--- title: "In the search for new worlds, Artificial Intelligence helps analyze TESS data • LAB-IA" description: "A milestone achieved by NASA’s space telescope highlights the importance of analyzing large volumes of astronomical data. In a study published in 2025, Lab-IA researchers identified an exoplanet candidate based on observations from the mission." canonical_url: "https://labia.cbpf.br/news/tess-descobre-planetas" locale: en meta: "og:description": "A milestone achieved by NASA’s space telescope highlights the importance of analyzing large volumes of astronomical data. In a study published in 2025, Lab-IA researchers identified an exoplanet candidate based on observations from the mission." "og:image": "https://labia.cbpf.br/_og/d/c_Model1.takumi%2Ctitle_~SW4gdGhlIHNlYXJjaCBmb3IgbmV3IHdvcmxkcywgQXJ0aWZpY2lhbCBJbnRlbGxpZ2VuY2UgaGVscHMgYW5hbHl6ZSBURVNTIGRhdGE%2Cp_Ii9uZXdzL3Rlc3MtZGVzY29icmUtcGxhbmV0YXMi%2Cs_L6mMkroRZXlnGRtI.png" "og:image:height": "600" "og:image:type": image/png "og:image:width": "1200" "og:title": "In the search for new worlds, Artificial Intelligence helps analyze TESS data • LAB-IA" "twitter:image": "https://labia.cbpf.br/_og/d/c_Model1.takumi%2Ctitle_~SW4gdGhlIHNlYXJjaCBmb3IgbmV3IHdvcmxkcywgQXJ0aWZpY2lhbCBJbnRlbGxpZ2VuY2UgaGVscHMgYW5hbHl6ZSBURVNTIGRhdGE%2Cp_Ii9uZXdzL3Rlc3MtZGVzY29icmUtcGxhbmV0YXMi%2Cs_L6mMkroRZXlnGRtI.png" --- > Index of every page on this site: [llms.txt](https://labia.cbpf.br/llms.txt) **Science and Technology****November 06, 2026** # **In the search for new worlds, Artificial Intelligence helps analyze TESS data** A milestone achieved by NASA’s space telescope highlights the importance of analyzing large volumes of astronomical data. In a study published in 2025, Lab-IA researchers identified an exoplanet candidate based on observations from the mission. ![In the search for new worlds, Artificial Intelligence helps analyze TESS data](http://127.0.0.1:8080/news/files/images-2-1793994975682.webp) NASA’s Transiting Exoplanet Survey Satellite ([TESS](https://science.nasa.gov/mission/tess/)) has surpassed the milestone of 1,000 confirmed exoplanets. According to data released by the mission’s Science Support Center on October 8, 2026, [the observatory has already contributed to the confirmation of 1,008 planets beyond our Solar System](https://heasarc.gsfc.nasa.gov/docs/tess/tess-reaches-1000-confirmed-exoplanets.html). The search, however, is far from over: more than 8,000 candidates are still awaiting confirmation. Mapa de todo o céu mostrando os 1.008 exoplanetas confirmados descobertos pelo TESS (pontos rosas). A imagem de fundo foi criada com base nas observações dos primeiros 96 setores do TESS. As regiões pretas dentro da área oval representam partes do céu que ainda não foram observadas pelo telescópio. Crédito da imagem: NASA/MIT/TESS e Nicole Schanche (UMD/NASA GSFC). Launched in 2018, TESS monitors large regions of the sky, searching for variations in the brightness of stars that may indicate the presence of orbiting planets. Over the years, the mission has expanded our understanding of the diversity of planetary systems and generated a vast amount of data that continues to be analyzed by researchers around the world. Turning these records into discoveries requires tools capable of identifying promising signals and methods for investigating their origins. Artificial intelligence and computational analysis have been contributing to astronomical research in this area. One example is the work carried out by researchers at the Laboratory of Artificial Intelligence for Physics (Lab-IA), at the Brazilian Center for Research in Physics (CBPF), which used TESS data to identify an exoplanet candidate. # How Do Telescopes Identify Distant Planets? One of the main techniques used by TESS is the planetary transit method. To understand how it works, it is important to start with photometry, a technique that measures the amount of light received from an astronomical object. These measurements make it possible to create graphs called light curves, which show how a star’s brightness changes over time. When a celestial body passes between the star and the observer, part of the light emitted by the star is blocked, causing a slight decrease in its observed brightness. When this phenomenon occurs as a planet passes in front of its host star, it is called a planetary transit. As the planet follows its orbit, these small decreases in brightness tend to repeat at regular intervals, and it is precisely this repetition that can indicate the presence of a planet. Light curves also contain information about the observed object. By analyzing the duration, frequency, and depth of these brightness dips, researchers can estimate characteristics such as the planet’s size, orbital period, and aspects of its orbit. The challenge is that not every decrease in a star’s brightness is caused by a planet. Eclipsing binary stars, nearby light sources, and instrumental effects can produce similar signals. Therefore, identifying a possible planetary transit is only the first step. Researchers must also investigate alternative explanations and assess whether the data are truly consistent with the presence of an exoplanet. # A Hot Jupiter Candidate Identified by Lab-IA Published in 2025 in the journal Research Notes of the American Astronomical Society, the study “ [TIC 318249571.01: A New Hot-Jupiter Transiting Planet Candidate from TESS Data](https://iopscience.iop.org/article/10.3847/2515-5172/ae2de9)” presents an analysis of a potential planet orbiting the star TIC 318249571. The object, identified as TIC 318249571.01, exhibits characteristics consistent with the class of planets known as hot Jupiters: gas giants that orbit very close to their host stars. According to the estimates presented in the publication, it has a radius approximately 1.43 times that of Jupiter and completes an orbit in about 2.8 days. Its host star is located approximately 430 light-years from Earth. O alvo TIC 318249571 foi observado nos setores 12, 38 e 65 do TESS, e suas curvas de luz apresentaram reduções de brilho compatíveis com trânsitos planetários. Curva de luz obtida a partir das imagens de campo completo (FFIs) do setor 38, processadas pelo pipeline do Science Processing Operations Center e baixadas do arquivo MAST. The research was supported by FAPERJ and FAPESP and involved the collaboration of NASA scientists. It was carried out with the aid of a neural network-based system, which identified the star as a promising target for further investigation. Based on this result, the researchers analyzed TESS observations to assess the origin of the signal and determine whether it could be explained by other phenomena. The team examined data from different observation periods of the mission, including the star’s light curves, and conducted tests to investigate possible sources of interference. These procedures included checking for shifts in the centroid of the observed source and comparing odd and even transits. These steps help determine whether the variation in brightness is consistent with a planet passing in front of its host star or may have another origin, such as an eclipsing binary star. The researchers also used the Markov Chain Monte Carlo (MCMC) method, a statistical technique that makes it possible to explore a model’s parameters and estimate the characteristics of the planetary system. The results strengthened the interpretation that the observed signal has a planetary origin, but the object remains classified as a candidate, and its confirmation depends on additional observations. # Artificial Intelligence and Large-Scale Data Analysis The study is an example of how different tools can work together in astronomical research. Neural networks help identify promising signals in large datasets of observations, while statistical methods and astrophysical analyses make it possible to investigate these signals in greater depth. This integration becomes particularly relevant given the growing volume of data produced by missions such as TESS, which has already contributed to the confirmation of more than 1,000 exoplanets, while thousands of other candidates remain under investigation. Each of them may require specific analyses and additional observations to determine its nature. The Lab-IA research also benefited from the computational infrastructure of Sci-Mind, the laboratory’s supercomputer designed to support scientific projects that require high-performance computing. # The Search for New Worlds Continues The milestone of 1,000 confirmed exoplanets represents the result of years of observations and the work of researchers investigating planetary systems beyond our own. According to NASA, the planets identified by TESS exhibit a wide diversity of sizes and orbital periods, expanding the possibilities for studying how these systems form and evolve. The mission remains operational and continues to identify new candidates. Meanwhile, the development of computational methods capable of analyzing large volumes of data plays an important role in turning distant signals into knowledge about the Universe. ```json {"@context":"https://schema.org","@graph":[{"@id":"https://labia.cbpf.br/#website","@type":"WebSite","description":"LAB-IA drives research by combining AI with physics, developing technologies for data analysis.","inLanguage":"en-US","name":"LAB-IA","url":"https://labia.cbpf.br/","publisher":{"@id":"https://labia.cbpf.br/#identity"},"workTranslation":{"@id":"https://labia.cbpf.br/pt#website"}},{"@id":"https://labia.cbpf.br/news/tess-descobre-planetas#webpage","@type":"WebPage","description":"A milestone achieved by NASA’s space telescope highlights the importance of analyzing large volumes of astronomical data. 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