Press Release: Astronomers Turn to the Brightest Entities in the Universe to Understand the Secrets of the Darkest Entities

Elana Farrell

Many are familiar with supermassive black holes, but few have heard of Active Galactic Nuclei (AGN). A black hole is a region of space where gravity is so strong that nothing, not even light, can escape. Supermassive black holes are the largest of black holes and are believed to lie at the center of all large galaxies including our Milky Way. An active galactic nucleus only occurs when a supermassive black hole is actively feeding on material. When dust and gas orbit just beyond the edge — known as an event horizon — of a supermassive black hole, the particles collide and release energy in the form of heat and light, forming a bright halo called an accretion disc. These particles heat up to millions of degrees and some are ejected along strong magnetic fields into two jets that shoot along the axis of the supermassive black hole for hundreds of thousands of lightyears. The central region of the galaxy can be more luminous than all the stars in the galaxy combined and this is an AGN. The most luminous AGN to date shines as brightly as 600 trillion suns. 

Beyond claiming the title as the brightest entity in the universe, AGNs are incredibly helpful in unlocking the secrets of supermassive black holes and cosmic evolution. We can’t technically see a supermassive black hole because they don’t emit any light. We can, however, detect their presence and understand them by studying their effect on matter nearby that we can see. AGNs can be seen at radio frequencies, so astronomers can study their radio emission to learn about supermassive black holes. 

In an article published by The Journal of Young Investigators on April 1st, Kamalpreet Kaur and Anil Ramdas Bari discuss theories of supermassive black hole formation, methods of detecting and weighing supermassive black holes, and how supermassive black holes can be located via the Citizen Science Project ‘Radio Galaxy Zoo: LOFAR’ presented by Zooniverse. The authors emphasize the importance of studying supermassive black holes because they are the control mechanisms and engines of cosmic change. 

LOFAR is an international network of telescopes used to observe and map the universe at radio frequencies in unprecedented detail. The LOFAR radio survey of the northern sky has captured hundreds of thousands of radio sources. Most of these radio sources are AGN. Astronomers rely on a sophisticated automatic “source finder” computer program to identify radio sources. The problem is that sometimes the computer program mistakenly separates constituent parts of one radio source into unrelated radio sources. There is no algorithm as good as the human eye at associating the components that the computer program mistakenly separated. Given that LOFAR generates vast amounts of data, astronomers need the help of citizen scientists to identify the sources in the most complex radio data images. This will prepare the data for further research by astronomers. 

After gaining a thorough understanding of the methodology of the project, Kaur and Bari created a decision tree based on the classifications they made as volunteers of the project. The goal of this decision tree is to help volunteers more easily identify the optical counterpart, or single source in the image that seems to be generating the radio emissions. The first categorization that must be made when classifying a radio source for the LOFAR project is made according to the symmetry of the radio sources. Once the source has been identified as “Symmetrical,” “Asymmetrical,” or a “Special-Case,” volunteers move on to make the next categorization based on shape, size and contour lines. Based on these categorizations, the decision tree provides an approximate location of the optical counterpart. In total, there are eight distinct classifications that can be made using the decision tree for analysis. Kaur and Bari provide an example of all eight types of classifications. This decision tree is a significant contribution to the project because it can be used by other citizen scientists to simplify the classification process. Since classifications made by citizen scientists are also used to train automatic decision tree classifiers, the decision tree may also indirectly improve machine learning. 

There are several limitations to the “Radio Galaxy Zoo: LOFAR” project. Each target must be presented to five people before an appropriate classification can be created, which means that five participants are needed to make one classification. Although nearly one million classifications have been made by citizen scientists to date, the project is currently just halfway to meeting their goal. Citizen scientists can help address this issue by getting involved as a volunteer. With the aid of Kaur and Bari’s classification tree, it is easier to make a supermassive impact.

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