Data Science Helps Astronomers Match Objects Across Surveys

Johns Hopkins University researchers develop innovative method for matching astronomical objects

In the vast expanse of the universe, astronomers have always faced the challenge of matching objects across different surveys. With each survey providing unique information, such as wavelength data, exposure times, and survey dates, it becomes crucial to correctly identify and match objects in order to conduct accurate scientific analysis. To tackle this problem, a group of researchers at Johns Hopkins University has turned to data science, developing a groundbreaking method that enables the matching of astronomical objects across multiple surveys.

The Challenge of Matching Astronomical Objects

Matching astronomical objects from different surveys has always been a complex task for space scientists. Consider the scenario of observing a distant galaxy, only to discover that another foreground galaxy appears in close proximity. When studying these objects across different surveys and wavelengths, it becomes challenging to differentiate between them. The accuracy of scientific analysis relies on correctly matching these objects.

Introducing the Data Science Solution

To address this challenge, Jacob Feitelberg, Amitabh Basu, and Tamás Budavári from Johns Hopkins University have developed a method that utilizes data science techniques. Their approach involves pairing objects from multiple surveys and determining the likelihood that recorded objects are indeed the same celestial object. By assigning a “score” to each observation pair, the researchers can measure the likelihood of their identity. This scoring system allows for efficient pairing across vast amounts of data and multiple surveys, enabling quick identification of matching objects.

Matching Objects Across Surveys

The effectiveness of the team’s method is evident in its capability to match objects from 100 different catalogs. This breakthrough means that researchers can now extract more knowledge from the same data by matching observations across time and telescopes. The ability to combine data from various surveys contributes to a deeper understanding of the cosmos, allowing scientists to build theories about the universe, from the smallest particles to the vast cosmos.

The Impact of Data Science in Astronomy

The application of data science techniques in astronomy has revolutionized the way researchers analyze and interpret astronomical data. With the vast amount of information available from surveys like the Sloan Digital Sky Survey, the Hubble Source Catalog, the Fermi Gamma-ray Space Telescope, and the Evolutionary Map of the Universe, data science methods provide a powerful tool for making sense of this wealth of data. The ability to match objects across surveys enhances the accuracy and reliability of scientific findings, enabling astronomers to uncover new insights into the workings of the universe.

Open Access to the Method

One of the remarkable aspects of this research is that the team’s code is publicly available. This open access approach allows other researchers to utilize and build upon their method, fostering collaboration and advancing the field of astronomy. By sharing their innovative technique, Feitelberg, Basu, and Budavári have contributed to the collective knowledge of the scientific community and paved the way for further discoveries in the realm of astronomy.


The marriage of data science and astronomy has yielded significant advancements in the field of astronomical research. The method developed by the researchers at Johns Hopkins University offers a solution to the challenge of matching astronomical objects across surveys. By utilizing data science techniques, scientists can now efficiently pair objects from multiple surveys, leading to a deeper understanding of the universe. This breakthrough underscores the importance of interdisciplinary collaboration and open access in advancing scientific knowledge. As we continue to explore the mysteries of the cosmos, the integration of data science in astronomy promises to unlock new frontiers of discovery.






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