An international effort led by astrophysicists at the Niels Bohr Institute, University of Copenhagen, and the Technical University of Denmark, has identified a distant object with properties that lie in between those of a galaxy and those of a so-called quasar. The object can be seen as the ancestor of a supermassive black hole, and it was born relatively soon after the Big Bang. Simulations had indicated that such objects would exist, but this is the first actual finding. 

“The discovered object connects two rare populations of celestial objects, namely dusty starbursts and luminous quasars, and thereby provides a new avenue toward understanding the rapid growth of supermassive black holes in the early universe,” says Seiji Fujimoto, a postdoctoral fellow based at the Niels Bohr Institute, University of Copenhagen.

The discovery can be attributed to the Hubble Space Telescope operated jointly by ESA and NASA. With its location in space – undisturbed by weather changes, pollution, etc. – the telescope can gaze further into the depths of the universe than would have been the case on the ground. And in astronomy, looking further equals being able to observe phenomena that took place at earlier cosmic periods – since light and other types of radiation will have traveled longer to reach us.

The newly found object – named GNz7q by the team – was born 750 million years after the Big Bang which is generally accepted as the beginning of the universe as we know it. Since the Big Bang occurred about 13.8 billion years ago, GNz7q origins in an epoch known as “Cosmic Dawn”.

The mystery of supermassive black holes

The discovery is linked to a specific type of quasars. Quasars, also known as quasi-stellar objects, are extremely luminous objects. Images from Hubble and other advanced telescopes have revealed that quasars occur in the centers of galaxies. The host galaxy for GNz7q is intensely star-forming, forming stars at a rate 1,600 times faster than our galaxy, the Milky Way. The stars, in turn, create and heat cosmic dust, making it glow in infrared to the extent that GNz7q’s host is more luminous in dust emission than any other known object at this period of the Cosmic Dawn.

In the most recent years, it has transpired, that luminous quasars are powered by supermassive black holes, with masses ranging from millions to tens of billions of solar masses, surrounded by vast amounts of gas. As the gas falls towards the black hole, it will heat up due to friction which provides an enormous luminous effect.

“Understanding how supermassive black holes form and grow in the early universe has become a major mystery. Theorists have predicted that these black holes undergo an early phase of rapid growth: a dust-reddened compact object emerges from a heavily dust-obscured starburst galaxy, then transitions to an unobscured luminous compact object by expelling the surrounding gas and dust,” explains Associate Professor Gabriel Brammer, Niels Bohr Institute, continuing:

“Although luminous quasars had already been found even at the earliest epochs of the universe, the transition phase of the rapid growth of both the black hole and its star-bursting host had not been found at similar epochs. Moreover, the observed properties are in excellent agreement with the theoretical simulations and suggest that GNz7q is the first example of the transitioning, rapid growth phase of black holes at the dusty star core, an ancestor of the later supermassive black hole.”

Both Seiji Fujimoto and Gabriel Brammer are part of the Cosmic Dawn Center (DAWN), a collaboration between Niels Bohr Institute and DTU Space.

Hiding in plain sight

Curiously, GNz7q was found at the center of an intensely studied sky field known as the Hubble GOODS North field.

“This shows how big discoveries can often be hidden right in front of you,” Gabriel Brammer comments.

Finding GNz7q hiding in plain sight was only possible thanks to the uniquely detailed, multi-wavelength datasets available for GOODS North. Without the richness of data, the object would have been easy to overlook, as it lacks the distinguishing features of quasars in the early universe.

“It’s unlikely that discovering GNz7q within the relatively small GOODS-N survey was just “dumb luck”, but rather that the prevalence of such sources may be significantly higher than previously thought,” Brammer adds.

The team now hopes to systematically search for similar objects using dedicated high-resolution surveys and to take advantage of the NASA/ESA/CSA James Webb Space Telescope.

“Fully characterizing these objects and probing their evolution and underlying physics in much greater detail will become possible with the James Webb Telescope. Once in regular operation, Webb will have the power to decisively determine how common these rapidly growing black holes truly are,” Seiji Fujimoto concludes.

First-of-its-kind survival predictor detects patterns in heart MRIs invisible to the naked eye

A new artificial-intelligence-based approach can predict, significantly more accurately than a doctor, if and when a patient could die of cardiac arrest. The technology, built on raw images of patients’ diseased hearts and patient backgrounds, stands to revolutionize clinical decision-making and increase survival from sudden and lethal cardiac arrhythmias, one of medicine’s deadliest and most puzzling conditions. 

“Sudden cardiac death caused by arrhythmia accounts for as many as 20 percent of all deaths worldwide and we know little about why it’s happening or how to tell who’s at risk,” said senior author Natalia Trayanova, the Murray B. Sachs Professor of Biomedical Engineering and Medicine. “There are patients who may be at low risk of sudden cardiac death getting defibrillators that they might not need and then there are high-risk patients that aren’t getting the treatment they need and could die in the prime of their life. What our algorithm can do is determine who is at risk for cardiac death and when it will occur, allowing doctors to decide exactly what needs to be done.”

The team is the first to use neural networks to build a personalized survival assessment for each patient with heart disease. These risk measures provide with high accuracy the chance for a sudden cardiac death over 10 years, and when it’s most likely to happen.

The deep learning technology is called Survival Study of Cardiac Arrhythmia Risk (SSCAR). The name alludes to cardiac scarring caused by heart disease that often results in lethal arrhythmias and is the key to the algorithm’s predictions.

The team used contrast-enhanced cardiac images that visualize scar distribution from hundreds of real patients at Johns Hopkins Hospital with cardiac scarring to train an algorithm to detect patterns and relationships not visible to the naked eye. Current clinical cardiac image analysis extracts only simple scar features like volume and mass, severely underutilizing what’s demonstrated in this work to be critical data.

“The images carry critical information that doctors haven’t been able to access,” said first author Dan Popescu, a former Johns Hopkins doctoral student. “This scarring can be distributed in different ways and it says something about a patient’s chance for survival. There is information hidden in it.”

The team trained a second neural network to learn from 10 years of standard clinical patient data, 22 factors such as patients’ age, weight, race, and prescription drug use.

The algorithms’ predictions were not only significantly more accurate on every measure than doctors, they were validated in tests with an independent patient cohort from 60 health centers across the United States, with different cardiac histories and different imaging data, suggesting the platform could be adopted anywhere.

“This has the potential to significantly shape clinical decision-making regarding arrhythmia risk and represents an essential step towards bringing patient trajectory prognostication into the age of artificial intelligence,” said Trayanova, co-director of the Alliance for Cardiovascular Diagnostic and Treatment Innovation. “It epitomizes the trend of merging artificial intelligence, engineering, and medicine as the future of healthcare.”

The team is now working to build algorithms now to detect other cardiac diseases. According to Trayanova, the deep-learning concept could be developed for other fields of medicine that rely on a visual diagnosis.

The team from Johns Hopkins also included: Bloomberg Distinguished Professor of Data-Intensive Computation Mauro Maggioni; Julie Shade; Changxin Lai; Konstantino Aronis; and Katherine Wu. Other authors include M. Vinayaga Moorthy and Nancy Cook of Brigham and Women’s Hospital; Daniel Lee of Northwestern University; Alan Kadish of Touro College and University System; David Oyyang and Christine Albert of Cedar-Sinai Medical Center.