.