Saturday, December 15, 2018

The Data Revolution in the Night Sky

Coffee break at the Astroinformatics Conference. Participants from four continents and different fields of research discussed machine learning methods in astronomy. (Photo: HITS)

The HITS Astroinformatics research group helps astronomers to better analyze the rapidly growing data-sets with modern methods from computer science. The researchers apply artificial intelligence techniques – among others – and develop new methods that are used in major projects, such as LOFAR and SKA. In addition, the group organized this year’s international “Astroinformatics” conference, which for the first time took place in Germany.

Astroinformatics is a young discipline that owes its rise to computers: Digital hardware and software have revolutionized astronomy in the last twenty years. Thanks to new detectors and innovative telescopes, astronomers can now observe objects of unprecedented size and at high resolution.

“With these new technologies, the volume of data is increasing exponentially, but the number of astronomers analyzing the data is not,” says Dr. Kai Polsterer, leader of the Astroinformatics research group at the Heidelberg Institute for Theoretical Studies (HITS). “Methods from computer science can help us here.”

Five years ago, Polsterer came to Heidelberg when HITS established the first group in this new research field in Europe. Since then, the physicist and computer scientist and his team developed machine learning methods and tools to enable astronomers to work exploratively – change the searching in researching.

One example is the automatic estimation of redshifts. The redshift helps scientists to estimate how far a galaxy is away from Earth. Measuring these values directly requires a great technical and cost intensive effort. To solve this problem, HITS astronomers have developed a deep learning method that automatically extracts the redshift directly from the available data-sets.

Another digital tool is “PINK,” the centerpiece of an explorative method used to analyze large and complex structured datasets in order to better understand – for example – morphologies of galaxies in the radio wavelength regime. “A new version has just been released,” Kai Polsterer explains. “PINK” is now being used as an analysis tool by the precursors of the Square Kilometer Array (SKA) telescopes in Africa and Australia. Moreover, the method is also used in the Low Frequency Array (LOFAR), a supercomputer-driven radio telescope.

Machine learning has become increasingly popular in recent years. “Astronomers are still often lacking the required know-how,” states Polsterer. “That’s why we are helping our colleagues to better understand the topic via tutorials and other formats, such as ‘birds of a feather.’”

This training is most effective at conferences, such as the international “Astroinformatics” conference, which Polsterer and his group brought to Heidelberg this year and thus to Germany for the first time. Topics such as modern database systems, visualizations and augmented reality, artificial intelligence, and the reproducibility of research results were the focus of the most important event in this field (see also the conference talks on the HITS YouTube channel).

Kai Polsterer is currently working with his team on a new project – a collection of blue prints and tools. This project will allow astronomers to exploratively search through a wide variety of data and thus intuitively filter out what they are interested in. “We want to revolutionize access to data in archives. In addition to the typical queries of today, astronomers will soon be able to efficiently find the objects that are important to them.”


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